- Email: [email protected]
Review of Fuzzy and ANN Fault Location Methods for Distribution Power System in Oil and Gas Sectors
Stability 18th Conference on 18th IFAC Conference Sept on Technology, Technology, Culture and and International International Baku,IFAC Azerbaidschan, 13-15, 2018Culture Stability 18th IFAC Conference on Technology,Available Culture and International Stability online at www.sciencedirect.com Baku, Azerbaidschan, 13-15, Stability Baku,IFAC Azerbaidschan, Sept 13-15, 2018 2018Culture and International 18th Conference Sept on Technology, Baku, Azerbaidschan, Sept 13-15, 2018 Stability Baku, Azerbaidschan, Sept 13-15, 2018
ScienceDirect
Review of Fuzzy and ANNIFAC Fault Location Methods for Distribution Power System in PapersOnLine 51-30 (2018) 263–267 and Gas Sectorsfor Distribution Power System in Review of Fuzzy and ANN Fault Oil Location Methods Review of Fuzzy and ANN Fault Oil Location Methods for Distribution Power System in and Gas Sectors Review of Fuzzy and ANN Fault Oil Location Methods Distribution Power System in Muhammad M.A.S. Mahmoud *. Zafar Qurbanov** and Gas Sectorsfor and Gas Sectors Muhammad Oil M.A.S. Mahmoud *. Zafar Zafar Qurbanov** Qurbanov** Muhammad M.A.S. Mahmoud *.
*ProcessMuhammad Automation Engineering Department, Baku Higher Oil School, M.A.S. Mahmoud *. Zafar Qurbanov** (muhammad.salih@ socar.az). Muhammad M.A.S. Mahmoud *. Zafar Qurbanov** *Process Baku *Process Automation Automation Engineering Engineering Department, Department, Baku Higher Higher Oil Oil School, School, ** ProcessAutomation Automation EngineeringDepartment, Department, BakuHigher HigherOil OilSchool, School, *Process Engineering Baku (muhammad.salih@ socar.az). (muhammad.salih@ socar.az). ([email protected]). *Process Engineering Baku (muhammad.salih@ socar.az). ** ProcessAutomation Automation EngineeringDepartment, Department, BakuHigher HigherOil OilSchool, School, ** Process Automation Engineering Department, Baku Higher Oil School, (muhammad.salih@ socar.az). ** Process Automation Engineering Department, Baku Higher Oil School, ([email protected]). ([email protected]). ** Process Automation([email protected]). Engineering Department, Baku Higher Oil School, ([email protected]). Abstract: Fault location and isolation is mandatory for service and restoration. Fault finding in power system distribution networks been is anmandatory active areafor of service researchand forrestoration. decades. PC based techniques for Abstract: Fault location location and has isolation Fault finding in power power Abstract: Fault and isolation is mandatory for service and restoration. Fault finding in fault finding are become very important formandatory the fast results. Artificial Neural Networks (ANN) and Fuzzy Abstract: Fault location and isolation is for service and restoration. Fault finding in power system distribution networks has been an active area of research for decades. PC based techniques for system distribution networks has been an active area of research for decades. PC based techniques for Logic (FL) methods havevery recently gained popularity and proved successful in many practical problems. Abstract: Fault location and isolation is mandatory for service and restoration. Fault finding in power system distribution networks has been an active area of research for decades. PC based techniques for fault finding are become important for the fast results. Artificial Neural Networks (ANN) and Fuzzy fault finding are become very important for the fast results. Artificial Neural Networks (ANN) and Fuzzy This paper focuses on fault location in power network using ANN successful and FL techniques. The paper provides system distribution networks has been an active area of research for decades. PC based techniques for fault finding are become very important for the fast results. Artificial Neural Networks (ANN) and Fuzzy Logic (FL) methods have recently gained popularity and proved in many practical problems. Logic (FL) methods have recently gained popularity and proved successful in many practical problems. afault comprehensive review of theimportant conceptual aspects asresults. well asArtificial algorithmic developments forpaper fault location finding are become very for the fast Neural Networks (ANN) and Fuzzy Logic (FL) methods have recently gained popularity and proved successful in many practical problems. This paper focuses on fault location in power network using ANN and FL techniques. The provides This paper focuses on fault location in power network using ANN and FL techniques. The paper provides in power networks especially that serve oil and gas successful fields. fundamentally different (FL)distribution methods gained popularity and proved in many practical This paper focuses onhave fault location in power network using and FLSeveral techniques. The provides a comprehensive comprehensive review ofrecently the conceptual conceptual aspects as well well asANN algorithmic developments forpaper faultproblems. location aLogic review of the aspects as as algorithmic developments for fault location approaches are discussed in the paper together with the factors affecting the assumptions of the This paperdistribution focusesreview on networks fault in power network using and FLSeveral techniques. The aincomprehensive of location the conceptual aspects as well asANN algorithmic developments forpaper fault provides location power distribution networks especially that serve oil and gas fields. fields. Several fundamentally different in power especially that serve oil and gas fundamentally different underlying concepts and the various criteria used in the different approaches are reviewed. aapproaches comprehensive review of the conceptual aspects as well as algorithmic developments for fault location in power distribution networks especially that servewith oil and fields. Severalthe fundamentally approaches are discussed discussed in the the paper together together with the gas factors affecting the assumptionsdifferent of the the are in paper the factors affecting assumptions of in power distribution networks especially that serve oildifferent and gas fields. Several fundamentally different approaches are discussed in the paper together with the factors affecting the assumptions of the underlying concepts and the various criteria used in the different approaches are reviewed. underlying concepts and the various criteria used in the approaches are reviewed. © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Fault location method, Distribution power system, System restoration, Fuzzy logic approaches are discussed the paper withdifferent the factors affecting the assumptions of the underlying concepts and thein various criteriatogether used in the approaches are reviewed. applications, ANN applications. Keywords: Fault location location method,criteria Distribution power system, Systemarerestoration, restoration, Fuzzy logic logic Keywords: Fault method, Distribution power system, System underlying concepts and the various used in the different approaches reviewed. Fuzzy Keywords: Fault location method, Distribution power system, System restoration, Fuzzy logic applications, ANN applications. applications. applications, ANN Keywords: location method, Distribution power system, restoration, Fuzzy applications,Fault ANN applications. protection relaysSystem followed by continuous testslogic are applied to 1. INTRODUCTION applications, ANN applications. detect faults Fink, D. G, et al..(1993). This will a long protection relays relays followed followed by by continuous continuous tests tests are aretake applied to protection applied to 1. time for fault detection and will not be practical for 1. INTRODUCTION INTRODUCTION The problem of locating faults is a fundamental issue for protection relays continuous tests aretake applied to detect faults faults Fink,followed D. G, G, et etbyal..(1993). al..(1993). This will take a large long detect Fink, D. This will a long 1. INTRODUCTION networks. The application ofwill ANN and FL found continuous power delivery. In the past, many efforts have protection relays followed byal..(1993). continuous tests arewere applied to detect faults Fink, D. G, et This will take a long time for fault detection and not be practical for large time for fault detection and will not be practical for large The problem of locating faults is a fundamental issue for The problem of locating faults is a fundamental issue for suitable techniques, fast method and less cost to solve such 1. INTRODUCTION been made to solve fault location problems using intelligent detect faults Fink, D. G, et al..(1993). This will take a long time for fault detection and will not be practical for large networks. The application of ANN and FL were found The problem of locating is apast, fundamental issuehave for networks. The application of ANN and FL were found continuous power delivery. In many continuous power delivery.faults In the the past, many efforts efforts have nonlinear problem. Specially, when the problem becomes programming techniques. electrical power time for techniques, fault and ofwill notless be for large The detection application ANN andpractical FL to found suitable fast and cost solve such The of locating faults isproblems apast, fundamental issue for networks. suitable techniques, fast method method and less cost towere solve such continuous power delivery. InGenerally, the many efforts have been problem made to to solve fault location location problems using intelligent been made solve fault using intelligent more complicated for a multi-ring distribution network and distribution systems include all parts of electrical utility networks. The application of ANN and FL were found suitable techniques, fast method and less cost to solve such nonlinear problem. Specially, when the problem becomes continuous power delivery. InGenerally, theproblems past, many efforts have nonlinear problem. Specially, when the problem becomes been made to solve fault location using intelligent programming techniques. Generally, electrical power programming techniques. electrical power unbalanced faults, while all actual fault information are read systems between bulk power sources the consumers’ techniques, fast methodwhen anddistribution less tonetwork solve such nonlinear problem.for Specially, the cost problem becomes more aa multi-ring and been made tosystems solve fault location problems using intelligent more complicated complicated for multi-ring distribution network and programming techniques. Generally, power distribution systems include all parts and ofelectrical electrical utility suitable distribution include all parts of electrical utility only atcomplicated the problem. major substation. service-entrance equipment. Supplying electrical power nonlinear Specially, when the problem becomes more for a multi-ring distribution network and unbalanced faults, while all actual fault information are read programming techniques. Generally, electrical power unbalanced faults, while all actual fault information are read distribution systems parts and of electrical utility systems between between bulkinclude power all sources and the consumers’ consumers’ systems bulk power sources the generated from large sources to parts consumers atconsumers’ a desired moreat complicated for a multi-ring distribution network and unbalanced faults, while all actual fault information are read only at the major substation. distribution systems include all of electrical utility only the major substation. systems between bulk power sources and the service-entrance equipment. Supplying electrical power service-entrance equipment. Supplying electrical power This paper reviews selected fault location techniques voltage level is anlarge important factor inconsumers reliability, Patton, R, et unbalanced faults, while all actual fault information are read only at the major substation. systems between bulk power sources and the consumers’ service-entrance equipment. Supplying electrical power generated from sources to at a desired for reviews distribution systems FL and ANN generated from large sources to consumers at a desired proposed This atpaper paper selected fault using location techniques al..(1985). Reliability, along with maintenance, are only This reviews selected fault location techniques the major substation. service-entrance equipment. Supplying electrical power generated from sources to efficient atPatton, a desired voltage is important factor in reliability, R, In general, these methods can be classified in voltage level level is an anlarge important factor inconsumers reliability, Patton, R, et et techniques. This paperfor selected fault using location techniques proposed for reviews distribution systems using FL and and ANN crucial for the of electrical energy. It at is customary distribution systems FL ANN generated from sources to efficient a desired voltage level iscontinuity anlarge important factor inconsumers reliability, Patton, R,are et proposed al..(1985). Reliability, along with maintenance, three broad categories, which are using Artificial Intelligent al..(1985). Reliability, along with efficient maintenance, are This paper reviews selected fault location techniques proposed for distribution systems FL and ANN techniques. In general, these methods can be classified for moderate distribution networks to have amaintenance, large number of In general, these methods can be classified in in voltage level is an important factor in energy. reliability, Patton, R,are et techniques. al..(1985). Reliability, along with efficient crucial for the continuity of electrical energy. It is is customary Trending, Fuzzy Clustering Method and Neuro-Fuzzy Hybrid crucial for the continuity of electrical It customary proposed for distribution systems using FL classified and ANN techniques. Incategories, general, these methods can be in three broad which are Artificial Intelligent nodes to serve a vast geographical area and to ensure a safe three broad categories, which are Artificial Intelligent al..(1985). Reliability, along with efficient are crucial for the continuity of electrical It is number customary for moderate moderate distribution networks to energy. have aamaintenance, large number of methods. for distribution networks to have large of techniques. In general, these methods can be classified in three broad categories, which are Artificial Intelligent Trending, Fuzzy Clustering Method and Neuro-Fuzzy Hybrid operation atthesevere conditions, Laughton, M. aA,safe et Trending, Fuzzy Clustering Method and Neuro-Fuzzy Hybrid crucial continuity of electrical energy. Itensure is number customary for moderate distribution networks to have a to large of nodes toforserve serve vastambient geographical area and to ensure nodes to aa vast geographical area and a safe three broad categories, which are Artificial Intelligent Trending, Fuzzy Clustering Method and Neuro-Fuzzy Hybrid methods. al.. (1990). of numerous working elements for moderate distribution networks to have a to large number of nodes to serve a employment vastambient geographical area and ensure aA, operation atThe severe ambient conditions, Laughton, M. A,safe et methods. 2. ARTIFICIAL INTELLIGENT METHODS operation at severe conditions, Laughton, M. et Trending, Fuzzy Clustering MethodTRENDING and Neuro-Fuzzy Hybrid methods. and devices makes the operation susceptible toensure malfunction nodes to serve a vast geographical area and to a safe operation at severe ambient conditions, Laughton, M. A, et methods. al.. (1990). The employment of numerous working elements (AIT) al.. (1990). The employment of numerous working elements 2. ARTIFICIAL INTELLIGENT TRENDING METHODS or failure. Providing on-line operating information is 2. ARTIFICIAL INTELLIGENT TRENDING METHODS operation atThe severe ambient conditions, Laughton, M. A, et al.. employment of numerous working elements and devices makes the operation susceptible to and (1990). devices makes the operation susceptible to malfunction malfunction 2. ARTIFICIAL INTELLIGENT TRENDING METHODS (AIT) important for reliability and of safety requirements toelements ensure (AIT) are several Artificial intelligent trending methods such al.. (1990). The employment numerous working and devices makes the operation susceptible to malfunction or failure. Providing on-line operating information or failure. Providing on-line operating information is is There 2. ARTIFICIAL INTELLIGENT TRENDING METHODS (AIT) continuous and satisfactory operations of power systems. as Artificial Neural network (ANN), Fuzzy Logic (FL), and devices makes the operation susceptible to malfunction or failure. Providing on-line operating information is important for reliability and safety requirements to ensure important for reliability and safety requirements to ensure There There are are several several Artificial Artificial (AIT) intelligent trending trending methods methods such such intelligent This can befor done by a scheme of observation and monitoring System (ES) and Genetic Algorithm (GA), etc., (FL), with or failure. Providing on-line operating information is Expert important reliability and operations safety requirements tosystems. ensure continuous and satisfactory operations of power power systems. continuous and satisfactory of There are several Artificial intelligent trending methods as Artificial Neural network (ANN), Fuzzy Logic as Artificial Neural network (ANN), Fuzzy Logic (FL), to detect faults as occur, malfunction type of the development of computers emerged. These methodssuch can important for reliability and operations safety requirements tosystems. ensure continuous and satisfactory of power This can be be done bythey scheme ofidentify observation and monitoring monitoring There are several Artificial intelligent trending methods such This can done by aa scheme of observation and as Artificial Neural network (ANN), Fuzzy Logic (FL), Expert System (ES) and Genetic Algorithm (GA), etc., Expert System (ES) and Genetic Algorithm (GA), etc., with with faulty components, and compensate for the faults by help operators or engineers to do much laborious work. By continuous and satisfactory operations of power systems. This can be doneas a scheme observation and monitoring to detect detect faults asbythey they occur,ofidentify identify malfunction type of of the as network (ANN), Fuzzy Logic to faults occur, malfunction type Expert SystemNeural (ES) and Genetic Algorithm (GA), etc., (FL), with development of emerged. These methods can the Artificial development of computers computers emerged. These methods can appropriate action and management. For overhead lines, using these methods, the time factor is substantially reduced This can be done by a scheme of observation and monitoring to detectcomponents, faults as they identify malfunction type by of Expert faulty components, andoccur, compensate for the the faults faults by System or (ES) and Genetic Algorithm (GA), etc., with faulty and compensate for the of computers emerged. These methods can help operators engineers to much work. By helpdevelopment operators or engineers to do do much laborious laborious work. By visual inspection—either from the groundFor or aoverhead helicopter may human mistakes areto factor avoided. Therefore, many to detect faults as they identify malfunction type of and faulty components, andoccur, compensate for the faults by appropriate action and management. lines, the development of computers emerged. These methods can appropriate action and management. For overhead lines, help operators or engineers do much laborious work. By using these methods, the time is substantially reduced using these methods, the time factor is substantially reduced be possible, but this is not practicable and is usually used AIT based methods insubstantially distribution system faulty components, andfrom compensate for the faultslines, by researchers appropriate action and management. visual the or helicopter may help operators or engineers to do much laborious work. By visual inspection—either inspection—either from the ground groundFor or aaoverhead helicopter may using these methods, the time factor is reduced and human mistakes are avoided. Therefore, many and human mistakes are avoided. Therefore, many impossible inaction the case ofisunderground cables. Distance relays appropriate and management. For lines, visual inspection—either ground or aoverhead helicopter may fault be but not and is usinglocations. these methods, the time factor isin substantially reduced be possible, possible, but this this isfrom notthepracticable practicable and is usually usually and human mistakes are avoided. Therefore, many researchers used AIT based methods in distribution system researchers used AIT based methods distribution system or sophisticated fault detector are used along the visual inspection—either from thepracticable ground or aand helicopter may be possible, but this notdevices is with usually impossible in case of cables. Distance relays and humanused mistakes are methods avoided. Therefore,by many impossible in the the case ofisunderground underground cables. Distance relays researchers AIT based system fault locations. fault locations. multi-way graph partitioning methodinisdistribution employed Bi et traditional methods to locate faults in large distribution be sophisticated possible, but this notdevices practicable is with usually impossible in the case ofisunderground cables. Distance relays or sophisticated fault detector devices are usedand along with the A researchers used AIT based methods in distribution system or fault detector are used along the fault locations. al.,(2002a;2002b) based on weighted minimum degree networks Patton, R, et al..(1990). However, their design is impossible in thefault casedetector underground cables. Distance relays A multi-way multi-way graph partitioning partitioning method method is is employed employed by by Bi Bi et et or sophisticated devices along with the fault traditional methods toof locate locate faultsare in used large distribution A graph locations. traditional methods to faults in large distribution reordering to graph partition a large scale power network into some complicated andfault costly. inspection flagtheir indicators on or sophisticated are used along with the multi-way partitioning method is employed Bi et al.,(2002a;2002b) based on minimum traditional methods to Also, locatedevices faults inof large distribution networks Patton, R, detector et al..(1990). However, design is A al.,(2002a;2002b) based on weighted weighted minimum bydegree degree networks Patton, R, et al..(1990). However, their design is multi-way partitioning method is employed bydegree Bi et traditional methods to Also, locateinspection faults inof distribution al.,(2002a;2002b) based on scale weighted minimuminto reordering to partition aa large power network some networks Patton, R, et al..(1990). However, design on is A complicated and costly. costly. Also, inspection of large flagtheir indicators on reordering to graph partition large scale power network into some complicated and flag indicators Copyright ©Patton, 2018 based on scale weighted minimumintodegree reordering to partition a large power network some networks R, et Also, al..(1990). However, design on is 263 al.,(2002a;2002b) complicated andIFAC costly. inspection of flagtheir indicators reordering to partition a large power network into some 2405-8963 © IFAC (International Federation of Automatic Control) by Elsevier Ltd. All rights scale reserved. complicated andIFAC costly. Also, inspection of flag indicators on 263Hosting Copyright © 2018, 2018 Copyright 2018 responsibility IFAC 263Control. Peer review©under of International Federation of Automatic Copyright © 2018 IFAC 263 10.1016/j.ifacol.2018.11.298 Copyright © 2018 IFAC 263
IFAC TECIS 2018 264 Muhammad M.A.S. Mahmoud et al. / IFAC PapersOnLine 51-30 (2018) 263–267 Baku, Azerbaidschan, Sept 13-15, 2018
sub-networks. The speed of the distributed fault section estimation system made it possible to use it as an on-line system.
action. He applied the parsimonious set covering theory to make the faulted section estimation as an integerprogramming problem.
Al-Shahere. Manar M. Sabry et al., (2003) developed a fault location method for multi-ring distribution network serving oil field using neural network. He used the feeder fault voltage, circuit breaker status, real power of feeders during the normal condition, and real power of feeders during short circuit, operator experience, etc, to train the neural network. In this method, three-phase-to-ground fault were considered with the assumption that the network is continuously fully loaded and its transmission lines are short, then the effect of ambient temperature variation is negligible and the conductor temperature is considered constant. This assumption may be practical only heavily loaded networks for certain applications such as oil and gas electrical distribution network. However, this methods may be not accurate in domestic distribution as the load varies drastically between day and night and the fault can be occur at any time. The paper also considered only solidly grounded three-phase short-circuit faults. However, the results, which showed an average saving effort of 96.1%, are very encouraging to move one step forward to introduce an unsymmetrical fault algorithm using same procedure with proper adaptation for the new case.
Martins et al., (2003) proposed an approach used the Eigenvalue and an artificial neural network based learning algorithm. The neural network was trained to map the nonlinear relationship existing between fault location and characteristic Eigenvalue. This approach used the eigenvalue/eigenvector and an artificial neural based learning algorithm. The main characteristics and particularities of the proposed method were: Reduced number of input signals (this was an importance aspect due to the non-use of voltage detectors); Recognition of the faults type and identification of faulty line or lines; Location of the fault, independent of his presence at the moment of the analysis; almost independent on harmonics influence. Simulation results presented show that the proposed algorithm was a promising technique for fault location on distribution power systems. In Azriyenni, M.W. Mustafa (2013) the article has introduced the NF method for the detection line fault in electrical power system. Fuzzy represents the relationship between the pattern of alarms and system components were fault. Alarm signal that there is fault from input to NN structure. NN is used as a method of exercise in classes neurons for interference system components Fuzzy used to calculate the value of membership in each system component interference. The test results of test each every system components in the form are highest membership value. The value of Membership displayed consist of the highest grade of membership, there is value membership zero and the value highest membership and zero. The Highest value membership indicates there is an interruption of information systems components and value membership zero indicates no interference, then the value of membership between the highest and zero indicates the possibility of interface.
Muhammad M.A.S. Mahmoud (2015) examines the problem of locating single-line-to-ground faults through resistance that may occur anywhere in existing multi-ring electrical distribution network belongs to one of the Kuwaiti oil fields, in order to improve reliability and to reduce the shutdown time of this network. The developed method is based also on using Artificial Neural Network techniques as trending algorithm to save time in fault location, as well as estimating the value of the fault resistance. In the training process, additional data were used during and after the fault, such as voltage angle, X/R ratio during fault, short circuit current angle, etc… The technique was applied to an existing 13.8kilovolt-distribution network, which caters to an oil production field spread over an area of approximately sixty km2. The nature of this distribution system, which consists of multi-ring configuration, illustrates the complexity of the network feasibility and the effectiveness of the suggested fault location method. The challenging aspect in the developed approach was identifying the minimum necessary inputs and outputs that can facilitate the application and the training of ANN procedure in this multi-ring distribution network. This identification may be simple for the case of radial distribution network. The results for several cases study were carried out. In order to improve the validity of the suggested technique, one case where excluded. However, in general, the results showed the feasibility and the effectiveness of the suggested fault location method. In addition to that, determining the value of fault resistance Rf may give additional information for the design engineers when designing and setting the protection devices.
3. FUZZY CLUSTERING METHODS Power transformer is one of the pivot equipment of power system in oil and gas distribution network as well as utility. Its normal operation is the important guarantee for the safe operation of power system. Research on the fault diagnosis of power transformer is of important theoretical value and realistic significance. In Zhao Yong Lei (2012), the relationship between the fault cause and fault symptom is described in fuzzy terms. Fuzzy c-means clustering algorithm is used to classify fault samples in a fuzzy way, on that basis, this paper proposed new algorithms to improve the accuracy of fault diagnosis of transformer. In the insulation fault of transformer, different types of fault produce different main characteristic gasses and the components. The samples which are composed of the components of dissolved gas-inoil are present in variable degrees, which should be treated differently. The paper presented a weighted fuzzy c-means clustering algorithm which utilizeed the weights to express the relative degree of the importance of various samples in fault classification. Compared the diagnostic findings with Fuzzy c-means clustering algorithm, the proposed algorithm improved the accuracy and robustness of fault classification.
Wen (1997) proposed a method that constructed a probabilistic causality matrix to represent the probabilistic relationship between faulted sections and protective device 264
IFAC TECIS 2018 Baku, Azerbaidschan, Sept 13-15, 2018 Muhammad M.A.S. Mahmoud et al. / IFAC PapersOnLine 51-30 (2018) 263–267
On the basis of the above, this paper investigates a dynamic weighted fuzzy c-means clustering algorithm based on genetic algorithm. The algorithm adopts a kind of clustercenter-based floating point encoding mode. The experimental results show that the algorithm not only overcomes the shortcoming which fuzzy c-means clustering algorithm is the sensibility to initial value, but also can scientifically reflect the real structure of fault sample data.
265
and interrupted load analysis, the candidate pool was reduced. Khosravi (2007) developed a framework for fault detection and modelling when uncertainty in the plant was present. In the proposed method, a fault alarm was fired when an inconsistency between the behaviors of the system and the model emerges. Afterwards, the behavior of the faulty system is modelled using an Adaptive Neuro Fuzzy Inference System (ANFIS). The identified model can be used for the fault accommodation task.
An irregular activity on electric power distribution feeder, which does not draw adequate fault current to be detected by general protective devices, is called as High impedance fault (HIF). In Marizan Sulaiman al. (2013) the work presented the algorithm for HIF detection based on the amplitude of third and fifth harmonics of current, voltage and power. This paper proposed an intelligent algorithm using the Takagi SugenoKang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance fault. The Fast Fourier Transformation (FFT) was used to extract the feature of the faulted signals and other power system events. The effect of capacitor bank switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics was discussed. The HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. A qualitative comparison was made among three types of features for HIF detection in power distribution feeder, in the proposed algorithm. Based on the outcomes, it was found that the feature of type1 provides better results compared with other features. The classification rate for radial distribution network is 98.81% for feature of type1compared with 91.4% and 85.41% for both features of type3 and type 2, respectively. Also with using 90% training and 10% testing data sets to train and test the fuzzy subtractive has given a good classification rate result.
Mora et al.,(2006) proposed a fault location approach based on the current waveforms measured at the power substation and the knowledge of protective device setting and ANFIS nets. The ANFIS nets proved the capability to locate the fault in a specific zone of power distribution system and showed validation errors lower than 1% to locate faulted zones. This approach did not use the electrical model of the network and the signal treatment to obtain descriptors is simple. The fact of not using the electric al network model was an additional advantage because it was not always easy to have the electrical power system parameter values. Fuzzy inference is used in both method s (Khosravi, 2007; Mora et al., 2006) to deal with uncertainty inherent in these methods. Fan (Chunju et al., 2007) proposed a fault location method employing wavelet fuzzy neural network to use post-fault transient and steady-state measurements. However, this method was not influenced by the fault resistance and load current but he assumed the system was balanced and it tacked a lot of time in off line mode the training and calculation for the fault location in industrial distribution lines. In S.R. Samantaray (2008), intelligent techniques for HIF detection and classification are presented in the proposed study. An attempt was made to classify the HIF from NF under nonlinear loading. In this study, the time – frequency and time – time distributions of the HIF and NF current signals were extracted using S-transforms and TTtransforms, respectively, and different features like energy, standard deviation were computed and used to train and test the PNN for HIF classification. As futures are extracted for half cycle post-fault HIF signal and PNN testing takes half-cycle time (0.01 s), thus the combined approach takes one cycle for HIF classification from the fault inception. Also HIF classification rate is more than 98%, obtained from PNN. Thus, the proposed approach is fast and accurate for HIF identification and can be extended for protection of large power distribution network.
In Muhammad M.AS. Mahmoud (2013), the author provided a fast and relatively low cost method to locate network faults by using fuzzy c-mean clustering technique. The data was collected from comprehensive Load Flow and short Circuit stud to estimate the feature matrix of faults at different locations (176 nodes). Load flow study was implemented to determine the respective power loss for each short circuit case and also short circuit study was carried out to determine the feature vector for each short circuit case. The results, obtained from the load flow study and the short circuit study, have been used to form the network feature matrix, which was clustered and analyzed to find the faults. . Thirteen cases are used as test cases. Euclidean distance technique is implemented to find out the group of nodes, which the fault may be found near to, for each test case. The results have shown the feasibility and the effectiveness of the suggested fault location method.
Ramadoni Syahputra (2013) has proposed a neuro-fuzzy approach that estimates the distance of a transmission line short circuit fault from relay locations using unsynchronized fundamental frequency voltages and currents measured at the two ends of transmission line. In this paper, a neuro-fuzzy approach for short circuit fault location estimation which uses data from both ends of overhead transmission line is described. The approach utilizes the advantages of digital relaying which are available today. The accurate fault location estimation algorithm has irrespective of source impedances, fault resistances, fault types, and load currents. Simulation of short circuit fault of transmission line has done by using EDSA software. Short circuit currents and voltages
4. NEURO-FUZZY HYBRID METHODS Zhong et al.,(1996) presented a method to locate faults based on fault current measurements, fault currents calculated from short circuit analysis, and system operators’ experience. Lee et al.,(2004) calculated the fault distance first to provide some fault location candidates. Then, by current pattern matching 265
IFAC TECIS 2018 266 Muhammad M.A.S. Mahmoud et al. / IFAC PapersOnLine 51-30 (2018) 263–267 Baku, Azerbaidschan, Sept 13-15, 2018
from both ends of overhead transmission line have used to input data of neuro-fuzzy method in Matlab program. Simulation results demonstrate the accuracy of the method. The results shows that the lowest estimation error for single phase to ground fault with the variation of fault resistances of 0 ohms, 10 ohms, 50 ohms, and 100 ohms, respectively, is 0.0027%, while the highest estimation error is 0.2962%.
REFERENCES Adeyemi Charles Adewole, Raynitchka Tzoneva, Shaheen Behardien (2016). Distribution network fault section identification and fault location using wavelet entropy and neural networks, “Applied Soft Computing” Elsevier B.V. Al-Shaher, M., M.M. Sabra and A.S. Saleh, (2003). Fault Location In Multi-Ring Distribution Network Using Artificial Neural Network,” Electric Power Systems Research, 64(2): 87-92 Azriyenni, M.W. Mustafa (2013), Performance Neuro-Fuzzy for Power System Fault Location, “International Journal of Engineering and Technology “ Volume 3 No. 4, 497501, ISSN: 2049-3444 © 2013 – IJET Publications UK. Bi, T., Y. Ni, C.M. Shen and F.F. Wu. (2002). An On-Line Distributed Intelligent Fault Section Estimation System For Large-Scale Power Networks,” Electric Power Systems Research, 62(3): 173-182. Brown, F., Harris, M.G., and Other, A.N. (1998). Name of paper. In Name(s) of editor(s) (ed.), Name of book in italics, page numbers. Publisher, Place of publication. Chunju, F., K.K. Li, W.L. Chan, Y. Weiyong and Z. Zhaoning (2007). Application Of Wavelet Fuzzy Neural Network In Locating Single Line To Ground Fault (SLG) In Distribution Lines. Electric Power and Energy System, 29: 497-503 Fink, D. G., and Beaty, H. W. (1993) Standard Handbook of Electrical Engineers, 13th ed., New York: McGraw-Hill. Khosravi, A. and J.A. Llobet (2007). A Hybrid Method for Fault Detection and Modelling using Modal Intervals and ANFIS. In Proceeding of American Control Conference, New York, USA. Laughton, M. A., and Say, M. G. (1990), Electrical Engineer’s Reference Book, 14th ed., London: Butterworth-Heinemann Lee, S.J., M.S. Choi, S.H. Kang, B.G. Jin, D.S. Lee, et al. (2004). An Intelligent And Efficient Fault Location And Diagnosis Scheme For Radial Distribution Systems. IEEE Trans on Power Delivery, 19(2): 524532. Muhammad MAS Mahmoud (2015), Detection of high impedance faults in M.V. mesh distribution network, Modern Electric Power Systems (MEPS) IEEE, ISBN: 978-1-5090-3101-6 Muhammad M.A.S. Mahmoud (2013), 3-Phase Fault Finding in Oil Field MV Distribution Network Using Fuzzy Clustering Techniques, Journal of Energy and Power Engineering 7 155-161. Marizan Sulaiman, Adnan Hasan Tawafan and Zulkifilie Ibrahim (2013), Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model, Australian Journal of Basic and Applied Sciences, 7(8): 81-91, 2013 ISSN 1991-8178 Martins, L.S., J.F. Martins, C.M. Alegria and V.F. Pires, (2003). A Network Distribution Power System Fault Location Based on Neural Eigenvalue Algorithm. In Proceeding of IEEE Bologna PowerTech Conference, Bologna, Italy Patton, R., Frank, P., and Clark, R (1985), Fault Diagnosis in Dynamic Systems: Theory and Application, Englewood Cliffs, NJ: Prentice-Hall.
Another Hybrid method is introduced in Adeyemi Charles Adewole (2016) , the paper has developed a hybrid 2-stage method for distribution network fault section identification (FSI) and fault location (FL)based on the coefficients from level-5 detail coefficients obtained from DWT decomposition using db4 mother wavelet. Wavelet Energy Spectrum Entropy (WEE) and Entropy Per- Unit (EPU) indices are computed from the DWT detail coefficients. These indices are used in training artificial neural network models for the FSI and FL tasks respectively. Comparison of ANN models trained using the EPU and WEE indices is carried out in terms of the prediction accuracy, computation time, processor usage, and memory usage. In order to validate the proposed hybrid method, it is applied to the IEEE 34-node benchmark test feeder. The proposed method can easily be implemented practically using actual data obtained from Digital Fault Recorders (DFR) or Intelligent Electronic Devices (IEDs). Although, data obtained from DFRs/IEDs are usually noisy, the noise would be filtered out as a result of the DWT decomposition. The ANN models trained using EPU indices were shown to require less computer memory, less processor usage, and gave faster computation speed. Possible future extension of the proposed hybrid method could be in the use of synchrophasor measurements from Phasor Measurement Units (PMUs). Also, machine learning classifiers and predictors based on decision trees/ensembles of decision trees can be applied to identify the faulted section and the location of the fault in the power system distribution network.
5. CONCLUSIONS This paper has presented an overview of fault location in power network, mainly serve oil and gas field, using Fuzzy logic and ANN. The paper summarized various locating fault methods. Different Fault locating approaches using Trending methods, Clustering Methods and Neuro-Fuzzy Hybrid methods have been reviewed. Most of the fault location techniques discussed have some limitations. Some of them are as follows: According to the advantages and disadvantages described for each method we can conclude that in comparison between all methods, Artificial Intelligent Trending Methods seemed to have more accuracy and speed and less cost. Among the (AIT), ANN algorithm are more used with accordance to the success progress in recent years. Although Fuzzy clustering technique has been used very successfully to locate different types of faults in electrical power system, but may be due to the difficulties in the data correlation, this approach has not found to be used widely. The paper has discussed some areas of possible future extension of the proposed fault detection methods.
266
IFAC TECIS 2018 Baku, Azerbaidschan, Sept 13-15, 2018 Muhammad M.A.S. Mahmoud et al. / IFAC PapersOnLine 51-30 (2018) 263–267
Ramadoni Syahputra (2013).A Neuro-Fuzzy Approach for the Fault Location Estimation of Unsynchronized TwoTerminal Transmission Lines. “International Journal of Computer Science & Information Technology (IJCSIT)” Vol 5, No 1. 23-27, DOI : 10.5121/ijcsit.2013.5102. S.R. Samantaray, B.K. Panigrahi and P.K. Dash (2008). High impedance fault detection in power distribution networks using time – frequency transform and probabilistic neural network, IET Gener. Transm. Distrib. (2), pp. 261 – 270. Wen, F.S. and C.S. Chang. (1997). Probabilistic Approach for Fault-Section Estimation in Power Systems Based On A Refined Genetic Algorithm. IEE Proc. Gen. Trans. and Dist., 144(2): 160-168. .Zhao Yong Lei Huang, Jia Dong. (2012). Research on Transformer Fault Diagnosis Based on New Fuzzy Clustering, Master Thesis, North China Electric Power University. Zhong, W. and W.H. Liu (1996). Application of A Fuzzy Set Method In Distribution System Fault Location. In Proceedings of IEEE International Symposium on Circuits and Systems, 1: 617-620.
267
267
ScienceDirect
Review of Fuzzy and ANNIFAC Fault Location Methods for Distribution Power System in PapersOnLine 51-30 (2018) 263–267 and Gas Sectorsfor Distribution Power System in Review of Fuzzy and ANN Fault Oil Location Methods Review of Fuzzy and ANN Fault Oil Location Methods for Distribution Power System in and Gas Sectors Review of Fuzzy and ANN Fault Oil Location Methods Distribution Power System in Muhammad M.A.S. Mahmoud *. Zafar Qurbanov** and Gas Sectorsfor and Gas Sectors Muhammad Oil M.A.S. Mahmoud *. Zafar Zafar Qurbanov** Qurbanov** Muhammad M.A.S. Mahmoud *.
*ProcessMuhammad Automation Engineering Department, Baku Higher Oil School, M.A.S. Mahmoud *. Zafar Qurbanov** (muhammad.salih@ socar.az). Muhammad M.A.S. Mahmoud *. Zafar Qurbanov** *Process Baku *Process Automation Automation Engineering Engineering Department, Department, Baku Higher Higher Oil Oil School, School, ** ProcessAutomation Automation EngineeringDepartment, Department, BakuHigher HigherOil OilSchool, School, *Process Engineering Baku (muhammad.salih@ socar.az). (muhammad.salih@ socar.az). ([email protected]). *Process Engineering Baku (muhammad.salih@ socar.az). ** ProcessAutomation Automation EngineeringDepartment, Department, BakuHigher HigherOil OilSchool, School, ** Process Automation Engineering Department, Baku Higher Oil School, (muhammad.salih@ socar.az). ** Process Automation Engineering Department, Baku Higher Oil School, ([email protected]). ([email protected]). ** Process Automation([email protected]). Engineering Department, Baku Higher Oil School, ([email protected]). Abstract: Fault location and isolation is mandatory for service and restoration. Fault finding in power system distribution networks been is anmandatory active areafor of service researchand forrestoration. decades. PC based techniques for Abstract: Fault location location and has isolation Fault finding in power power Abstract: Fault and isolation is mandatory for service and restoration. Fault finding in fault finding are become very important formandatory the fast results. Artificial Neural Networks (ANN) and Fuzzy Abstract: Fault location and isolation is for service and restoration. Fault finding in power system distribution networks has been an active area of research for decades. PC based techniques for system distribution networks has been an active area of research for decades. PC based techniques for Logic (FL) methods havevery recently gained popularity and proved successful in many practical problems. Abstract: Fault location and isolation is mandatory for service and restoration. Fault finding in power system distribution networks has been an active area of research for decades. PC based techniques for fault finding are become important for the fast results. Artificial Neural Networks (ANN) and Fuzzy fault finding are become very important for the fast results. Artificial Neural Networks (ANN) and Fuzzy This paper focuses on fault location in power network using ANN successful and FL techniques. The paper provides system distribution networks has been an active area of research for decades. PC based techniques for fault finding are become very important for the fast results. Artificial Neural Networks (ANN) and Fuzzy Logic (FL) methods have recently gained popularity and proved in many practical problems. Logic (FL) methods have recently gained popularity and proved successful in many practical problems. afault comprehensive review of theimportant conceptual aspects asresults. well asArtificial algorithmic developments forpaper fault location finding are become very for the fast Neural Networks (ANN) and Fuzzy Logic (FL) methods have recently gained popularity and proved successful in many practical problems. This paper focuses on fault location in power network using ANN and FL techniques. The provides This paper focuses on fault location in power network using ANN and FL techniques. The paper provides in power networks especially that serve oil and gas successful fields. fundamentally different (FL)distribution methods gained popularity and proved in many practical This paper focuses onhave fault location in power network using and FLSeveral techniques. The provides a comprehensive comprehensive review ofrecently the conceptual conceptual aspects as well well asANN algorithmic developments forpaper faultproblems. location aLogic review of the aspects as as algorithmic developments for fault location approaches are discussed in the paper together with the factors affecting the assumptions of the This paperdistribution focusesreview on networks fault in power network using and FLSeveral techniques. The aincomprehensive of location the conceptual aspects as well asANN algorithmic developments forpaper fault provides location power distribution networks especially that serve oil and gas fields. fields. Several fundamentally different in power especially that serve oil and gas fundamentally different underlying concepts and the various criteria used in the different approaches are reviewed. aapproaches comprehensive review of the conceptual aspects as well as algorithmic developments for fault location in power distribution networks especially that servewith oil and fields. Severalthe fundamentally approaches are discussed discussed in the the paper together together with the gas factors affecting the assumptionsdifferent of the the are in paper the factors affecting assumptions of in power distribution networks especially that serve oildifferent and gas fields. Several fundamentally different approaches are discussed in the paper together with the factors affecting the assumptions of the underlying concepts and the various criteria used in the different approaches are reviewed. underlying concepts and the various criteria used in the approaches are reviewed. © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Fault location method, Distribution power system, System restoration, Fuzzy logic approaches are discussed the paper withdifferent the factors affecting the assumptions of the underlying concepts and thein various criteriatogether used in the approaches are reviewed. applications, ANN applications. Keywords: Fault location location method,criteria Distribution power system, Systemarerestoration, restoration, Fuzzy logic logic Keywords: Fault method, Distribution power system, System underlying concepts and the various used in the different approaches reviewed. Fuzzy Keywords: Fault location method, Distribution power system, System restoration, Fuzzy logic applications, ANN applications. applications. applications, ANN Keywords: location method, Distribution power system, restoration, Fuzzy applications,Fault ANN applications. protection relaysSystem followed by continuous testslogic are applied to 1. INTRODUCTION applications, ANN applications. detect faults Fink, D. G, et al..(1993). This will a long protection relays relays followed followed by by continuous continuous tests tests are aretake applied to protection applied to 1. time for fault detection and will not be practical for 1. INTRODUCTION INTRODUCTION The problem of locating faults is a fundamental issue for protection relays continuous tests aretake applied to detect faults faults Fink,followed D. G, G, et etbyal..(1993). al..(1993). This will take a large long detect Fink, D. This will a long 1. INTRODUCTION networks. The application ofwill ANN and FL found continuous power delivery. In the past, many efforts have protection relays followed byal..(1993). continuous tests arewere applied to detect faults Fink, D. G, et This will take a long time for fault detection and not be practical for large time for fault detection and will not be practical for large The problem of locating faults is a fundamental issue for The problem of locating faults is a fundamental issue for suitable techniques, fast method and less cost to solve such 1. INTRODUCTION been made to solve fault location problems using intelligent detect faults Fink, D. G, et al..(1993). This will take a long time for fault detection and will not be practical for large networks. The application of ANN and FL were found The problem of locating is apast, fundamental issuehave for networks. The application of ANN and FL were found continuous power delivery. In many continuous power delivery.faults In the the past, many efforts efforts have nonlinear problem. Specially, when the problem becomes programming techniques. electrical power time for techniques, fault and ofwill notless be for large The detection application ANN andpractical FL to found suitable fast and cost solve such The of locating faults isproblems apast, fundamental issue for networks. suitable techniques, fast method method and less cost towere solve such continuous power delivery. InGenerally, the many efforts have been problem made to to solve fault location location problems using intelligent been made solve fault using intelligent more complicated for a multi-ring distribution network and distribution systems include all parts of electrical utility networks. The application of ANN and FL were found suitable techniques, fast method and less cost to solve such nonlinear problem. Specially, when the problem becomes continuous power delivery. InGenerally, theproblems past, many efforts have nonlinear problem. Specially, when the problem becomes been made to solve fault location using intelligent programming techniques. Generally, electrical power programming techniques. electrical power unbalanced faults, while all actual fault information are read systems between bulk power sources the consumers’ techniques, fast methodwhen anddistribution less tonetwork solve such nonlinear problem.for Specially, the cost problem becomes more aa multi-ring and been made tosystems solve fault location problems using intelligent more complicated complicated for multi-ring distribution network and programming techniques. Generally, power distribution systems include all parts and ofelectrical electrical utility suitable distribution include all parts of electrical utility only atcomplicated the problem. major substation. service-entrance equipment. Supplying electrical power nonlinear Specially, when the problem becomes more for a multi-ring distribution network and unbalanced faults, while all actual fault information are read programming techniques. Generally, electrical power unbalanced faults, while all actual fault information are read distribution systems parts and of electrical utility systems between between bulkinclude power all sources and the consumers’ consumers’ systems bulk power sources the generated from large sources to parts consumers atconsumers’ a desired moreat complicated for a multi-ring distribution network and unbalanced faults, while all actual fault information are read only at the major substation. distribution systems include all of electrical utility only the major substation. systems between bulk power sources and the service-entrance equipment. Supplying electrical power service-entrance equipment. Supplying electrical power This paper reviews selected fault location techniques voltage level is anlarge important factor inconsumers reliability, Patton, R, et unbalanced faults, while all actual fault information are read only at the major substation. systems between bulk power sources and the consumers’ service-entrance equipment. Supplying electrical power generated from sources to at a desired for reviews distribution systems FL and ANN generated from large sources to consumers at a desired proposed This atpaper paper selected fault using location techniques al..(1985). Reliability, along with maintenance, are only This reviews selected fault location techniques the major substation. service-entrance equipment. Supplying electrical power generated from sources to efficient atPatton, a desired voltage is important factor in reliability, R, In general, these methods can be classified in voltage level level is an anlarge important factor inconsumers reliability, Patton, R, et et techniques. This paperfor selected fault using location techniques proposed for reviews distribution systems using FL and and ANN crucial for the of electrical energy. It at is customary distribution systems FL ANN generated from sources to efficient a desired voltage level iscontinuity anlarge important factor inconsumers reliability, Patton, R,are et proposed al..(1985). Reliability, along with maintenance, three broad categories, which are using Artificial Intelligent al..(1985). Reliability, along with efficient maintenance, are This paper reviews selected fault location techniques proposed for distribution systems FL and ANN techniques. In general, these methods can be classified for moderate distribution networks to have amaintenance, large number of In general, these methods can be classified in in voltage level is an important factor in energy. reliability, Patton, R,are et techniques. al..(1985). Reliability, along with efficient crucial for the continuity of electrical energy. It is is customary Trending, Fuzzy Clustering Method and Neuro-Fuzzy Hybrid crucial for the continuity of electrical It customary proposed for distribution systems using FL classified and ANN techniques. Incategories, general, these methods can be in three broad which are Artificial Intelligent nodes to serve a vast geographical area and to ensure a safe three broad categories, which are Artificial Intelligent al..(1985). Reliability, along with efficient are crucial for the continuity of electrical It is number customary for moderate moderate distribution networks to energy. have aamaintenance, large number of methods. for distribution networks to have large of techniques. In general, these methods can be classified in three broad categories, which are Artificial Intelligent Trending, Fuzzy Clustering Method and Neuro-Fuzzy Hybrid operation atthesevere conditions, Laughton, M. aA,safe et Trending, Fuzzy Clustering Method and Neuro-Fuzzy Hybrid crucial continuity of electrical energy. Itensure is number customary for moderate distribution networks to have a to large of nodes toforserve serve vastambient geographical area and to ensure nodes to aa vast geographical area and a safe three broad categories, which are Artificial Intelligent Trending, Fuzzy Clustering Method and Neuro-Fuzzy Hybrid methods. al.. (1990). of numerous working elements for moderate distribution networks to have a to large number of nodes to serve a employment vastambient geographical area and ensure aA, operation atThe severe ambient conditions, Laughton, M. A,safe et methods. 2. ARTIFICIAL INTELLIGENT METHODS operation at severe conditions, Laughton, M. et Trending, Fuzzy Clustering MethodTRENDING and Neuro-Fuzzy Hybrid methods. and devices makes the operation susceptible toensure malfunction nodes to serve a vast geographical area and to a safe operation at severe ambient conditions, Laughton, M. A, et methods. al.. (1990). The employment of numerous working elements (AIT) al.. (1990). The employment of numerous working elements 2. ARTIFICIAL INTELLIGENT TRENDING METHODS or failure. Providing on-line operating information is 2. ARTIFICIAL INTELLIGENT TRENDING METHODS operation atThe severe ambient conditions, Laughton, M. A, et al.. employment of numerous working elements and devices makes the operation susceptible to and (1990). devices makes the operation susceptible to malfunction malfunction 2. ARTIFICIAL INTELLIGENT TRENDING METHODS (AIT) important for reliability and of safety requirements toelements ensure (AIT) are several Artificial intelligent trending methods such al.. (1990). The employment numerous working and devices makes the operation susceptible to malfunction or failure. Providing on-line operating information or failure. Providing on-line operating information is is There 2. ARTIFICIAL INTELLIGENT TRENDING METHODS (AIT) continuous and satisfactory operations of power systems. as Artificial Neural network (ANN), Fuzzy Logic (FL), and devices makes the operation susceptible to malfunction or failure. Providing on-line operating information is important for reliability and safety requirements to ensure important for reliability and safety requirements to ensure There There are are several several Artificial Artificial (AIT) intelligent trending trending methods methods such such intelligent This can befor done by a scheme of observation and monitoring System (ES) and Genetic Algorithm (GA), etc., (FL), with or failure. Providing on-line operating information is Expert important reliability and operations safety requirements tosystems. ensure continuous and satisfactory operations of power power systems. continuous and satisfactory of There are several Artificial intelligent trending methods as Artificial Neural network (ANN), Fuzzy Logic as Artificial Neural network (ANN), Fuzzy Logic (FL), to detect faults as occur, malfunction type of the development of computers emerged. These methodssuch can important for reliability and operations safety requirements tosystems. ensure continuous and satisfactory of power This can be be done bythey scheme ofidentify observation and monitoring monitoring There are several Artificial intelligent trending methods such This can done by aa scheme of observation and as Artificial Neural network (ANN), Fuzzy Logic (FL), Expert System (ES) and Genetic Algorithm (GA), etc., Expert System (ES) and Genetic Algorithm (GA), etc., with with faulty components, and compensate for the faults by help operators or engineers to do much laborious work. By continuous and satisfactory operations of power systems. This can be doneas a scheme observation and monitoring to detect detect faults asbythey they occur,ofidentify identify malfunction type of of the as network (ANN), Fuzzy Logic to faults occur, malfunction type Expert SystemNeural (ES) and Genetic Algorithm (GA), etc., (FL), with development of emerged. These methods can the Artificial development of computers computers emerged. These methods can appropriate action and management. For overhead lines, using these methods, the time factor is substantially reduced This can be done by a scheme of observation and monitoring to detectcomponents, faults as they identify malfunction type by of Expert faulty components, andoccur, compensate for the the faults faults by System or (ES) and Genetic Algorithm (GA), etc., with faulty and compensate for the of computers emerged. These methods can help operators engineers to much work. By helpdevelopment operators or engineers to do do much laborious laborious work. By visual inspection—either from the groundFor or aoverhead helicopter may human mistakes areto factor avoided. Therefore, many to detect faults as they identify malfunction type of and faulty components, andoccur, compensate for the faults by appropriate action and management. lines, the development of computers emerged. These methods can appropriate action and management. For overhead lines, help operators or engineers do much laborious work. By using these methods, the time is substantially reduced using these methods, the time factor is substantially reduced be possible, but this is not practicable and is usually used AIT based methods insubstantially distribution system faulty components, andfrom compensate for the faultslines, by researchers appropriate action and management. visual the or helicopter may help operators or engineers to do much laborious work. By visual inspection—either inspection—either from the ground groundFor or aaoverhead helicopter may using these methods, the time factor is reduced and human mistakes are avoided. Therefore, many and human mistakes are avoided. Therefore, many impossible inaction the case ofisunderground cables. Distance relays appropriate and management. For lines, visual inspection—either ground or aoverhead helicopter may fault be but not and is usinglocations. these methods, the time factor isin substantially reduced be possible, possible, but this this isfrom notthepracticable practicable and is usually usually and human mistakes are avoided. Therefore, many researchers used AIT based methods in distribution system researchers used AIT based methods distribution system or sophisticated fault detector are used along the visual inspection—either from thepracticable ground or aand helicopter may be possible, but this notdevices is with usually impossible in case of cables. Distance relays and humanused mistakes are methods avoided. Therefore,by many impossible in the the case ofisunderground underground cables. Distance relays researchers AIT based system fault locations. fault locations. multi-way graph partitioning methodinisdistribution employed Bi et traditional methods to locate faults in large distribution be sophisticated possible, but this notdevices practicable is with usually impossible in the case ofisunderground cables. Distance relays or sophisticated fault detector devices are usedand along with the A researchers used AIT based methods in distribution system or fault detector are used along the fault locations. al.,(2002a;2002b) based on weighted minimum degree networks Patton, R, et al..(1990). However, their design is impossible in thefault casedetector underground cables. Distance relays A multi-way multi-way graph partitioning partitioning method method is is employed employed by by Bi Bi et et or sophisticated devices along with the fault traditional methods toof locate locate faultsare in used large distribution A graph locations. traditional methods to faults in large distribution reordering to graph partition a large scale power network into some complicated andfault costly. inspection flagtheir indicators on or sophisticated are used along with the multi-way partitioning method is employed Bi et al.,(2002a;2002b) based on minimum traditional methods to Also, locatedevices faults inof large distribution networks Patton, R, detector et al..(1990). However, design is A al.,(2002a;2002b) based on weighted weighted minimum bydegree degree networks Patton, R, et al..(1990). However, their design is multi-way partitioning method is employed bydegree Bi et traditional methods to Also, locateinspection faults inof distribution al.,(2002a;2002b) based on scale weighted minimuminto reordering to partition aa large power network some networks Patton, R, et al..(1990). However, design on is A complicated and costly. costly. Also, inspection of large flagtheir indicators on reordering to graph partition large scale power network into some complicated and flag indicators Copyright ©Patton, 2018 based on scale weighted minimumintodegree reordering to partition a large power network some networks R, et Also, al..(1990). However, design on is 263 al.,(2002a;2002b) complicated andIFAC costly. inspection of flagtheir indicators reordering to partition a large power network into some 2405-8963 © IFAC (International Federation of Automatic Control) by Elsevier Ltd. All rights scale reserved. complicated andIFAC costly. Also, inspection of flag indicators on 263Hosting Copyright © 2018, 2018 Copyright 2018 responsibility IFAC 263Control. Peer review©under of International Federation of Automatic Copyright © 2018 IFAC 263 10.1016/j.ifacol.2018.11.298 Copyright © 2018 IFAC 263
IFAC TECIS 2018 264 Muhammad M.A.S. Mahmoud et al. / IFAC PapersOnLine 51-30 (2018) 263–267 Baku, Azerbaidschan, Sept 13-15, 2018
sub-networks. The speed of the distributed fault section estimation system made it possible to use it as an on-line system.
action. He applied the parsimonious set covering theory to make the faulted section estimation as an integerprogramming problem.
Al-Shahere. Manar M. Sabry et al., (2003) developed a fault location method for multi-ring distribution network serving oil field using neural network. He used the feeder fault voltage, circuit breaker status, real power of feeders during the normal condition, and real power of feeders during short circuit, operator experience, etc, to train the neural network. In this method, three-phase-to-ground fault were considered with the assumption that the network is continuously fully loaded and its transmission lines are short, then the effect of ambient temperature variation is negligible and the conductor temperature is considered constant. This assumption may be practical only heavily loaded networks for certain applications such as oil and gas electrical distribution network. However, this methods may be not accurate in domestic distribution as the load varies drastically between day and night and the fault can be occur at any time. The paper also considered only solidly grounded three-phase short-circuit faults. However, the results, which showed an average saving effort of 96.1%, are very encouraging to move one step forward to introduce an unsymmetrical fault algorithm using same procedure with proper adaptation for the new case.
Martins et al., (2003) proposed an approach used the Eigenvalue and an artificial neural network based learning algorithm. The neural network was trained to map the nonlinear relationship existing between fault location and characteristic Eigenvalue. This approach used the eigenvalue/eigenvector and an artificial neural based learning algorithm. The main characteristics and particularities of the proposed method were: Reduced number of input signals (this was an importance aspect due to the non-use of voltage detectors); Recognition of the faults type and identification of faulty line or lines; Location of the fault, independent of his presence at the moment of the analysis; almost independent on harmonics influence. Simulation results presented show that the proposed algorithm was a promising technique for fault location on distribution power systems. In Azriyenni, M.W. Mustafa (2013) the article has introduced the NF method for the detection line fault in electrical power system. Fuzzy represents the relationship between the pattern of alarms and system components were fault. Alarm signal that there is fault from input to NN structure. NN is used as a method of exercise in classes neurons for interference system components Fuzzy used to calculate the value of membership in each system component interference. The test results of test each every system components in the form are highest membership value. The value of Membership displayed consist of the highest grade of membership, there is value membership zero and the value highest membership and zero. The Highest value membership indicates there is an interruption of information systems components and value membership zero indicates no interference, then the value of membership between the highest and zero indicates the possibility of interface.
Muhammad M.A.S. Mahmoud (2015) examines the problem of locating single-line-to-ground faults through resistance that may occur anywhere in existing multi-ring electrical distribution network belongs to one of the Kuwaiti oil fields, in order to improve reliability and to reduce the shutdown time of this network. The developed method is based also on using Artificial Neural Network techniques as trending algorithm to save time in fault location, as well as estimating the value of the fault resistance. In the training process, additional data were used during and after the fault, such as voltage angle, X/R ratio during fault, short circuit current angle, etc… The technique was applied to an existing 13.8kilovolt-distribution network, which caters to an oil production field spread over an area of approximately sixty km2. The nature of this distribution system, which consists of multi-ring configuration, illustrates the complexity of the network feasibility and the effectiveness of the suggested fault location method. The challenging aspect in the developed approach was identifying the minimum necessary inputs and outputs that can facilitate the application and the training of ANN procedure in this multi-ring distribution network. This identification may be simple for the case of radial distribution network. The results for several cases study were carried out. In order to improve the validity of the suggested technique, one case where excluded. However, in general, the results showed the feasibility and the effectiveness of the suggested fault location method. In addition to that, determining the value of fault resistance Rf may give additional information for the design engineers when designing and setting the protection devices.
3. FUZZY CLUSTERING METHODS Power transformer is one of the pivot equipment of power system in oil and gas distribution network as well as utility. Its normal operation is the important guarantee for the safe operation of power system. Research on the fault diagnosis of power transformer is of important theoretical value and realistic significance. In Zhao Yong Lei (2012), the relationship between the fault cause and fault symptom is described in fuzzy terms. Fuzzy c-means clustering algorithm is used to classify fault samples in a fuzzy way, on that basis, this paper proposed new algorithms to improve the accuracy of fault diagnosis of transformer. In the insulation fault of transformer, different types of fault produce different main characteristic gasses and the components. The samples which are composed of the components of dissolved gas-inoil are present in variable degrees, which should be treated differently. The paper presented a weighted fuzzy c-means clustering algorithm which utilizeed the weights to express the relative degree of the importance of various samples in fault classification. Compared the diagnostic findings with Fuzzy c-means clustering algorithm, the proposed algorithm improved the accuracy and robustness of fault classification.
Wen (1997) proposed a method that constructed a probabilistic causality matrix to represent the probabilistic relationship between faulted sections and protective device 264
IFAC TECIS 2018 Baku, Azerbaidschan, Sept 13-15, 2018 Muhammad M.A.S. Mahmoud et al. / IFAC PapersOnLine 51-30 (2018) 263–267
On the basis of the above, this paper investigates a dynamic weighted fuzzy c-means clustering algorithm based on genetic algorithm. The algorithm adopts a kind of clustercenter-based floating point encoding mode. The experimental results show that the algorithm not only overcomes the shortcoming which fuzzy c-means clustering algorithm is the sensibility to initial value, but also can scientifically reflect the real structure of fault sample data.
265
and interrupted load analysis, the candidate pool was reduced. Khosravi (2007) developed a framework for fault detection and modelling when uncertainty in the plant was present. In the proposed method, a fault alarm was fired when an inconsistency between the behaviors of the system and the model emerges. Afterwards, the behavior of the faulty system is modelled using an Adaptive Neuro Fuzzy Inference System (ANFIS). The identified model can be used for the fault accommodation task.
An irregular activity on electric power distribution feeder, which does not draw adequate fault current to be detected by general protective devices, is called as High impedance fault (HIF). In Marizan Sulaiman al. (2013) the work presented the algorithm for HIF detection based on the amplitude of third and fifth harmonics of current, voltage and power. This paper proposed an intelligent algorithm using the Takagi SugenoKang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance fault. The Fast Fourier Transformation (FFT) was used to extract the feature of the faulted signals and other power system events. The effect of capacitor bank switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics was discussed. The HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. A qualitative comparison was made among three types of features for HIF detection in power distribution feeder, in the proposed algorithm. Based on the outcomes, it was found that the feature of type1 provides better results compared with other features. The classification rate for radial distribution network is 98.81% for feature of type1compared with 91.4% and 85.41% for both features of type3 and type 2, respectively. Also with using 90% training and 10% testing data sets to train and test the fuzzy subtractive has given a good classification rate result.
Mora et al.,(2006) proposed a fault location approach based on the current waveforms measured at the power substation and the knowledge of protective device setting and ANFIS nets. The ANFIS nets proved the capability to locate the fault in a specific zone of power distribution system and showed validation errors lower than 1% to locate faulted zones. This approach did not use the electrical model of the network and the signal treatment to obtain descriptors is simple. The fact of not using the electric al network model was an additional advantage because it was not always easy to have the electrical power system parameter values. Fuzzy inference is used in both method s (Khosravi, 2007; Mora et al., 2006) to deal with uncertainty inherent in these methods. Fan (Chunju et al., 2007) proposed a fault location method employing wavelet fuzzy neural network to use post-fault transient and steady-state measurements. However, this method was not influenced by the fault resistance and load current but he assumed the system was balanced and it tacked a lot of time in off line mode the training and calculation for the fault location in industrial distribution lines. In S.R. Samantaray (2008), intelligent techniques for HIF detection and classification are presented in the proposed study. An attempt was made to classify the HIF from NF under nonlinear loading. In this study, the time – frequency and time – time distributions of the HIF and NF current signals were extracted using S-transforms and TTtransforms, respectively, and different features like energy, standard deviation were computed and used to train and test the PNN for HIF classification. As futures are extracted for half cycle post-fault HIF signal and PNN testing takes half-cycle time (0.01 s), thus the combined approach takes one cycle for HIF classification from the fault inception. Also HIF classification rate is more than 98%, obtained from PNN. Thus, the proposed approach is fast and accurate for HIF identification and can be extended for protection of large power distribution network.
In Muhammad M.AS. Mahmoud (2013), the author provided a fast and relatively low cost method to locate network faults by using fuzzy c-mean clustering technique. The data was collected from comprehensive Load Flow and short Circuit stud to estimate the feature matrix of faults at different locations (176 nodes). Load flow study was implemented to determine the respective power loss for each short circuit case and also short circuit study was carried out to determine the feature vector for each short circuit case. The results, obtained from the load flow study and the short circuit study, have been used to form the network feature matrix, which was clustered and analyzed to find the faults. . Thirteen cases are used as test cases. Euclidean distance technique is implemented to find out the group of nodes, which the fault may be found near to, for each test case. The results have shown the feasibility and the effectiveness of the suggested fault location method.
Ramadoni Syahputra (2013) has proposed a neuro-fuzzy approach that estimates the distance of a transmission line short circuit fault from relay locations using unsynchronized fundamental frequency voltages and currents measured at the two ends of transmission line. In this paper, a neuro-fuzzy approach for short circuit fault location estimation which uses data from both ends of overhead transmission line is described. The approach utilizes the advantages of digital relaying which are available today. The accurate fault location estimation algorithm has irrespective of source impedances, fault resistances, fault types, and load currents. Simulation of short circuit fault of transmission line has done by using EDSA software. Short circuit currents and voltages
4. NEURO-FUZZY HYBRID METHODS Zhong et al.,(1996) presented a method to locate faults based on fault current measurements, fault currents calculated from short circuit analysis, and system operators’ experience. Lee et al.,(2004) calculated the fault distance first to provide some fault location candidates. Then, by current pattern matching 265
IFAC TECIS 2018 266 Muhammad M.A.S. Mahmoud et al. / IFAC PapersOnLine 51-30 (2018) 263–267 Baku, Azerbaidschan, Sept 13-15, 2018
from both ends of overhead transmission line have used to input data of neuro-fuzzy method in Matlab program. Simulation results demonstrate the accuracy of the method. The results shows that the lowest estimation error for single phase to ground fault with the variation of fault resistances of 0 ohms, 10 ohms, 50 ohms, and 100 ohms, respectively, is 0.0027%, while the highest estimation error is 0.2962%.
REFERENCES Adeyemi Charles Adewole, Raynitchka Tzoneva, Shaheen Behardien (2016). Distribution network fault section identification and fault location using wavelet entropy and neural networks, “Applied Soft Computing” Elsevier B.V. Al-Shaher, M., M.M. Sabra and A.S. Saleh, (2003). Fault Location In Multi-Ring Distribution Network Using Artificial Neural Network,” Electric Power Systems Research, 64(2): 87-92 Azriyenni, M.W. Mustafa (2013), Performance Neuro-Fuzzy for Power System Fault Location, “International Journal of Engineering and Technology “ Volume 3 No. 4, 497501, ISSN: 2049-3444 © 2013 – IJET Publications UK. Bi, T., Y. Ni, C.M. Shen and F.F. Wu. (2002). An On-Line Distributed Intelligent Fault Section Estimation System For Large-Scale Power Networks,” Electric Power Systems Research, 62(3): 173-182. Brown, F., Harris, M.G., and Other, A.N. (1998). Name of paper. In Name(s) of editor(s) (ed.), Name of book in italics, page numbers. Publisher, Place of publication. Chunju, F., K.K. Li, W.L. Chan, Y. Weiyong and Z. Zhaoning (2007). Application Of Wavelet Fuzzy Neural Network In Locating Single Line To Ground Fault (SLG) In Distribution Lines. Electric Power and Energy System, 29: 497-503 Fink, D. G., and Beaty, H. W. (1993) Standard Handbook of Electrical Engineers, 13th ed., New York: McGraw-Hill. Khosravi, A. and J.A. Llobet (2007). A Hybrid Method for Fault Detection and Modelling using Modal Intervals and ANFIS. In Proceeding of American Control Conference, New York, USA. Laughton, M. A., and Say, M. G. (1990), Electrical Engineer’s Reference Book, 14th ed., London: Butterworth-Heinemann Lee, S.J., M.S. Choi, S.H. Kang, B.G. Jin, D.S. Lee, et al. (2004). An Intelligent And Efficient Fault Location And Diagnosis Scheme For Radial Distribution Systems. IEEE Trans on Power Delivery, 19(2): 524532. Muhammad MAS Mahmoud (2015), Detection of high impedance faults in M.V. mesh distribution network, Modern Electric Power Systems (MEPS) IEEE, ISBN: 978-1-5090-3101-6 Muhammad M.A.S. Mahmoud (2013), 3-Phase Fault Finding in Oil Field MV Distribution Network Using Fuzzy Clustering Techniques, Journal of Energy and Power Engineering 7 155-161. Marizan Sulaiman, Adnan Hasan Tawafan and Zulkifilie Ibrahim (2013), Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model, Australian Journal of Basic and Applied Sciences, 7(8): 81-91, 2013 ISSN 1991-8178 Martins, L.S., J.F. Martins, C.M. Alegria and V.F. Pires, (2003). A Network Distribution Power System Fault Location Based on Neural Eigenvalue Algorithm. In Proceeding of IEEE Bologna PowerTech Conference, Bologna, Italy Patton, R., Frank, P., and Clark, R (1985), Fault Diagnosis in Dynamic Systems: Theory and Application, Englewood Cliffs, NJ: Prentice-Hall.
Another Hybrid method is introduced in Adeyemi Charles Adewole (2016) , the paper has developed a hybrid 2-stage method for distribution network fault section identification (FSI) and fault location (FL)based on the coefficients from level-5 detail coefficients obtained from DWT decomposition using db4 mother wavelet. Wavelet Energy Spectrum Entropy (WEE) and Entropy Per- Unit (EPU) indices are computed from the DWT detail coefficients. These indices are used in training artificial neural network models for the FSI and FL tasks respectively. Comparison of ANN models trained using the EPU and WEE indices is carried out in terms of the prediction accuracy, computation time, processor usage, and memory usage. In order to validate the proposed hybrid method, it is applied to the IEEE 34-node benchmark test feeder. The proposed method can easily be implemented practically using actual data obtained from Digital Fault Recorders (DFR) or Intelligent Electronic Devices (IEDs). Although, data obtained from DFRs/IEDs are usually noisy, the noise would be filtered out as a result of the DWT decomposition. The ANN models trained using EPU indices were shown to require less computer memory, less processor usage, and gave faster computation speed. Possible future extension of the proposed hybrid method could be in the use of synchrophasor measurements from Phasor Measurement Units (PMUs). Also, machine learning classifiers and predictors based on decision trees/ensembles of decision trees can be applied to identify the faulted section and the location of the fault in the power system distribution network.
5. CONCLUSIONS This paper has presented an overview of fault location in power network, mainly serve oil and gas field, using Fuzzy logic and ANN. The paper summarized various locating fault methods. Different Fault locating approaches using Trending methods, Clustering Methods and Neuro-Fuzzy Hybrid methods have been reviewed. Most of the fault location techniques discussed have some limitations. Some of them are as follows: According to the advantages and disadvantages described for each method we can conclude that in comparison between all methods, Artificial Intelligent Trending Methods seemed to have more accuracy and speed and less cost. Among the (AIT), ANN algorithm are more used with accordance to the success progress in recent years. Although Fuzzy clustering technique has been used very successfully to locate different types of faults in electrical power system, but may be due to the difficulties in the data correlation, this approach has not found to be used widely. The paper has discussed some areas of possible future extension of the proposed fault detection methods.
266
IFAC TECIS 2018 Baku, Azerbaidschan, Sept 13-15, 2018 Muhammad M.A.S. Mahmoud et al. / IFAC PapersOnLine 51-30 (2018) 263–267
Ramadoni Syahputra (2013).A Neuro-Fuzzy Approach for the Fault Location Estimation of Unsynchronized TwoTerminal Transmission Lines. “International Journal of Computer Science & Information Technology (IJCSIT)” Vol 5, No 1. 23-27, DOI : 10.5121/ijcsit.2013.5102. S.R. Samantaray, B.K. Panigrahi and P.K. Dash (2008). High impedance fault detection in power distribution networks using time – frequency transform and probabilistic neural network, IET Gener. Transm. Distrib. (2), pp. 261 – 270. Wen, F.S. and C.S. Chang. (1997). Probabilistic Approach for Fault-Section Estimation in Power Systems Based On A Refined Genetic Algorithm. IEE Proc. Gen. Trans. and Dist., 144(2): 160-168. .Zhao Yong Lei Huang, Jia Dong. (2012). Research on Transformer Fault Diagnosis Based on New Fuzzy Clustering, Master Thesis, North China Electric Power University. Zhong, W. and W.H. Liu (1996). Application of A Fuzzy Set Method In Distribution System Fault Location. In Proceedings of IEEE International Symposium on Circuits and Systems, 1: 617-620.
267
267