Non-technical loss analysis and prevention using smart meters

Renewable and Sustainable Energy Reviews 72 (2017) 573–589

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Non-technical loss analysis and prevention using smart meters

MARK

Tanveer Ahmad School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

A R T I C L E I N F O

A BS T RAC T

Keywords: Non-technical loss Advanced metering infrastructure Support vector machine Linear regression analysis

In the countries such as Pakistan, for analyzing the losses and techniques in the power distribution and for mitigating, are the two active areas of research which is spread globally for increasing the accessibility of power irrespective of installing future generation equipment. As, the Technical Loss and the Non-Technical Loss are accounted by the total energy losses. They both are also referred as TL and NTLs. In terms of the non-technical losses there are major financial losses for the utility companies present in the countries that are in the developing stage. NTLs is the major cause for the additional losses and also it includes the part of damaging the network that includes the infrastructure and network reliability reduction. This paper is subjected to investigating the non-technical losses in terms of the power distribution systems. In addition to that, the consumer energy consumption information is used for analyzing the NTLs from Rawalpindi region from the different feeder source. The data of Low Voltage (LV) of the distribution network are focused more that consists of commercial, industrial, residential and agricultural consumers by the use of KWh interval data which is captured over a month using the smart meter infrastructure. The discussion of this review paper determines analysis and prevention techniques of NTLs to safeguard from the illegal use of the electricity in the distribution of electrical power system.

1. Introduction In the electricity supply companies, the customer illegal use of electricity are the main problems and the reason for the electrical theft is due to the Non-Technical Losses (NTLs). These type of losses occur because of tampering the meter, then the illegal connections, malfunctioning of the meter, irregularities of billing and because of the bills that are not paid. The NTLs problem is faced not just by the developing countries instead it also includes the developed countries like the United States of America. Taking United States as an example for the problem of NTLs, the total annual revenue is estimated to range from 0.5% to 3.5% [1]. Bangladesh, India, Iran, and Pakistan comes under the category of developing countries, where the percentage of NTL is average, and it ranges from 10% to 15%, whereas the United States of America and the United Kingdom falls under the category of developed countries. Thus, the estimated total annual revenue is comparatively low in comparison with the developing countries [2]. The losses of worldwide transmission and Distribution (T & D) are comparatively higher than the total generation capacity of the countries such as France, Germany and UK. It is known that certain companies are known for their loss which is over US $20 billion annually, just because of the theft of electricity. On the other hand, the countries similar to India faces lose that ranges to US$9 billion annually because, of the theft of electricity [1]. The observed energy losses in Pakistan are

generated as 17.5% in the year 2012–2013 and the year of 2013–2014 it is observed as 16.9% and 0.6% is accounted for the improvement of the losses. The graph represents the line losses that Pakistan faced, which is shown in Fig. 1 [3]. There are huge range of losses suffered by the utilities because of the theft of electricity. Therefore, to save from the effects of losses, it is important to reduce the process of NTL. The power system components influences the power that is generated, distributed and transmitted including the appliances of the customer. The most difficult part of the electricity is the use of illegal electricity consumption in real time. In such cases, the parameters that are used to analyze the theft of electricity are, the economic, political, managerial and criminal aspects. Different kinds of methods are described such as the efficient management of NTLS and various electricity distribution system are proposed, because of the problem that occurred in NTLs in the form of electric utilities for securing the revenue. The use of Advanced Metering Infrastructure (AMI) can reduce the NTLs in an effective way, in which it makes the fraudulent activities complex and the detection is easy. But, in such case, the cost will be much expensive for those meters. For example, they are described in various sectors like the commercial, residential and agricultural sectors [4–6]. The fraud identification of the research studies on several data mining techniques and the prediction of it is carried out by the sector called as electricity distribution sector. They include many types like statistical

E-mail address: [email protected]. http://dx.doi.org/10.1016/j.rser.2017.01.100 Received 6 November 2015; Received in revised form 1 January 2017; Accepted 15 January 2017 1364-0321/ © 2017 Elsevier Ltd. All rights reserved.

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Fig. 1. Total power system losses including (transmission and distribution) in Pakistan [3].

categorized into the following forms:

methods [48], Decision Tress [49], Artificial Neural Networks (ANNs) [50], and then the Knowledge Discovery in the Database. By getting all of these the load profiling is one of the most widely used pattern of electricity and the customer over a period of time demand its pattern. At the same time, the system called as e-metering will be used for implementing the processing of the data, and will detect the abnormal activities that are present at the load profile. Thus, it will indicate the electricity theft. To identify the energy consumption of a customer, a smart meter is used for this process, which produces additional information when compared with the conventional energy meter. The approach for grouping of illegal consumption and the process of estimation will be based on the smart meter is proposed [51–56].

1. Line diversion is unauthorized: stealing by means of bypassing the meters or the other way of making an illegal connections [13]. 2. Line tapping is unauthorized: meters will be tampering and so the lower rates of the consumption will be recorded by the meters [11]. 3. The lack of quality and the meter reading will be inaccurate [13]. 4. Electricity bill of the customer are not accurate [10]. 5. The techniques are poor for the poor revenue collection [12]. 6. With the help of the internal employees, the bill can be arranged and the irregularities can be sorted, like the lower bills [12]. 7. The non-metered supplies are not accurate when estimated. For example, rail traction and public lighting [12]. 8. Faulty meters cause loss [10]. 9. Non-payment of electricity bills [12]. 10. The protective equipment, cables, conductors and the switchgear becomes loss if the equipment are damaged [11].

2. Power system losses The losses of electric power system will be affecting the utilities and it can be categorized into two types:

The electrical distribution business will help in the form of detecting the NTL, and it is said to be the very important task; because in Spain, represents the percentage of fraud based on the form of energy and in respect with the total NTLs. The NTLs is about 35–45%. There are so many fraud for the works and researches and they are from the literature. The researched based on fraud and for the detection of NTL are present in various fields [56–65]. But, the researches that are based on the detection of NTLs in the power utilities is less though, the percentage of the NTLs is comparatively greater [66–70]. The research works are generally based on theory and the types of detection techniques used are less. The electrical companies adopt to the current methodology in terms of the NTLs detection. It is basically divided into two kinds. They are listed below:

(1) The technical losses (2) The Non-technical losses The difference between the quantity of the energy delivered and the quantity of the energy recorded is defined as the power losses, and they are sold to the customers.

PLOSSES = PDELIVERED − PSOLD

(1)

The amount of energy can be represented as PLoss and the amount of energy delivered can be expected as PDelivered. Whereas, the amount of energy either recorded or sold will be determined as PSold. 2.1. Technical losses It mainly consists of power dissipation in terms of electrical system components. The components will be described as the transformers, transmission and the distribution lines, and energy system’s measurement unit. But, at the same time the losses are in technical terms and are being calculated during the phases of design and construction. Their power transmission and distribution networks are also mentioned. The values of the technical losses will be known by the utilities [7–9].

(1) The primary type is based on the making of situ inspections for the wanted users, which is selected from the earlier zone. (2) The secondary type is based on the user’s study and it contains the null consumption at a certain point. The primary issue that is faced in the primary alternative is that, it wants vast numbers of the inspectors, which results to high cost. The secondary alternative contains the problem of possibly detecting the users only with the null consumption. But, for the customers who have non-null consumption will be quite low when compared with their consumption. In recent days, the techniques of the data mining [71,72] will be applied to one or more fields and then the power utility will be considered as an industry, where it meets its success [73–78].

2.2. Non-technical losses The actions of external are caused by the Non-technical losses and then these actions are follows: power system’s external actions, mainly the theft of electricity, customer non-payment regards to the errors in the meter reading and in maintaining the record. The NTL activities are contributed and the factors can be shown in [10–13], and it can be 574

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Fig. 2. Flow of Power in advanced metering infrastructure.

AMI stands for Advanced Metering Infrastructure, in which it is determined as the primary step of process and moves towards the smart grid. It has been well known for its acknowledgement for power utilities. By using the advanced metering infrastructure, the automatic system of the management, the enchanted control mechanism of the low voltage distribution system can be achieved. They are being operated for the additional capabilities and the integrated systems with the use of the distribution automation system. The smart grid infrastructure contains the integral part named as the smart meter systems, in means of data collection and the data communications. It is known for their modern type of the transmission and the distribution with the aspects of electric grid. By functioning it, it will be determined as the automatic electric power system and that, it monitors and controls the activities of the grid, by confirming the efficient and the reliable process, the electricity flows in two-ways and the information is shared between the power plants and between all the points. The smart grid is also used for monitoring the delivery of the electricity and then it tracks down the consumption of the power with the smart meters that is used to transmit the energy usage of information to the utilities through the communication networks. It will allow the customers to track down their amount of own energy that is used by them on the internet, with the computer program as a thirdparty. There are two ways of nature for the smart meter systems and it allows sending the commands to perform it using the grid infrastructure devices, referred as distribution switches and the recloses, and these are able to provide the energy for more reliable system, and this is defined as the Distributed Automation (DA). For enhancing the power quality, the AMI will be having the capability. Between the smart maters and the central control stations there will be a two way communication. By the use of these communications they can send the billing information to the customers. The smart meter can be used to control the individual household loads with their ability. By the benefit of micro girds and the AMI’s network, we can possibly get the pattern of improved efficiencies and the moderating energy usage [14,15].(Fig. 2).

3. Advanced metering infrastructure The electrical grid will be named as the smart gird which contains different variety of operational and energy measures, smart appliances, smart meters, energy resources that are renewable and the efficiency of energy resources. The important aspects of the smart grid will be determined as the electronic power condition and the controlling process of production and the distribution of electricity. The United States of America follows a set of policy that supports the modern nation’s electricity transmission and helps in maintaining the distribution system. Then, it can create reliability and can protect the infrastructure of electricity. Further, it can meet the demands of the growth of the future generation and can help in achieving the following factors [79]: (1) Integration of the distributed resources and the deployment of the distributed resources and the generation, along with the renewable resources. (2) Usage of digital information will increase and the control technology will increase the security, reliability and electric grid’s efficiency. (3) Along with the full cyber-security, the dynamic optimization of the grid operations will take place. (4) Energy efficient resources and the resources of demand-side, and the demand response will be incorporated and developed. (5) Combination of the consumer devices and the smart appliances. (6) Providing the consumers with control options and timely information. (7) With the use of ‘smart’ technology, it can optimize the consumer devices and other appliances physically. It is used for metering the communicating concerns, its operations, and automation of the distribution. (8) The combination of advanced electrical storage and the techniques of peak-shaving technologies along with the plug-in electric and the hybrid electric vehicles will be deployed. (9) Progress in the communication standards, the operating process of the appliances and the electric grid connected equipment along with serving the infrastructure of the grid. (10) Gathering it and lowering the unnecessary barriers, will result in adoption of smart grid technology, services and practices.

3.1. Smart meter Electrical energy is recorded by the use of the smart meters and then simultaneously it also sends the data back forth to the central server for the purpose of monitoring and analysis of the data. The main 575

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manage and control the supply of the electricity or any of the home appliances. There will be a connected source between the harmonic signals and the distribution transformer called Hybrid Filter. They are used for the process of preventing the power system components damage from the unwanted harmonic signals. It consists of two filters namely active and passive filter. By the use of four fully control able IGBTs can build the active filter. The combination of inductors and the capacitors will be the passive filters [16–22].

functions of smart meters, which are followed by the measurement of the power flow due to the bi-directional form with the smart meters. The main functions are recording the energy consumption data and to give an interval between the reads, link with the communication, connecting and disconnecting, remotely programming and theft detection. The consumers that are described will be wasting the energy and they will make use of the energy in large amount of their sanctioned load. The consumers are based on their functions namely, home area network and the commercial and industrial consumers. Smart meter will be used for the controlling process of the energy that is misused. For certain cases, there will be certain limit for the power supply, when it exceeds the smart meter, it will cut off the power supply. Then, it will be restoring the power within its prescribed limits. The energy management of the government goals will be able to accomplish using smart meters. At present times, the distribution companies will be facing the challenge for changing the conventional meters with the smart meters. It is also used for updating the system. It has the biggest advantage of receiving the energy consumption data continuously and quickly and the processing, transmitting process will be received continuously. When the distributed companies are being changed with the smart meters, they will be able to find and locate the region and then it will be controlled by the use of power consumption. Further, it is able to find where the power consumption is very high. With the use of the smart meter we can reduce the cost of the meter reading and the collection of bill. Due to this feature, it can also improve the accuracy of the bill and can reduce the theft of the error. With the daily usage, the smart meter can be used to record the usage of the electricity and the gas, with addition to it, also it can have the possibilities of recording the consumption of the water. As delivered in Fig. 3 the architecture of the smart meter will be having an energy meter, harmonic sensor, circuit breaker. International Control Station (ICS) will be the central processing unit of the registered meter called smart meter. And also it will used for controlling the components of the smart meter. Smart meter will be allocating the portion for the energy mater. With the use of this the amount of consumed electricity will be recorded. After receiving the request from the ICS, the circuit breaker will be disabling the supply of the electricity. For detecting the presence of the harmonics the harmonic sensor function is used. There will be a work station present for the wireless transceiver and the control system and it will designed by the (ECS) external control system. By the use of this workstation, we can compute the issues of the control signals and the non-technical losses. The smart meter will be having wireless transceiver, and it is used for sending and receiving the data and controlling the signals, which is among the ECS, ICS. The ECS will be having the ability to

3.1.1. Smart meter benefits The smart meter benefits for the customers are: A. When an outage occurs, the smart meter will allows the detection of the faster outage and the service for restoration. B. The customers will be provided with the greater control of their own consumption of the electricity. When they available with the functions of time-based rates and the range increases, due to the different pricing plans that are available to the customers and providing them with more chances of monitoring their consumption of the electricity and the bills obtained. C. The smart meters which are produced to the customers will be having the access for prior’s day electricity, and the usage will be through the utility website. D. Smart meter will be helping the environment for reducing the need for building the power plants. To meet the high demand, the utilities will be avoiding the use of the peaker plants, the benefits of the environment will be determined as the peaker plants and typically it will having the higher greenhouse gas and some other air missions. E. It will be increasing the privacy because of the information about the usage of the electricity and it can be relayed according to the automatic utility process of the billing purposes without on-site visits by the use of the utility it can check the meter. F. Smart meters are the first preferences for creating the smart grid. For the use of the smart grid every aspects of the industry will be applied by the digital technologies, and from the generation, to the distribution, transmission and to the interface of the customer. By the help of the sense of the smart grid, it can be observed that what is happening to the energy flows and should keep it in the balance and then improve their reliability. 3.1.2. Smart meters worldwide deployment The interface between the smart grids and the consumers will be named as the smart metering. In Fig. 4, it will be delivering the process for installing the smart meters for measuring the power generation and the automation of distribution system as per the world markets of year

Fig. 3. Proposed new model of the smart metering infrastructure.

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further steps like setting up the accurate form for the billing purpose of the customer. After the installation of the smart meter, it is always ready for the operation that has to be performed and it will be proceeded automatically with the network system. There will be a notification generated for the customer regarding the failure of the installation, no presence of the user and the notification will be delivering the time for the customer for later installation process [81]. 4. Theft detection techniques There are drawbacks which are major for the process of implementing the policies existed, then following the electricity theft, and it includes corruption and weak infrastructure. As a unit of the utility company, the rules and guidelines for controlling the electricity theft are implemented for reducing the electricity theft. The corrupt employees will be in the same system and will be displaying the least dedication. For finding the various types of electricity theft, there are some theft detection techniques that are used on the power distribution system. By using all these features the NTLs can be detected.

Fig. 4. Installed capacity of the smart meters world-wide, world market: 2012–2015 [23].

(2012–2015). The Europe will be considering them self as the part for implementing the maters to 80% of the total population by the 2020 [20].

3.1.3. Smart meter installation The process of actual installation of the smart maters will be the model for planning the installation for normally. The part considered, if it is done correctly, it can be able to lead to the installation smooth with the low errors and the issues from the customers, delaying time for the installation [81]. The aspects for the installation will be safely and can be followed as below:

4.1. Harmonic generator deployment for theft detection and prevention The paradigm shift will be our proposed approach from the method called conventional method. It will be used for the process of finding the illegal customer, by the use of the physical observation on the distribution feeder. Otherwise, in the evaluating part the load pattern will be generated for the customers. The observed Fig. 5 will be showing the operations in the form of the flow chart for the proposed systems. The operations that are carried out in a normal way, the smart meters are presented in the household’s transmit instantaneous values of the power consumption. The power factor and current for the (ECS) are placed in the utility company. The ECS will be generating the total non-technical losses from the values that are being received. The ECS will be sending the control signals for the internal control station of the

1. For utilizing the wires- (NESC) The National Electric Safety Code. 2. For the wiring process in the home- (NEC) The National Electric Code. 3. For the codes of the Electricity Metering- ASNI C12.1 4. Codes for the local building. The beginning steps of the installation will be involving in the assessment for the access to the meter location and provides the safety for the equipment’s that are existed. After the establishment for the proper access, the following actions will be included: 1. For the safety issues, damage and diversion, it will be checking the meter location. 2. The meter form type and the service provider will be verified for the meter data. 3. Verifying the correct address, GPS location and meter number by the use of the premise information. 4. For the purpose of the new installation, the customer premise information should be updated The housing unit split will be recorded approximately by the national demographics [80]. The housing unit will be 74% for the single family and the 26% for the multi-family, and the percentage will be different from the state to state [80]. By concluding this, there will be wide majority for the installation process of the smart meters and it will be to the homes of single family with the designs of single meter. Generally the meter base will be mounted to the exterior wall surface, where the house will be attached with the service entrance. For the multi-family dwelling units the multiple meters to a few locations will be consolidated using the designs of the gang meter socket. Usually the gang socket will be placed in the meter rooms, in the wall outside of the apartment buildings or it can be placed in the basement of the long rising apartment buildings. Though the multifamily installations will be more complicated than the single family installations. But the process of the installation steps will be same for them. For an extra function the two processes will be designed to show the safety concerns and the physical access, to see that the smart meter is installed properly and safely and the information will be generated and delivered for the

Fig. 5. Flow chart for detection of electricity theft using harmonic generator [56].

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present smart meter to cut off the supply of electricity to the perfect customer. This will take place in case of the computed non-technical losses, which are higher than the 5% of the energy that is distributed. Coming to the process, the illegal consumers are separated from the genuine consumers. And the process of the legal consumers are being isolated, and the illegal consumers will continue the process of drawing energy from the grid. Just before the power cut in the neighborhood, the generator called harmonic generator will be processed to on and off for seconds. To produce the distribution feeder from the waste harmonic components the function harmonic generator is used. By using it, the customers who are illegal will be affected in the performance of their home appliances. After the process of operation on the harmonic generator, the signals of the ECS will be giving the notification for the ICS of all the smart meters in order to the restoring process of the power supply to the genuine customers. All this process continues, after the harmonic generator is switched off [24,56]. 4.2. Support vector machine The vapnik is the one, who invented the support vector machines. They are divided into the set of related supervised learning methods. With the respect to the output, the support vector machines will analyze the data given and can be able to recognize the pattern or data trend in which it respects with the output. For this process, the classification of the statistical data and the regression analysis is done with the use of the SVMs [27,28]. The set of rules is determined by receiving the training data and these generated model is developed by the training algorithm of SVM. Basically, the SVMs will be developing a set of hyper planes and a single hyper plane in a dimensional space of high or infinite, and it is based on the classified data. Considering a difference between the data points which is classified as it is achieved and for describing a largest distance to the shortest training data points of any type of class in the hyper plane. When the separation margin is high, the classifier will be termed as minimal in the traces of the generalization of error. For the past years, there are some applications which are generated by the SVMs [29]. The applications are data mining and face recognition. In case, if the SVM is to be tested and trained, the noise data must be developed according to the registered criterion. The development should be based on the approximate consumption of the energy with their pattern. Before the process of selecting the consumption energy data, the data must be considered and the parameters should be inserted. The SVM model are: 1. Based on the locations of geographical (east, north, west, center, south) 2. Seasons that will be in every year (winter, summer) 3. Customer classification (small, large) depending upon the area they live. Basically, the SVM is used for analyzing the consumption of the energy in terms of the data and it will be used for the process of recognizing the consumer energy consumption in terms of their present behavior. The consumer load profile information is used by the SVM, and is also used for exposing the behavior that is abnormal and highly correlated with the terms of (NTL). Based on the overall consumption of the energy, the customers are grouped in some forms depending on their size of the house, and the electrical appliances they use. For the illustrating process, Fig. 6 will be generated. The illustrating process will be the operational terms of the SVM. The operational flowchart for the theft detection system is adapted in terms of energy during the given interval of the time. The energy consumption data that is collected will be the series of energy consumption values that occurs instantaneously. The support vector will not support the format of this present data so, the data has to be preprocessed. For the purpose of training, a single part of the data is used. Hence, the input file for the data used for training will be the values of the output and the values of

Fig. 6. Flowchart for support vector machine [25].

the quick energy consumption. It will be representing the class in which the particular instance will be present. By concluding it, the consumers will be categorized into three classes based on the rules given below: 1. Classifying under the class D: The approximate consumption pattern must be followed by the energy consumed by the customers. For the two hours in 24-h period or the 96 inputs, it can have 578

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the illegal customer because of the stolen of electricity and they are responsible for the process of billing and moving with that irregularities. In the class S the customers will be involving in theft, and the customers are viewed for more time and being processed for any fault behavior [25–35].

zero consumption read. The six individual zero-energy consumption inputs must be produced. Then, for the process of the addition it will include the customers who are all having zero-energy consumption. 2. Classifying under the class S: The energy consumption pattern must be followed approximately by the customer’s energy consumption, and can proceed with minor changes or putting more than three inputs of the zero energy consumption in one hour or without any repetition. With not more than the two clients, it can have the two zero energy consumption. More than the given amount of six individual instances of the zero energy consumption will not be proceeding to the next step out of 96 inputs that are represented by 24 h in a day. 3. Classifying under class-I:

4.3. Energy theft detection using central observer meter The novel methodology is proposed in the system and then the detection will be automatic for the illegal utilization of the electricity used in the future distribution networks and then it is equipped with the infrastructure of the smart metering. Due to the overcome of the SM infrastructure the countries like European is moving towards the SM infrastructure from the traditional electricity metering. The standard that is in Netherlands (NTA 8130) for the SM will be defined as the basics function and the parties for the infrastructure of the SM. What so ever, the proper statement of the SM will define upon the level of functionality that is not fully developed [82]. In case of the managing process the efficiency for the new services will be integrating in the future grid, and then communication which is suitable must be performed. The method that is proposed for the detection in advance will be the illegal for the use of the electricity with respect to one of the features in the feasible type. And in which it should not be the type of form which is omitted in the period for planning and the process will be in the future standardization for the SM infrastructure [83,84].

The approximate energy consumption for the customer will be facing a small changes in the customer’s energy consumption profile. By including the repetitions, there should not be more than two non-continuous zero inputs. The electricity consumption that is received should be changed to other format, so only it can be a compatible format for the training data. Based on the locations of the geographical, the data must be used before the training, in case it belongs to the weekdays or the weekend, customer range based on the load capacity and in which year of the season the data is represented. In the database the data is being transmitted into the central control station where the database is located. For the purpose of training SVM model the training data is being used and it also used for testing the data and also for process of testing it and detecting the consumption that is named as illegal. The energy consumption will be continuous for only the customer profile that is genuine. And by checking the energy consumption pattern, the customer will be named as the genuine customer and it will be sent to the database. Suppose the suspicious profile found of the customer, then that customer will be checked. If all the rules that are specified are violated, the customer will be categorized the D class. And immediately the customer can be inspected, is due to the high range of the illegal consumption, if the profile of that customer doesn’t fall under the class, and it doesn’t matter the customer is large or the medium, but they are being tested for the regulations that are illustrated for classifying under S. The customers who are very small, medium and large are divided and shown in Table 1: The profile of the customers which are not being under the S class, the losses of the calculated overall distribution will be more than the 5% and then the class I will be generated after the profile of the customer is rechecked in terms of the rules. After the passage of the distribution transformer the losses of the distribution will be less than the 3–5%. The illegal consumption will be occurred, when the distribution losses are more than their range. The customers who are not under the part of the class I will be named as the genuine customer. Some customers are categorized in the class D are quickly checked. The customers classified under the S class either they are large or medium, they will also be checked and they are getting reported to the database for quick checking in. The outputs of −1,0,1 will be results classified under the classes D,S,I. Customers who are all named as 1 will be under the class I, class S will be named for the customers with 0, and the class D contains the customer generated as −1. The class D will be named as

4.3.1. Investigated network configuration In the future grid, in the Netherlands for stimulating the real conditions the typical LV grid configuration is considered as a base simulation of the model, where the cable networks with MV/LV transformer and 240 customers connected. As per the future grid's anticipated improvements, DG that is dispatched to the LV grid is added according a substantial amount. An implementation known as the “intelligent substations”, is called as future grid's additional feature. This implementation will have to improve MV and LV network's flexibility. When compared with the traditional substations, contains traditional substations, the intelligent substations will combine the controllable energy storage and it will have the MV/LV transformer, which contains the automatic tap changer known as “Smart transformer”. Therefore, the loading and voltage profile in the MV grid will not be associated directly with the voltage profile in the LV grid. A monitoring system such as the intelligent MV/LV substation will be improved, which can help in easy identification of theft in the network [85]. The information that is received from the SM will be used for proposing the methodology of detecting the electricity tampering. This will be useful for automating, then for detecting and in the process of localization. The distribution network in future will have the infrastructure of SM. The topology consists of the smart metering infrastructure and intelligent substation as an important part in the electricity theft detection that is proposed. 4.3.2. Detection of illegal use of electricity There are two phases that are involved in the method that is proposed for detecting the method for electricity tampering attempts. The initial phase emphasizes on the occurrence criteria of illegal usage inside the LV network. Whereas, the other phase emphasizes on POC localization and it is expected have illegal use. For evaluating the illegal electricity use based on the initial phase, must depend on the estimation and measurement of balancing the energy at the substation of MV/LV. In case of the mismatch of the local energy balance with the actually including the losses and the already measured one, it is required that the sequence of the localization must be initiated for verifying the occurrence of the illegal use occurrence inside the network that is investigated. This illustrated in the below Fig. 7. Only in the substations, the points of measuring and the data

Table 1 Types of very small, medium and large customers [25]. Consumption

Very-Small

Medium

Large

KWH per Month KWH per Day KWH per Hours

00–500.0 00–17.0 00–0.70

500.0–2000.0 17.0–67.0 00.7.0–2.70

2000.0–20000.0 67.0–667.0 2.70–27.70

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results with the balancing of power. The first priority are the cables that are focused to provide the violating attempt before the meter or on the terminals of the meter [87]. The tampering attempt's functional adjustments basically depends on the presumptions and it must be considered that there is no burden on the currents that are measured by SMs. On the other hand, the POC's voltage must be measured as the local values that easily reflect the power drawn from the nearby branch of POC. The meter's voltage is influenced by the illegal use of the power from the nearby branches. Hence, the illegal electricity usage is burdened by the voltages. Every time there is an attempt of tempering, voltage profile for all POCs connected will reflect it. Thus, Theft localization present in the finial procedure, refer the Fig. 7, as it shows the comparison of the voltages that are measured and the estimated for the localization. 4.3.4. Errors in the influence of measurement After the measurements for the accuracy in the errors, the expected data from the SMs are mainly affect the errors in the measurements. The values in the measurement should contain the standard errors in the measurement for distribution and also these measurement errors are attenuated by the extension of the time frame. Still in the process of stimulation, showing the proper methods the robustness and also it shows the robustness for the errors that generated randomly. In fact the results are obtained in the successful manner for the tampering attempts in the correct positions are found easily. The localization accuracy that mainly depends on both the accuracy measurement of the SMs and the time frames in the amount. These two methods are used in the localization. From the localization, the location in accurate that does not provide the clean and clear difference. Then the localization that extended the estimation from one time frame to another time frame or may be the time frames are aggregated, till the order of the localization that provides the level of confidence satisfied [88]. Different types of methods in their models are used to find the theft in electricity. Above methods of mathematical or other models are shown such as Support Vector Machine Radial (SVM-RADIAL), MultiLayer Perceptron (ANN-MLP), Artificial Neural Network and Optimum Path Forest classifier (OPT) [36,37]. The technique of Multi-layer is mainly depends on the some of the methods for modelling, which obeys some of the other methods like tree diagram or Non-Linear Statistical Modelling (NLSM). The technique for recognizing the supervised pattern are commonly used. Others like Support Vector Machine and Artificial Neural Network may be put for the purpose of identifying the commercial frauds through automatic methods. Anyhow, they bear a lot from the convergence in slow and burden high computation [38]. According to [40], it is demonstrated that the Optimum-Path Forest classifier for identifying the losses related to the non-technical ones are higher than the neural networks but they resemble to the Support Vector Machines in a faster manner. Though the techniques such as artificial intelligence are increasing there is some need to focus on the drawbacks. For instance, the ANN with multi-layer perceptrons (ANNMLP) has the capacity to both address the issues in a linear and in a non-linear way. But, the exception is that it is not possible to do the non-separable situations [89]. Being a classifier, which is unstable, the ANN-MLP collections can improvise the performance level of the classifiers with an unidentified limit [90]. The development of the vector machines is initiated for resolving the existing issues [91]. To support this the linearly separable classes in a higher-dimensional feature space is assumed [93]. The support vector machine's processing cost surges as the training size and the support vector increases. Being a binary classifier, there is a need of multiple SVMs for solving the issues related to the multi-class. The new framework which reduces the issues of pattern recognition for the graph-based classifiers same as the Optimum Path Forest (OPF) computation in the feature space that is induced by the graph are shown [92]. Such classifiers will not obstruct the tasks related to classification, which is known as the hyper planes optimization issues whereas, the combinatorial optimum-path compu-

Fig. 7. The flow chart for detection electricity theft using central observer meter [88].

processing will take place in the primary phase. Therefore, the occurrence criteria must run in all the time frames irrespective of maximizing the data or the requirements of computing for the infrastructure of communication. Through SM, the production of data and the consumption of energy must be present. The reconsideration for the illegal detection once again is possible only for the localization sequence that is implemented to define the time frame. In the scenario, where there is no encountering of accuracy level, it is suggested that the localization must intend to concentrate on the other time frame [86]. 4.3.3. Tampering localization procedure Generally, the illegal use of electricity takes place only where the meter is nearby or inside the property. The relays secretly controls the frequent illegal use of the electricity for complicating the fraud detection. When the illegal use of electricity theft is encountered it will be automatically launched, as the substation level shows positive 580

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5. Simulation and results

Table 2 DISCO’s revenue collection efficiency in Pakistan [47]. DISCOs

2009– 2010

2010– 2011

2011– 2012

2012– 2013

2013– 2014

PESCO HESCO QESCO LESCO GEPCO FESCO IESCO MEPCO

71.0% 77.0% 86.0% 98.0% 98.0% 99.0% 98.0% 97.0%

67.0% 68.0% 80.0% 96.0% 95.0% 97.0% 97.0% 96.0%

70.0% 60.0% 76.0% 93.0% 96.0% 97.0% 96.0% 94.0%

78.0% 59.0% 41.0% 98.0% 99.0% 97.50% 93.0% 98.0%

68.0% 60.0% 36.0% 96.0% 98.0% 98.0% 96.0% 97.0%

With the implementation of the linear regression analysis, the spearman rank correlation coefficient test and distribution line monitoring system to detect the theft of electricity with the help of Power Line Communication (PLC) is done which is developed to detect the theft of electricity on the electrical distribution system. The Matlab software and the SPSS are used for analyzing the performed operations. 5.1. Linear Regression Analysis In the linear equation, the appropriate linear regression applies for modelling the two variable's relationship according to the data that is observed, where one of the variable is referred as the explanatory variable and the second variable is referred as the dependent variable. The “X” and “Y” variable's average values will be identified using the regression analysis method. Each variable has a purpose, and it is determined that the variable “Y“ contains the data that is retrieved from the energy which is produced either with the energy that is received or from power house or from the feeder energy that is delivered. It is determined that the variable “X” provides the energy paid back values as a bill format for the utility from customer's side. The following equation provides the correlation which is present among the two X and Y variables as follows:

tation are present for the rest of the nodes. Every single node has been categories as per the strength of the prototypes that are connected which is defined as the discrete optimal partition for the feature space. Every single prototype acts as the root from the optimum-path tree. There is a technique proposed by Ramos, C. C. O et al called as a technique which is based on the OPF to identify and to safeguard the NTLs. The phase of implementation is fast according to [39] and [40]. It is determined that the OPF has higher chances to identify the electricity theft. On the other hand, they are even higher in accuracy when compared to SVM-RBF, ANN-MLP and SVM-LINEAR [41–46].

ρ= 4.4. Distribution companies (DISCOs) wise revenue collection efficiency in Pakistan

(2)

The above equation contains some of the elements as follows: “σx” resembles the standard deviation of the variable “X”. “σy” resembles the standard deviation of the variable “Y”. Finally, “ρ”, it resembles coefficient of the correlation. The above equation can also be written in the below mentioned form:

The reasons for the largest contributors of the circular debts are the non-payment bills of the electricity used, then the industrial customers and the commercial customers. The same reasons are not applicable for all the companies in the world, there are some of the firms that take care of their records which are not possible by some of the firms and they faced the low revenue collection issues. This is showed in Table 2 [47]. The issues that are being escalated due to the circular debt is because of the below mentioned reasons:

• • • • • • • • • •

σxy σx. σy

ρxy = Cov

x, y σx. σy

(3)

The data reflects the dispersion with the co-variance (Cov). Cov is a function's property to retain its form during the cases when there is a linear transformation of the variables. Co-variance's formula is provided below. It is a combination of the expectation and the mean.

Theft of electricity is the main reason. The revenue collection of DISCOs is poor. The delay in infusing the cash in the power sector using the fuel price methodology. Losses related to transmission and distribution. Long term approval by the court for the Fuel Price Adjustments (FPAs). The governance is poor. Tariff determination delay Contract payment for the subsidy of the Tariff Differential Subsidy (TDS) and Karachi Electric Supply Company (KESC). Late notification from the Government of Pakistan (GOP). Late payments on Tariff Differential by the Ministry of Finance (MOF).

Cov (x, y) = E [(x − x )( y − y )]

(4)

The above equation contains some of the elements as follows: “E” resembles expectation/expected value. “E” is the random variable’s weighted average value. The following equation provides the basic equation for finding sample’s squared standard deviation, which is also referred as the energy consumption data.

σ² =

∑

2 x2 ⎛ x⎞ − ⎜∑ ⎟ ⎝ N N⎠

(5)

The Islamabad Electric Supply Company’s energy consumed data is provided in Table 3. The analysis to identify the energy theft is conducted in various parts of the Rawalpindi region by the various feeders. Fig. 8, projects a scatter plot, which represents that the X-axis with the customer's monthly metered load denoted as “X” whereas, the Yaxis represents the feeder section delivered load denoted as “Y” with the help of the SPSS Statistical software. The Scatter plot displays the correlation among the utility-supplied load and the total customermetered load. In the plot the slope changes refers to the losses based on the technical front and due to the errors of calibration, which is projected in Fig. 8 by the slope 1.007. The percentage of technical loss is corresponded in Fig. 8 by 0.7%. The theft of monthly electricity is determined as 519.93-MWH by Fig. 8. If there is any data point's dispersion in the linear regression line then it easily represents the electricity theft is not constant when a whole month is considered. As

The federal government doesn’t plays a vital role to pass the legislation for controlling the theft of electricity, to promote energy conservation, to strengthen the regulatory process, to achieve transparency and to encourage an open and competitive market. In Pakistan there are certain issues that are worsening the situation of the circular debt such as, insufficient budget by TDS, poor tariff terms and conditions, opposed generation views, poor thermal power plant efficiency, non-commercial or the non-professional approach for shedding the load, promoting the demand side management and Late Payment Surcharges (LPS) were GOP neglected by Central Power Purchasing Agency (CPPA) to Independent Power Producers (IPPs) [47]. 581

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Table 3 Energy consumption data from different feeders in Rawalpindi Region (IESCO) by July 5, 2015. Sr. #

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

11-KV Feeder Name

11-KV Out/Going NOGAZI 11-KV Out/Going AIR PORT NEW FEEDER DHAMIAL CAMP 11-KV OUT/GOING ADYALA FEEDER RADIO PAKISTAN NEW 11-KV OUT/GOING CHHAB FEEDER DHOKE FARMAN ALI FEEDER GAWALMANDI FEEDER KHADAM HUSSAIN FEEDER NATIONAL PARK FEEDER PAKISTAN AIR FORCE FEEDER CHAKLALLA SCHEME-3 11 kV OUT/GOING MEADIA-II FEEDER RIVER GARDEN FEEDER SWAN-II FEEDER OUT/GOING SECTOR-H FEEDER OUT/GOING KLM 11-KV OUT/GOING AHMEDAL FEEDER RADIO PAKISTAN FEEDER ENGINEERING.M.E COM. 11-KV OUT/GOING AZHAR ABAD 11-KV OUT/GOING CHAHAN FEEDER WILLAYAT COMPLEX FEEDER OUT/GOING FAUJI/FOUNDATION FEEDER NAWAZISH SHAHEED 11-KV OUT/GOING FEEDER HFF- 1 11-KV OUT/GOING FEEDER HFF−3 11-KV FEEDER TAXILA 11-KV O/G FEEDER BHATIOT FEEDER LANI WALA 11-KV OUT/GOING FEEDER TANAZA DAM FEEDER SAJJAD SHAHEED FEEDER OUT/GOING BEHLOT FEEDER 1- KV OUT/GOING W/COLONY FEEDER 11-KV O/G AMIN ABAD FEEDER 11-KV OUT/GOING P/COLONY FEEDER 11-KV INCOM./C NO−2 FEEDER 11-KV TENCH BHATHA FEEDER 11-KV AWC HOUSING FEEDER COL. SHER KHAN FEEDER RASHID MINHAS FEEDER SHAMSABAD FEEDER 11-KV OUT/GOING HAZRO FEEDER 11-KV OUT/GOING MALHOWALI FEEDER GHARIBWAL FEEDER 11-KV OUT/GOING KACHARI FEEDER 11-KV OUT/GOING SHADI KHAN FEEDER MANSER FEEDER OUT/GOING PIND SULTANI FEEDER 11-KV NARRA

Energy- MWh Received (a)

Sold (b)

1398.0 235.00 7222.00 10752.650 14311.960 3322.1150 11099.690 8013.060 9663.0 13182.0 9145.6280 15738.950 3170.9520 4187.0750 2526.50 330.00 3060.0 3787.380 7857.3940 14601.00 7579.9080 7850.0 8689.980 8642.20 2862.880 7424.00 3064.00 4480.0 945.220 6171.520 4069.6060 1954.00 7826.00 7901.1980 6815.6340 2602.080 6386.00 1112.5180 5014.10 9821.5640 2823.2420 366.0780 836.960 5018.340 6632.6310 826.00 1979.4950 590.5250 590.5250 7579.080

1397.0720 235.5240 7162.320 10768.5450 14304.9440 3326.1430 10480.2870 7941.8340 9676.5520 13055.9660 9125.340 15512.5880 3175.2380 4185.640 2520.2230 331.040 3058.9470 3794.1520 7873.7340 14627.4590 7593.7480 7838.2480 8689.8190 8629.9110 2848.0250 7271.70 3057.5090 4490.0470 939.1710 6167.1290 4068.1090 1899.6480 7991.4570 7889.760 6815.9730 2567.60 6365.7470 1109.430 5001.0250 9783.6860 2817.5220 366.00 839.7840 5011.4140 5633.9260 785.0880 1978.7010 590.2690 7313.5920 7529.7140

b1 =

∑

∑ . (Yi − Y )² ∑ (Xi − X )(Yi − Y i )

Increase/Decrease in Losses (a-b)

0.00664 −0.220 0.82640 −0.140 0.04900 −0.120 5.58040 0.88890 −0.140 0.95610 0.22180 1.43820 −0.130 0.03430 0.24840 −0.310 0.03440 −0.10 −0.200 −0.180 −0.1820 0.14970 0.00190 0.14220 0.51890 2.05150 0.21180 −0.22430 0.64000 0.07110 0.03680 2.78160 −2.1140 0.14480 −0.050 1.32510 0.31710 0.27760 0.26080 0.38570 0.20260 0.02130 −0.30 0.13800 15.05740 4.95300 0.04010 0.04340 00.8192 0.06513

0.9280 −0.520 59.680 −15.80 7.0160 −4.00 619.400 71.2260 −13.50 126.030 20.2880 226.360 −4.280 1.4350 6.2770 −1.040 1.0530 −6.70 −16.30 −26.40 −13.840 11.7520 0.1610 12.2890 14.8550 152.300 6.4910 −10.040 6.0490 4.3910 1.4970 54.3520 −165.50 11.4380 −0.30 34.480 20.2530 3.0880 13.0750 37.8780 5.720 0.0780 −2.80 6.9260 998.700 40.9120 0.7940 0.2560 60.0408 49.0366

dent variable is easily predicted with the help of the variable which is independent. It is noted that the coefficient of determination each time is just between 0 and 1.

mentioned earlier, the slope of the scatter plot provides the losses based on the technical and calibration errors aspect with the help of linear regression on the load. Moreover, the y-intercept provides the straightforward values of the theft. Centroid=(X , Y )=(5728.746, 5776.86226). The centroid is a place where whichever regression line fits best will surely pass this or it should pass from this point. The centroid consists of the “X” and “Y” variable’s mean value. The regression line’s equation is as follows:

ˆ =bo +b1 . Xi Yi

%-Line Losses (c)

r²=

n ( ∑ XY ) − ( ∑ X )( ∑ Y ) [n ∑ Y 2−( ∑ Y )²][n ∑ X2 −( ∑ X )²]

(8)

So when we led the relapse, the SSE (A relapse chooses the line with the most minimal aggregate whole of squared forecast mistakes, this esteem is known as the SSE or Error based on the total sum of Squares) diminished from SST to SSE (or from 779732138.7 to 14559360). The aggregate entirety of SST Squares is equal to Sum of Squares Regression plus Total sum of Squares (SST=SSR+SST) so therefore Sum of Squares Regression is equal to Total Sum of Squares minus Sum of Squares Error (765172778.5=779732138.7-14559360). SSR is consider as total square variations form SSE within the interval and SSE

(6)

(7)

From the analysis of the regression, the resultant key is the coefficient of determination, which is denoted as r2. The understanding is that, the variance’s proportion in the depen582

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Fig. 8. Regression line fitted on customer-metered loads and feeder section load delivered (MWh), July 07, 2015.

Determination Co−efficient = r 2 =

765172778.5 = 98.13277413 779732138.7

Variable X and Variable Y has two ranks as R(xi) and R(yi). Two Variable’s (A > and B > ) positive correlation is obtained from positive relationship co-efficient and two variable’s (A > and > B) negative correlation is obtained from negative relationship co-efficient. A relationship co-efficient of null (0) determines the correlation between two variables are zero. Regardless of the possibility that the connection coefficient is zero a non-straight relationship may exist. Existing information might ordered already but we need to order our information again and again. In Table 4, Test for Spearman rank relationship co-efficient energy utilization information can quickly get from UserMetered Contour Layer and Load based on feeder delivery with the help of ranking. In Table 4, Load based on feeder delivery and user-metered contour layer are utilized to find out rs and rank accordingly. Rank 1 will be least value in mhw based on User-Metered Contour Layer verses, No. 2 rank will be second least value in mhw and remaining ranks will added accordingly. After that we need analysis the variation of two variables X and Y and then variance should be multiplied. This is known as squaring difference rank or squaring.

(9)

To assess a one dependent variable from one or more Independent variable, Relapse methods are used. The two strong positive direct connections (r2) from X and Y, if r2 is close to point +1. The exact Positive fit means r2 value meets at point +1. The connections between the X and Y is described from the above positive fit values, which indicates that X values increase then simultaneously Y value will decrease. A connection more noteworthy than 0.8 is by and large portrayed as solid, though a relationship under 0.5 is for the most part depicted as powerless. In this direct relapse investigation, r2 is equal to 0.9813227741 indicates X and Y variable linear relationship is determine as the 98% of the total variation in variable X and the remaining 1.867225872% of total variation of variable X is undetermined or Theft load Energy utilized information based on 05July 2015, mentioned in Table 4. In Fig. 9 utilizing SPSS form points are useful to setting up attractive reaction (forecast of variations from the norm zone). Energy utilized data is obtained from connection of X (UserMetered Contour Layer) and Y (Load based on feeder delivery) that form a maximum or minimum ridges of large or small values. In Fig. 9 shows a least energy utilization values and largest energy utilization values form the contour layer, least energy utilization values shown between 235.5240 and 2145.1570 MWh and largest energy utilization values shown between 14557.77 and 15512.5880 MWh and also Fig. 9 shows scattered from User-Metered contour layer and Load based on feeder delivery are indicated as 4000–6000 MWh and 1000– 13000 MWh respectively.

rs=0. 9890 = 98. 9%

In Table 3.6, we find the positive relationship based on the value of spearman’s relationship co-efficient rs=0.9890 = 98.9percent rs value is near to 1, so that the connection is powerful between information of two sets and information of two sets can attached that will go high together. If rsis (minus ) − 0.9890 then one set will go high and other set will go down. 100% minus 98.9% is equal to 1.1% this indicates as unexplained or theft load energy utilized information based on Table 4. 5.3. Detect the theft power with the help of PLC (Power Line Communication)

5.2. Spearmans rank correlation coefficient test The statistical energy value of the singles/monotonic (amount/ capacity) correlation for combined data is obtained from Spearman’s Relationship Co-efficient. An example it is signified by configuration compelled takes after −1≤rs≤1. Spearman’s rank relationship co-efficient (rho or r) is derived as

∑i R (xi ) R ( yi) − n { n

rs=ρ=

0.5

n +1 2

(11)

Electricity theft detection using Power Line Communication (PLC) a narrow band carrier signal is injected into the electric distribution lines along with power frequency signal (230 V, 50 Hz). Thin Band of current Conducting Unit has amplifier that will identify unauthorized consumption of power through fluctuation in the amplifier. MATLAB software is used to find the simulation results. To product the electrical belongings in the home from high electricity and current fluctuation some methods used by Power Line Communication (PLC), that are capacitors coupling and traps in the supply line. Fig. 10 shows the

}2 0.5

{∑in R (xi )2 − n { n 2+1 }2 } {∑in R ( yi)2 − n { n 2+1 }2 }

(10) 583

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values can be selected using the generated frequency produced in the carrier signal. In the distribution line, the combined signal present will be having the amplitude of 250 V with respect to the addition of power frequency voltage of 230 V with the amplitude frequency signal of 20 V. The theft less condition will be considered in Fig. 12. The occurrence of the output carrier signal with high frequency will be caused by the coupling capacitor, and distribution line. The occurred signal amplitude will be under the condition of low and it is used as the reference term for the detecting process of the energy theft. Fig. 13 will be considered as the theft occurrence in the simulation. The consideration is due to the impedance presence in the circuit also the occurrence of the power frequency signal and the high frequency carrier signal will be present. Then, the loss of the power frequency signal will be involved and with respect to that there will not be changes in the electricity usage illegally. If any absence occur in the coordination of the high frequency carrier signal, the main state for causing it will be the impedance with loss of power signal that cause loss in the signal.(Fig. 14). For the simulation process, the RF load is considered. The loading consists of series connection through the distribution line in terms of the power supply. The theft will be detected after the amplitude of the output is compared with reference in terms of the condition. Fig. 15 will be consisting of two theft load of 100 W and 230 V lighting bulbs. Then the distribution line load is made to be resistive. The generated load resistance will be calculated using the below formula, R=V^2/P Ω. R- Load Resistance. V- Voltage drop in the load. P- Consumed power in the load. The principles in the power line communication technique is proves by the simulating process of the results based on the theft and no theft condition and also it can be more effective for the usage of detecting the theft in the electricity in the distributing system of power. Comparing with the existing system, the various benefits can be identified in the PLC power theft detection system.(Fig. 16). The electricity theft can be identified within the range of 250– 500 m, the system used will be providing an efficient and accurate system, when comparing with the existing system. With the use of the simple and the cheap components in the updated system, the implementation can be done easily in the network of the power system. The loss in the electricity can be reduced by the system with respect to the power theft limitation produced by the system. The function of losing the revenue due to the power theft will be leading to the non-development of the countries. So, using this the theft can be reduced and the revenue will be increased accordingly. So, the power quality can be improved in the developing countries.

Table 4 Ranking of the data between customer metered-load and feeder delivered load. Load delivered by the Feeder Y

Y-Rank

15738.950 9145.6280 13182.0 9663.0 8013.060 11099.690 3322.1150 14311.960 10752.650 7222.0 235.0 1398.0 8642.20 8689.980 7850.0 7579.9080 14601.0 7857.3940 3787.380 3060.0 330.0 2526.50 4187.0750 3170.9520 6386.0 2602.080 6815.6340 7901.1980 7826.0 1954.0 4069.6060 6171.520 945.220 4480.0 3064.0 7424.0 2862.880 7579.080 7374.0 590.5250 1979.4950 826.0 6632.6310 5018.340 836.960 366.0780 2823.2420 9821.5640 5014.10 1112.5180

50.0 42.0 47.0 43.0 39.0 46.0 19.0 48.0 45.0 30.0 1.0 9.0 40.0 41.0 36.0 34.0 49.0 37.0 20.0 16.0 2.0 12.0 22.0 18.0 27.0 13.0 29.0 38.0 35.0 10.0 21.0 26.0 7.0 23.0 17.0 32.0 15.0 33.0 31.0 4.0 11.0 5.0 28.0 25.0 6.0 3.0 14.0 44.0 24.0 8.0

CustomerMetered Load

X-Rank

X 15512.5880 9125.340 13055.9660 9676.5520 7941.8340 10480.2870 3326.1430 14304.9440 10768.5450 7162.320 235.5240 1397.0720 8629.9110 8689.8190 7838.2480 7593.7480 14627.4590 7873.7340 3794.1520 3058.9470 331.040 2520.2203 4185.640 3175.2380 6365.7470 2567.60 6815.9703 7889.760 7991.4570 1899.6480 4068.1090 6167.1290 939.1710 4490.0470 3057.5090 7271.70 2848.0205 7529.7104 7313.5902 590.2690 1978.7010 785.0880 5633.9260 5011.4140 839.7840 366.0 2817.5022 9783.6806 5001.0205 1109.430

50.0 42.0 47.0 43.0 38.0 45.0 19.0 48.0 46.0 30.0 1.0 9.0 40.0 41.0 35.0 34.0 49.0 36.0 20.0 17.0 2.0 12.0 22.0 18.0 28.0 13.0 29.0 37.0 39.0 10.0 21.0 27.0 7.0 23.0 16.0 31.0 15.0 33.0 32.0 4.0 11.0 5.0 26.0 25.0 6.0 3.0 14.0 44.0 24.0 8.0

Rank Difference

Square Difference in the Rank

di (Y-X)

di²

0 0 0 0 1 1 0 0 −1 0 0 0 0 0 1 0 0 1 0 −1 0 0 0 0 −1 0 0 1 −4 0 0 −1 0 0 1 1 0 0 −1 0 0 0 2 0 0 0 0 0 0 0

0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 16 0 0 1 0 0 1 1 0 0 1 0 0 0 4 0 0 0 0 0 0 0

∑ di2= 32

6. Conclusion The losses produced in the non-technical will be due to the actions of the external power system. They will be consisting primarily of electricity theft, the loss of $20 billion is estimated in the companies worldwide. The illegal customers are identified by the use of the support of vector machines based on their consumption of the energy. The flowchart is shown for the theft detection with the use of the harmonic generator. The difference between the imports and the exports of the energy can be identified by the technique of linear regression. Table 3 will be showing the electric supply company in the Islamabad for the analyzing of the fraud detection. In the slope the changes will be occurred in terms of the technical and calibration errors. For the analysis of energy consumption, the central observer technique is being used. The load delivered in the feeder section will be correlating the customer meter load and utility supply load. As shown in the slope of 1.007 in Fig. 8 with corresponds to the losses in the

complete construction designs for PLC (Power Line Communication) by using MATLAB, ordinary current fluctuation unit and large fluctuation unit are connect together and the transferred to Supply distribution line by using capacitor coupling and traps for supply line.(Fig. 11). The series of inductor will be present at the input circuit having great frequency supply for the purpose of blocking the signal of high frequency, which affects the source of the supply. For protecting the oscillator circuit, coupling capacitor and series resistor is being connected in the higher frequency side. The protection is made for the supply of the power frequency in it. For providing an impedance matching, line tuner will have the connection of series of resistor. Once the supply is simulated, the addition of two signals will take place and the transmission will be made through distribution line. The carrier signal will be frequency will be at 50 kHZ. The inductor and capacitor

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Fig. 9. User-Metered Contour Layer verses load based on feeder delivery.

Fig. 10. Proposed system circuit diagram without using theft load.

Fig. 11. Input signal simulation with the use of distribution line.

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Fig. 12. Output signal simulation under the less theft condition.

Fig. 13. Output signal simulation under the theft condition with the use of the RL Load.

Fig. 14. Diagram for the proposed system with respect to the RL load.

technical of 0.7%. The analysis of the linear regression and the monthly electricity will be providing a correlation between the X and Y variables. In the other area the value of 1.534735487% in the variation

keeps unchanged or it is theft in the energy consumption. In addition to the system, for detecting and monitoring the electricity theft will be done by power line communication technique.

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Fig. 15. Diagram for the proposed system with respect to the RL load as 2*100–W bulb.

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