A new approach to fault location in three-phase underground distribution system using combination of wavelet analysis with ANN and FLS

Electrical Power and Energy Systems 55 (2014) 261–274

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Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes

A new approach to fault location in three-phase underground distribution system using combination of wavelet analysis with ANN and FLS Ali Rafinia, Jamal Moshtagh ⇑ Department of Electrical Engineering, University of Kurdistan, Sanandaj, Iran

a r t i c l e

i n f o

Article history: Received 20 February 2013 Received in revised form 17 September 2013 Accepted 24 September 2013

Keywords: Fault location Ground faults Underground distribution system Wavelet analysis Neural network Fuzzy logic

a b s t r a c t This paper presents the results of investigation into a new fault classification and location technique, using EMTP software. The simulated data is then analyzed using advanced signal processing technique based on wavelet analysis to extract useful information from the signals. The artificial neural network (ANN) and the fuzzy logic system (FLS) is then used to detect the type and the location of the ground high impedance, ungrounded series, ungrounded and ground shunt faults in a practical underground distribution system (UDS). The results indicate that the fault location technique has an acceptable accuracy (error < 1.5%) under the whole variety of different systems and fault conditions. Therefore, the proposed approach can have high performance for the evaluation of the fault location and classification. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction The distribution system (DS) provides the infrastructure to deliver power from the substations to the loads. These systems have connection among consumers, generation and transmission systems. Typically radial in nature, the distribution system includes feeders and laterals [1]. The distribution voltages in a specific service territory are likely similar because it is easier and more cost effective to stock spare parts when the system voltages are consistent [2]. In recent years, there have been many activities in using fault generated traveling wave methods for fault location and protection [3]. The traveling wave current-based fault location scheme have been developed for permanent faults in underground low voltage DSs in which the distance to fault is determined by the time differences measured at the sending end between an incident wave and the corresponding wave reflected from the fault by Navanithan, Soraghan, Siew, Mcpherson and Gale in [4]. However, due to the limitation of the band width of the conventional CT (up to a few GHz) and VT (up to 50 kHz), the accuracy of fault location provided by such a scheme is not satisfactory for a UDS. Also there have been many activities in using power frequency (low frequency) for fault location and protection. Aggarwal, Aslan and Johns in [5] present a new technique in single-ended fault location ⇑ Corresponding author. Tel.: +98 918 8784368; fax: +98 641 2243271. E-mail address: [email protected] (J. Moshtagh). 0142-0615/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijepes.2013.09.011

for overhead DSs, which is based on the concept of superimposed components of voltages and currents rather than total quantities and also special filtering technique have been utilized to accurately extract the fundamental phasors from the measured fault signals. However, in such techniques which are based on power frequency signals, some useful information associated with high frequencies in transient condition is missed. Paper [6], presents the application of calculated non-linear voltage sag profiles and voltage sag measurement at primary substation to locate a fault in distribution networks. The results indicate the possibility of using this method to support automatic fault management system. Ref. [7] introduces a practical approach to power system fault location in power networks using advanced fault signal processing. The simulation results, including single line to ground faults, faults in mixed feeders and high-impedance arcing faults, confirm the accuracy and practical applicability of the proposed approach. In [8], an accurate and efficient method is proposed for fault section estimation and fault distance calculation in distribution systems, based on frequency spectrum components of fault generated traveling waves. Simulation results of various types of faults on a typical distribution system demonstrate high efficiency and accuracy of the proposed method. In [10], an ANFIS (Adaptive Neural Fuzzy Inference System) based fault classification scheme in neutral non-effectively grounded distribution system is proposed. The results show that it has high accuracy and through simulation, the proposed approach exhibits good performance.

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This paper presents a new off-line method fault location based on signal processing using wavelet and ANNs and FLSs in UDS. A practical 20 kV underground power distribution system is simulated using the EMTP software; the faulted current and voltage responses are then extracted from the sending end for different faults and fault conditions. The effect of transducers (CTs and VTs) and hardware errors such as anti-aliasing filters and quantization are taken into account; the information processed throughout the fault locator algorithm is thus very close to real-life situation. Finally, the simulated data is processed in order to locate the fault point using ANNs and FLSs. 2. Data simulation In order to obtain the voltage and the current signals under different faults and conditions, a practical three-phase UDS shown in Fig. 1 has been considered. In this paper, the simulation of the quantization process is based on 16-bit A/D converter with ±10 V by using MATLAB program. In order to keep the voltage and current signals in range ±10v, these signals are divided by 2200 and 700 respectively which are 1/10 of maximum amount of voltage and current signals under all conditions. It is apparent that both the steady and transient states of the voltage and current signals can be affected by some important parameters such as the type of fault, inception angle, faulted branch and distance to fault for ungrounded and grounded faults. In order to obtain useful information from signals in the signal processing stage and mapping the extracted information to the location of fault in artificial intelligent (AI) stage, it is necessary to obtain voltage and current signals, in different fault types and different conditions in the data simulation stage. In this respect, three types of ground high impedance fault (hi) with electrical arc and

variable resistance including single-phase to ground high impedance fault (3 cases: ag-hi, bg-hi, cg-hi), two-phase to ground high impedance fault (3 cases: abg-hi, acg-hi and bcg-hi), three-phase to high impedance fault (abcg-hi), three types of open-circuit fault (oc) including one-phase open-circuit fault (3 cases: a-oc, b-oc and c-oc), two-phase open-circuit fault (3 cases: ab-oc, ac-oc and bcoc), three-phase open-circuit fault (one case: abc-oc), three types of short-circuit fault (sc) including phase to phase short-circuit fault (3 cases: ab-sc, ac-sc and bc-sc), three-phase short-circuit fault (one case: abc-sc) and three types of grounded short-circuit fault including single phase to ground short-circuit fault (3 cases: ag-sc, bg-sc and cg-sc), two-phase to ground short-circuit fault (3 cases: abg-sc, acg-sc and bcg-sc) and 3-phase to ground short-circuit fault (one cases: abcg-sc) also 3 fault resistance in the case of ground short-circuit fault (0.1 X, 1 X and 10 X), three inception angles (including 90°, 135° and 180°) and 17 distances of fault from recording point (including branch 1:0 m, 100 m, 1300 m, 3850 m, 5100 m, 7900 m, 11,200, 12,900 m; branch 2: 6200 m, 10,200 m; branch 3:8800 m, 9500 m; branch 4:12,000 m, 12,900 m, 13,800 m, 14,800 m and branch 5: 15,500 m, 16,500 m) are simulated. Figs. 2 and 3, show the three-phase voltages and the threephase currents after applying ground high impedance fault on phases b and c, open-circuit fault on phases a, b and c in 5100 m from the source, respectively. These faults are applied in two degrees of 90 and 135 for the system phases. Figs. 4 and 5, show the three-phase voltages and the three-phase currents for the two-phase fault (bcg-sc). As shown in Fig. 4, the initial disturbance in case of fault at 90° is much more than 180°. Fig. 5 shows the three-phase current signals after two-phase to ground high impedance fault event, as the current amplitude in two-phase fault increase significantly. Also the initial current amplitude in the case of fault at 90° is much more than 180°.

Fig. 1. Practical 3-phase underground distribution network.

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Fig. 2. Voltage and current signals, bc-hi fault, L = 5100 m, Inception angle = 90°. Fig. 5. Current signals, bcg-sc fault, Rf = 0.1 X, L = 5100 m, inception angle = 90 and 180°.

Fig. 3. Voltage and current signals, abc-oc fault, L = 5100 m, inception angle = 135°.

Fig. 6. Voltage signals, abc-sc fault, L = 100 m and 5100 m, inception angle = 135°.

distortions are much smaller compared to the voltage signals. Importantly, the currents of the three faulted phases increase after occurring the fault but are much smaller in the case of location at 5100 m to 100 m fault.

3. Feature extraction using wavelet

Fig. 4. Voltage signals, bcg-sc fault, Rf = 0.1X, L = 5100 m, inception angle = 90 and 180°.

Figs. 6 and 7 depict the three-phase voltages and the threephase currents respectively, in the cases of location at 100 m and 5100 m. Fig. 6 shows the voltage signals and it can be seen that the initial distortions are much higher and the transients die down much more slowly in the case of longer distance faults. Fig. 7 shows the current signals and it can be observed that the initial

Transient signal analysis has been extensively used in fault location and condition monitoring of power system lines and cables. The time and frequency information can be calculated using techniques such as Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT) and Wavelet Transform (WT). FFT and STFT techniques yield good information on the frequency content of the transient, but the time at which a particular disturbance in the signal occurred is lost. In this paper, a new approach based on feature extraction using the WT is presented. WT possesses some unique features that make it very suitable for this particular application. It maps a given function from the time domain into time-scale domain. Unlike the basis function used in Fourier analysis, the wavelets are not only localized in frequency but also in time. This localization allows

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decaying and oscillating type of high and low frequency voltage and current signals. One of the most popular mother wavelets suitable for a wide range of applications used is Daubichies’s wavelet. In this respect, db4 wavelet with 8 level of decomposing of signals has been considered herein [13]. Table 1 gives the frequency band information for the different scale of the wavelet analysis, where the sampling frequency is Fs = 100 kHz. There are very useful features in the signals, particularly in the approximate part of the signals, as its frequency band is DC to 195.3125 Hz. Thus in this study, approximate-8 signal is considered for further feature analysis and this pattern is used for all each signal including the faulted and healthy phases.

3.3. Feature extraction using statistical relations

Fig. 7. Current signals, abc-sc fault, L = 100 m and 5100 m, inception angle = 135°.

the detection of the time of occurrence of abrupt disturbances, such as fault transients. 3.1. Wavelet Transform In the case of WT, the analyzing function, which is called wavelets, will adjust their time-widths to their frequency in such a way that higher frequency wavelets will be very narrow and lower frequency ones will be broader. This property of multi-resolution is particularly useful for analyzing fault transients which localize high frequency components superposed on power frequency signals (Manago and Abur [11]). WT of sampled waveforms can be obtained by implementation the discrete WT which is given by the following equation:

 m 1 X  n  ka0 DWTðf ; m; nÞ ¼ pffiffiffiffiffiffi f ðkÞh m a0 am 0 k

ð1Þ

m

where the parameters am 0 and ka0 are scaling and translation constant respectively, k and m being integer variables and h is the wavelet function which may not be real, as assumed in the above equation for simplicity. In a standard discrete WT (DWT), the coefficients are sampled from the continuous WT on a dyadic grid, a0 = 2, yielding a00 ¼ 1, a1 0 ¼ 1=2, etc. Actual implementation of the (DWT) involves successive pairs of high-pass and low-pass filters at each scaling stage of the WT. At each detail, there is a signal appearing at the filter output at the same sample rate F and scaling by two (a0 = 2), Eq. (2) shows the association of each scale 2m with a frequency band containing distinct components of signals. to

Frequency band of scale 2m ¼ F=2mþ2 ! F=2mþ1

ð2Þ

In this paper the original signals have been sampled at 100 kHz and passed through a DWT; thus according to Eq. (2) the frequency band for detailed and approximate signals are; 25 kHz to 50 kHz at detail-1, 12.5 kHz to 25 kHz at detail-2, etc. [12]. 3.2. Choice of mother wavelet Choosing of mother wavelets plays an important role in localizing and depends on a particular application. Researches, in the study of underground power distribution transients are particularly interested in detecting and analyzing short duration, fast

In this paper, the statistical relations were used to obtain a suitable increasing or decreasing model to be used as input for the training of the neural network. The situation in fuzzy logic is also similar to that was employed for neural networks. Therefore, this approach tried to use mathematical concepts and relationships to determine the best performance in finding a suitable fault location and classification. The definitions of some statistical concepts are as follows [14] (The equations were also excluded to avoid being bulky paper):  Mean: The arithmetic mean (typically referred to as the mean) is the most common measure of central tendency. The mean is the only common measure in which all the values play an equal role. The mean serves as a ‘‘balance point’’ in a set of data.  Median: The median is the value that splits a ranked set of data into two equal parts. The median is not affected by extreme values, so you can use the median when extreme values are present. The median is the middle value in a set of data that has been ordered from lowest to highest value.  Mode: The mode of a set of data is the value which occurs most frequently. Like the median and unlike the mean, extreme values do not affect the mode.  Skewness: In probability theory and statistics, skewness is a measure of the extent to which a probability distribution of a real-valued random variable ‘‘leans’’ to one side of the mean.  Correlation coefficients: The coefficient of correlation measures the relative strength of a linear relationship between two numerical variables.  Central moment: In probability theory and statistics, a central moment is a moment of a probability distribution of a random variable about the random variable’s mean; that is, it is the expected value of a specified integer power of the deviation of the random variable from the mean.

3.3.1. Feature extraction in fault classification The process comprises two stages including; (1) Fault classification (2) Fault location [15–17]. At first, the original signals are

Table 1 Different scale of wavelet analysis. Detail

Frequency band

Approximation

Frequency band

D1 D2 D3 D4 D5 D6 D7 D8

25–50 kHz 12.5–25 kHz 6.25–12.5 kHz 3.125–6.25 kHz 1.5625–3.125 kHz 0.78125–1.5625 kHz 390.225–781.25 Hz 195.3125–390.625 Hz

A1 A2 A3 A4 A5 A6 A7 A8

0–25 kHz 0–12.5 kHz 0–6.25 kHz 0–3.125 kHz 0–1.5625 kHz 0–0.78125 kHz 0–390.225 Hz 0–195.3125 Hz

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passed through a DWT then 8-detailed and 1-approximate signals are extracted. With regard to statistics option in wavelet and data processing on approximate signals of the voltage and current phases, it was observed that some useful information can be extracted from standard deviation (STD) of approximate-8 signals in fault classification, since the amount of STD for every input data with dimension 6 (three voltage phases and three current phases) has an obvious relationship with the type of fault and faulted phases. STD equation is as the following equation:

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n u 1 X STDðv Þ ¼ t ðv i  v Þ2 n  1 i¼1

ð3Þ

where vi is the ith sample of signal v, n is the number of samples and is the average of the samples. In order to classify the fault type, both the voltage and current phase signals are employed. Firstly, the original signals are passed through a DWT, then eight detailed and one approximate signals are extracted. Since the number of samples for the original signal is 4001, thus the numbers of coefficients for the decomposed signals are 2004, 1005, 506, 256, 131, 69, 38, 22 and 22 for details at levels 1–8 and approximate at level 8, respectively. After recognizing the type of fault, STD of voltage phases, current phases are used for all types of faults to recognize the faulted phases and whether it is a grounded or ungrounded fault.

v

265

Figs. 8–11 show the STD of voltage phase and current phase signals for the 51 conditions in the case of abg-hi, abc-oc, abc-sc faults and the 153 conditions in the case of ag-sc. Also, these figures show such data which is used in the fault classification associated with the type of fault and faulted phases. Each figure comprises two graph associated with voltage phases and current phases. Each graph shows three waveforms related to the three phases and each waveform depicts the STD of approximate-8 of signal for the all conditions dealt with in the previous section. Also, each waveform contains 3 separate the parts. Each part corresponds to the 17 locations and the same inception angle. As it can be seen, there is a significant difference between the faulted phases and healthy phases. In this stage, the feature of STD has been used because STD of voltage in the faulted phases increases and also STD of current in the faulted phases decreases in each part as the fault distance increases from the measurement point while it is more or less constant for healthy phases. For example, in Fig. 11 although the difference between the maximum STD and minimum STD decreases, in particular the level of STD of the faulted phase approaches those in the healthy phases as Rf increases, there is still a discernable difference in the STD levels between the faulted and healthy phases. 3.3.2. Feature extraction in finding the faulty branch Since the proposed network has five branches, the location of the fault should be considered in two stages;(1) selecting a branch

Fig. 8. STD of approximate-8 signal in the case of abg-hi fault.

Fig. 9. STD of approximate-8 signal in the case of abc-oc fault.

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Fig. 10. STD of approximate-8 signal in the case of abc-sc fault.

which fault has been occurred, (2) locating fault (distance from the source). Thus, three features have been used. These three features are as follows: (1) Ratio of voltage approximate skewness to STD of the current approximate at level 8. (2) Ratio of the square of skewness current approximate to square of skewness voltage approximate at level 8. (3) The absolute value of central moment voltage approximate for the elements of 3 at level 8 (see Fig. 12).

3.3.3.1. Ground high impedance fault (hi). (1) Ratio of voltage approximate variance to current approximate variance at level 8. (2) Ratio of absolute square of voltage approximate mode to absolute square of current approximate mode at level 8. (3) Ratio of central moment of voltage approximate for the elements of 2 to central moment of current approximate for the elements of 2 at level 8.

3.3.3.2. Short-circuit fault (sc). Three features similar to section a. It should be mentioned that these six factors are employed only for the faulted phases in the case of a phase to phase fault, but only phase ‘a’ is considered in the case of three phase fault. In order to obtain more accurate results, the signals are normalized according to the following equation:

X normed ¼

X  X min X max

ð4Þ

3.3.3. Feature extraction to find the fault distance from the source Similar to previous studies, it can be seen that there are very useful features in the decomposed voltage and current signals using the DWT. These features are considered to determine the location of all type of faults and only used faulty phases. These features are:

3.3.3.3. Ground short-circuit fault (g-sc). Three features similar to section a b. 3.3.3.4. Open-circuit fault (oc). (1) Maximum of square of mean correlation coefficients between voltage and current approximate at level 8. (2) STD of absolute the median values for elements along the dimension of current approximate by 2 at level 8. (3) Mean of absolute the median values for elements along the dimension of current approximate by 2 at level 8. (4) Square of central moment of voltage approximate for the elements of 2 at level 8. Figs. 13–15 show the results for 51 fault conditions, associated with three-phase to ground, ungrounded three-phase and phase-a

Fig. 11. STD of approximate-8 signal in the case of ag-sc fault.

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Fig. 12. Three parameters used in the finding faulty branch stage.

to ground faults. Figs. 13 and 14 depict three graphs according to 51 fault conditions associated with 17 locations and three inception angles, solely for ‘a’ phase in the case of the abcg-hi and a-sc faults. The reason for only selecting phase ‘a’ is the similarity between the three phases in terms of the considered parameters. Fig. 15 shows four features in the case of abc-oc. Each figure shows three graphs themed by the three aforementioned parameters. As mentioned before, each 17 consecutive conditions are based on the same inception angle but different locations. Also Fig. 16 depicts the behavior of three features for the faulted phase ‘a’ to ground according to the aforementioned 153 fault conditions. All graphs comprise 9 parts related to three inception angles and three values of Rf and each part shows their behavior depending on the 17 fault locations. Each three consecutive parts correspond to the same Rf but a different inception angle. For single and two-phase fault the same process is used. It should be considered that for two-phase fault the features are related to the two phases.

4. Artificial intelligence techniques 4.1. Artificial neural network ANNs have emerged as a powerful pattern recognition technique and act on data by detecting some form of underlying organization not explicitly given or even known by human experts and it possesses certain features which are not attainable by the conventional methods. In this respect, this paper describes a new method for accurate fault location based on the ANNs technique. The successful development of ANNs approaches depends on the successful learning of the correct relationship or mapping between the input and output patterns by the ANNs [19–20]. In order to achieve this, practical issues surrounding the design and testing of an ANN such as the best network size, generalizing versus memorization feature extraction and scaling of signals have been addressed and examined.

Fig. 13. Three parameters used in finding the distance of fault stage in the case of abcg-hi fault.

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Fig. 14. Three parameters used in finding the distance of fault stage in the case of a-sc fault.

Fig. 15. Four parameters used in finding the distance of fault stage in the case of abc-oc fault.

Fig. 16. Three parameters used in finding the distance of fault stage in the case of ag-sc fault.

There are many types of ANNs but the most commonly used are the multi-layer feed-forward networks, as, a three-layer network (input, one hidden and output layers). Because of this, a fully con-

nected three-layer feed-forward ANNs with Levenberg–Marquardt (LM) learning algorithm has been used in the complete fault classification and fault location networks.

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Table 2 depicts the specifications of employed ANNs in proposed fault location technique. Where N1 = number of training data, N2 = dimension of input layer, N3 = number of neuron in hidden layer, N4 = number of neuron in output layer, F1(x) = transfer function in hidden layer and F2(x) = transfer function in output layer. The NNtf recognizes the type of fault and faulted phase’s ungrounded and grounded faults. The NNbf represents the branch of fault. The NNag-hi and the other similar architectures mentioned in Table 2 determine the distance of fault from the source in the case of HI, SC and OC fault.

4.2. Fuzzy logic FLS is a convenient way to map an input space to an output space with a set of common-sense rules. In almost every case, FLS can be replaced by other options, such as linear, non-linear, Neural-Network, expert system and genetic algorithm, but FLS is faster and more adaptable. Furthermore, it is conceptually easy to understand as well as is flexible, tolerant to imprecise data and is based on natural language. Also it can be built on the experience of experts and can be blended with conventional control techniques. Because of previously mentioned points, in this paper, FLS has been selected to localize and identify different types of fault. Different steps are needed in the practical design of FLS in power engineering application [22]. Table 3 shows the specifications of employed FLSs in proposed fault location technique. Where M1 = number of input data, M2 = number of input MF, M4 = number of output MF, M5 = number of output variables, G1(x) = input MFs and G2(x) = input MFs. The FLtf recognizes the type of fault and faulted phase’s ungrounded and grounded faults. The FLbf represents the branch of fault. The FLag-hi and the other similar architectures determine the distance of fault from the source in the case of HI, SC and OC fault.

Table 2 Specifications of employed ANNs. ANN

N1

N2

N3

N4

F1(x)

F2(x)

NNtf NNbf NNag-hi NNbg-hi NNcg-hi NNabg-hi NNacg-hi NNbcg-hi NNabcg-hi NNa-oc NNb-oc NNc-oc NNab-oc NNac-oc NNbc-oc NNabc-oc NNa-sc NNb-sc NNc-sc NNab-sc NNac-sc NNbc-sc NNabc-sc NNag-sc NNbg-sc NNcg-sc NNabg-sc NNacg-sc NNbcg-sc NNabcg-sc

2142 50 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 153 153 153 153 153 153 153

9 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3

6 10 8 8 8 8 8 8 8 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8 8 8 8

7 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig

Linear Linear TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig TanSig

Table 3 Specifications of employed FLSs. FLS

M1

M2

M3

M4

G1(x)

G2(x)

FLtf FLbf FLag-hi FLbg-hi FLcg-hi FLabg-hi FLacg-hi FLbcg-hi FLabcg-hi FLa-oc FLb-oc FLc-oc FLab-oc FLac-oc FLbc-oc FLabc-oc FLa-sc FLb-sc FLc-sc FLab-sc FLac-sc FLbc-sc FLabc-sc FLag-sc FLbg-sc FLcg-sc FLabg-sc FLacg-sc FLbcg-sc FLabcg-sc

2142 50 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 153 153 153 153 153 153 153

12,852 150 153 153 153 153 153 153 153 204 204 204 204 204 204 204 153 153 153 153 153 153 153 459 459 459 459 459 459 459

7 5 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17

7 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

trimf trimf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf

trimf trimf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf trapmf

In order to find the best topology for accurate fault location under all practically encountered different system and fault conditions, an extensive series of studies have revealed that it is not satisfactory to merely employ a single ANN or FLS and attempt to train and design them with a large amount of data. A much better approach is to separate the problem into three parts: 1. To employ FLSs and train some ANNs to classify the faults, as they indicate on which type of fault (open circuit, short circuit or high impedance) and which phase(s) the fault is and whether there is ground involved in a particular fault, irrespective of the actual fault location at this stage; 2. In order to achieve a good generalization, to use separately trained ANNs and designed FLSs (one for each type of fault and faulty phase(s)) to accurately locate the actual fault branch and 3. Using some other features, the distance of the fault from the source is measured on the underground distribution system. Fig. 17 show the fault location scheme based on ANNs and FLSs. Inputs to the neural network and the fuzzy logic for fault classification, faulty branch location and the fault distance from the source are given in Sections 3.3.1, 3.3.2 and 3.3.3 respectively.

4.3. Fault classification based on ANNs and FLSs The fault type classification technique is based on training three-layer ANNs by the LM learning algorithm and also after an extensive series of studies, hyperbolic tangent and linear transfer functions were selected as the hidden and the output layer neurons, respectively. Table 4 shows desired ANN and FLS outputs for the fault type classification (Single-phase, two-phases and three-phases). The outputs of the ANN and FLS comprise of seven variables HI, SC, OC, A, B, C and G; of these, HI, SC and OC is associated with the type of fault (a value close to unity indicates the type of fault), a value close to unity for any of the A, B and C corresponds to the appropriate a, b or c phases being faulty, respectively. A near unity of G signifies that ground is involved in a fault.

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They are all driven from the ANNs and FLSs designed to classify the fault type and the input data is generated the same way as it is mentioned in detail in previous two sections. ANNs (or FLSs) are trained (or designed) with different training data to cater for all types of commonly encountered faults.

V,I Feature Extraction

Fault Classification 5. Analysis of test results Ground High Impedance Fault abcghi

abg -hi

aghi

bcg -hi acg -hi

bghi cghi

Open-Circuit Fault

abcoc

Ground Short-Circuit Fault

Short-Circuit Fault

aboc

aoc

bcoc acoc

boc coc

abcsc

absc

abcgsc

bcsc acsc

Fault Branch

abg -sc

agsc

bcg -sc acg -sc

bgsc cgsc

Fault Dictance

Fault Location Fig. 17. Schematic diagram of fault location technique.

4.4. Finding the fault branch based on ANNs and FLSs There are five branches in the network with different length; accordingly, the proper features are extracted for ANN and FLS input. Three features that pointed in Section 3 are employed as input of the ANN and FLS. The five outputs represented in Table 5 shows the faulted branch. In this table a value close to unity indicates the branch of fault. 4.5. Fault distance from the source based on ANNs and FLSs As mentioned before, separate ANNs and FLSs are designed to accurately calculate fault distance from the source for each type of fault under all practically encountered different fault conditions. Table 4 Desired ANNs and FLSs outputs for fault classification. Type fault

HI

OC

SC

A

B

C

G

ag-hi bg-hi cg-hi abg-hi acg-hi bcg-hi abcg-hi a-oc b-oc c-oc ab-oc ac-oc bc-oc abc-oc a-sc b-sc c-sc ab-sc ac-sc bc-sc abc-sc ag-sc bg-sc cg-sc abg-sc acg-sc bcg-sc abcg-sc

1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 0 0 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 1

0 1 0 1 0 1 1 0 1 0 1 0 1 1 0 1 0 1 0 1 1 0 1 0 1 0 1 1

0 0 1 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1 1 1

1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1

In order to analyze the accuracy of the proposed method, two groups of test data (group-1 for ground faults and group-2 for ungrounded faults) which are different from that was used for training of ANN and input of FLS have been selected. Then, the sensitivity of the method to the fault and system parameters such as: (1) Fault location (100 m, 1600 m, 1925 m, 4475 m, 5650 m, 6500 m, 8200 m, 8350 m, 9150 m, 9550 m, 9950 m, 12,050 m, 13,750 m, 14,300 m and 16,000 m), (2) Branch (1, 2, 3, 4 and 5), 3. Fault angle (62 deg, 117 deg and 158 deg) and 4. Fault resistance (only for ground short-circuit fault including 0.5 X, 5 X and 15 X) with adding of distributed generation (DG), Load variation including load shedding, load increasing and load unbalancing was evaluated. Table 6 shows the various fault conditions employed during the test procedure. In order to research and investigate any fault location/protection technique, it is vitally important to employ data simulation under a whole variety of different systems and fault conditions, for the development of the new technique. This paper gives a summary of all the different systems and fault conditions studies for generating the requisite data and the proposed method that is able to detect any type of fault. The idea is to include all fault data and problems of the distribution system in fuzzy logic and neural networks as inputs. Therefore, whenever a fault occurred in the new distance, the location of the fault could be determined. Each of these features was generated from the combination of several statistical concepts that would change with increasing distance from the source of the fault and also the changing of the fault type. Fig. 18, shows basic configuration of the ANN and FL based fault location technique for analysis of test results. It should be noted that neural networks and fuzzy logic are not in interaction and each of them can solve the fault location issues separately. Therefore, the neural network obtained better performance in finding the fault distance from the source as compared to fuzzy logic. The fuzzy logic also shows its advantage in classification and location of the branch fault. But in general, the neural networks seems to be more efficient than fuzzy logic due to their accurate determination of the fault location and also the designation of neural network is more easier than fuzzy inference systems (with increasing membership functions, the designation of fuzzy inference systems are too complex and time consuming). 5.1. Performance of fault classification and fault branch finding In order to quantitatively evaluate the performance of the fault classification technique, the NNtf was test by four aforementioned test data including over 46 system and fault conditions. It is evident from the results that the number of error decision was zero

Table 5 Desired ANN and FLS outputs for faulty branch. Branch number

1

2

3

4

5

Branch1 Branch2 Branch3 Branch4 Branch5

1 0 0 0 0

0 1 0 0 0

0 0 1 0 0

0 0 0 1 0

0 0 0 0 1

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characteristics and the types of faults that occur in power system is presented. The combination of signal processing and intelligent methods has been used for fault classification and location. The novelty of this research was using new statistical features and fault detection method compared to previous works. All possible simulations also have been performed. Initially, the radial network with multiple branches is considered and the type of fault, the faulty branch and the fault distance from the source have been calculated. Then, the radial network has been converted to a ring network with the addition of distributed generation (DG). In these conditions both of the proposed methods can accurately determine the type of fault, the faulty branch and the fault distance from the source and the proposed network is also considered as a real network.

Fig. 18. Basic configuration of the ANN and FL based fault location technique for analysis of test results.

in relation to the NNtf; therefore it can be concluded that this method is indeed to classify the type of fault and branch of fault and recognizes the faulted phases and have operated with accuracy more than 99%. 5.2. Performance of fault location In order to analyze the accuracy of the proposed method, the data used for testing is different and unseen from that used for training. The trained ANNs and the designed FLSs involved in the third stage of the fault location and classification technique were tested by five aforementioned groups of test data. The error for fault location is expressed as a percentage of the length of the cable, and is given as the following equation:

error% ¼

ðactual locationÞ  ðdesired locationÞ  100 ðcable lengthÞ

ð5Þ

Tables 7–10 show the ANN and the FLS performance for the tests carried out on a system in case of high impedance faults, short-circuits faults and open-circuit faults, respectively. Of course, the results related to some faults are shown and other results are excluded for brief. In this paper, a comprehensive solution for fault location in distribution networks with respect to various parameters,

5.2.1. Effect of load shedding with the data of groups 1&2 on accuracy fault location based on ANNs and FLSs The load shedding significantly affects the fault transient voltage and current signals and it is vitally important to verify the effect of this parameter on the performance of the proposed technique. Table 7 show the accuracies attained for groups-1&2 of test results. It is clearly evident from the results that the ANNs and FLSs give very accurate evaluation of fault position and the maximum and mean of error correspond to the g-sc, sc, oc and hi faults are for ANN: (0.8 & 0.346), (0.78 & 0.283), (1.01 & 0.812) and (0.512 & 0.225) percent and for FLS: (0.7 & 0.369), (0.8 & 0.292), (1.21 & 0.819) and (0.61 & 0.264) percent, respectively. This study clearly demonstrates that the algorithm is virtually immune to any errors caused by either the load shedding. 5.2.2. Effect of load increasing with the data of groups 1&2 on accuracy fault location based on ANNs and FLSs It is well known that load increasing can adversely affect the accuracy of conventional fault locators. In this respect, the algorithm was tested based on group-1&2 of test data. Table 8 depicts the results. In comparison to the previous case associated, it is evident that the presence of load increasing has only a slight effect on accuracy particularly in the case of one phase, phase to phase fault and three phase, as the maximum and the mean of error associated with four faults increased very slightly to (0.604 & 0.211), (0.697 & 0.287), (1.1 & 0.841) and (0.73 & 0.259) percent for ANN and (0.64 & 0.246), (0.69 & 0.293), (1.2 & 0.851) and (0.7 & 0.294) percent for FLS respectively. These small changes can be directly attributed to the fact that with load increasing, the current changes in the healthy phase in terms of magnitude and distortion.

Table 6 Testing data for fault location based on ANN and FLS. No. TDa

TD-1 TD-2 TD-3 TD-4 TD-5 TD-6 TD-7 TD-8 TD-9 TD-10 TD-11 TD-12 TD-13 TD-14 TD-15 a

Group-1 (g-sc fault)

Group-2 (hi, scoc) fault)

Location (m)

H (deg)

Rf (X)

Branch

Location (m)

H (deg)

Branch

750 1600 1925 4475 5650 6500 8200 8350 9150 9550 9950 12,050 13,750 14,300 16,000

62 117 158 62 117 158 62 117 158 62 117 158 62 117 158

0.5 5 15 0.5 5 15 0.5 5 15 0.5 5 15 0.5 5 15

1 1 1 1 12 12 1,2,3,4 1,2,3,4 1,2,3,4 1,2,4 1,2,4 1,4,5 4,5 4,5 5

750 1600 1925 4475 5650 6500 8200 8350 9150 9550 9950 12,050 13,750 14,300 16,000

62 117 158 62 117 158 62 117 158 62 117 158 62 117 158

1 1 1 1 12 12 1,2,3,4 1,2,3,4 1,2,3,4 1,2,4 1,2,4 1,4,5 4,5 4,5 5

Number of Test Data (TD).

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A. Rafinia, J. Moshtagh / Electrical Power and Energy Systems 55 (2014) 261–274

Table 7 Effect of load shedding with the data of groups 1&2 on accuracy fault location based on ANNs and FLSs. No. TD

a b

Group-1, G-SC Ma. error%

Group-2, SC M. error%

Group-2, OC M. error%

Group-2, HI M. error%

ANN

FLS

ANN

FLS

ANN

FLS

ANN

FLS

TD-1 TD-2 TD-3 TD-4 TD-5 TD-6 TD-7 TD-8 TD-9 TD-10 TD-11 TD-12 TD-13 TD-14 TD-15

0.55 0.8 0.25 0.25 0.65 0.05 0.525 0.04 0.02 0.115 0.492 0.222 0.625 0.325 0.275

0.46 0.63 0.35 0.45 0.7 0.3 0.08 0.42 0.04 0.149 0.42 0.42 0.62 0.25 0.25

0.002 0.7 0.42 0.072 0.387 0.337 0.475 0.025 0.22 0.1 0.0 0.35 0.78 0.23 0.145

0.01 0.67 0.32 0.07 0.37 0.37 0.75 0.05 0.2 0.11 0.01 0.3 0.8 0.2 0.15

0.8 0.81 0.55 0.905 0.546 0.69 0.89 0.77 0.78 0.63 1.01 0.97 1.0 0.955 0.876

0.88 0.91 0.5 0.95 0.46 0.9 0.8 0.7 0.8 0.3 1.21 1.07 1.0 0.95 0.86

0.02 0.025 0.23 0.44 0.11 0.13 0.05 0.45 0.237 0.266 0.361 0.09 0.512 0.221 0.24

0.05 0.05 0.3 0.4 0.1 0.3 0.09 0.4 0.27 0.26 0.61 0.01 0.52 0.21 0.4

TMb.error%

0.346

0.369

0.283

0.292

0.812

0.819

0.225

0.264

Mean of errors. Total mean of errors.

Table 8 Effect of load increasing with the data of groups 1&2 on accuracy fault location based on ANNs and FLSs. No. TD

Group-1, G-SC M. error%

Group-2, SC M. error%

Group-2, OC M. error%

Group-2, HI M. error%

ANN

FLS

ANN

FLS

ANN

FLS

ANN

FLS

TD-1 TD-2 TD-3 TD-4 TD-5 TD-6 TD-7 TD-8 TD-9 TD-10 TD-11 TD-12 TD-13 TD-14 TD-15

0.1 0.015 0.147 0.286 0.132 0.08 0.512 0.124 0.1 0.2 0.604 0.07 0.154 0.235 0.412

0.11 0.05 0.17 0.26 0.32 0.2 0.52 0.14 0.11 0.22 0.64 0.03 0.15 0.25 0.52

0.06 0.2 0.01 0.243 0.295 0.028 0.567 0.555 0.27 0.06 0.423 0.697 0.137 0.387 0.243

0.2 0.1 0.03 0.23 0.25 0.07 0.56 0.5 0.41 0.16 0.43 0.69 0.17 0.37 0.23

0.75 0.65 1.1 0.87 0.555 0.45 0.9 0.7 0.945 0.98 1.04 0.96 0.89 0.95 0.88

0.5 0.5 1.0 0.8 0.94 0.98 0.8 0.8 0.95 0.9 1.2 0.8 0.9 0.9 0.8

0.07 0.12 0.31 0.05 0.02 0.11 0.34 0.45 0.73 0.25 0.24 0.01 0.524 0.261 0.401

0.17 0.2 0.3 0.08 0.2 0.01 0.4 0.5 0.7 0.6 0.2 0.01 0.54 0.2 0.31

TM. error%

0.211

0.246

0.278

0.293

0.841

0.851

0.259

0.294

Table 9 Effect of load unbalancing with the data of groups 1&2 on accuracy fault location based on ANNs and FLSs. No. TD

Group-1, G-SC M. error%

Group-2, SC M. error%

Group-2, OC M. error%

Group-2, HI M. error%

ANN

FLS

ANN

FLS

ANN

FLS

ANN

FLS

TD-1 TD-2 TD-3 TD-4 TD-5 TD-6 TD-7 TD-8 TD-9 TD-10 TD-11 TD-12 TD-13 TD-14 TD-15

0.12 0.41 0.02 0.55 0.244 0.323 0.13 0.105 0.542 0.47 0.29 0.34 0.623 0.423 0.22

0.2 0.4 0.3 0.5 0.44 0.323 0.21 0.45 0.32 0.27 0.2 0.4 0.61 0.23 0.3

0.04 0.02 0.101 0.4 0.3 0.01 0.33 0.41 0.28 0.66 0.31 0.44 0.651 0.39 0.34

0.4 0.3 0.01 0.1 0.6 0.1 0.3 0.4 0.4 0.56 0.3 0.4 0.51 0.3 0.5

1.0 0.88 0.84 0.98 0.7 0.97 1.2 0.86 0.81 0.95 0.69 0.86 0.59 0.94 0.9

0.91 0.8 0.8 0.9 0.7 0.7 0.92 1.31 1.02 0.9 0.75 0.8 0.9 0.9 0.93

0.1 0.2 0.1 0.33 0.291 0.268 0.01 0.57 0.286 0.365 0.563 0.52 0.104 0.202 0.11

0.3 0.1 0.2 0.55 0.21 0.28 0.51 0.57 0.23 0.65 0.53 0.5 0.04 0.02 0.1

TM. error%

0.32

0.343

0.312

0.345

0.878

0.882

0.268

0.319

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A. Rafinia, J. Moshtagh / Electrical Power and Energy Systems 55 (2014) 261–274 Table 10 Effect of DG with the data of Groups 1&2 on accuracy fault location based on ANNs and FLSs. No. TD

Group-1, G-SC M. error%

Group-2, SC M. error%

ANN

FLS

ANN

TD-1 TD-2 TD-3 TD-4 TD-5 TD-6 TD-7 TD-8 TD-9 TD-10 TD-11 TD-12 TD-13 TD-14 TD-15

0.56 0.63 1.41 0.95 1.2 1.1 0.65 0.58 0.66 0.85 0.69 0.91 0.54 0.75 0.49

0.91 1.05 0.67 1.21 0.92 0.72 0.83 0.4 0.61 1.9 0.64 0.39 0.59 1.1 0.9

0.68 1.2 0.91 0.93 0.95 0.68 0.57 0.5 1.25 0.6 0.43 0.678 0.73 1.86 0.94

TM. error%

0.798

0.856

0.86

5.2.3. Effect of load unbalancing with the data of groups 1&2 on accuracy fault location based on ANNs and FLSs It is apparent that load unbalancing significantly affect the fault transient waveforms. Therefore, it is vitally important to verify the effect of the load unbalancing on the performance of the proposed technique. In these respect groups 1&2 are considered and Table 9 depicts the results. It is clearly evident from the results that the accuracy achieved in fault location is very high; being less than 1% error in all the test cases. 5.2.4. Effect of DG with the data of groups 1&2 on accuracy fault location based on ANNs and FLSs Integration of a DG into an existing distribution system has many impacts on the system, with the power system protection being one of the major issues. Short circuit power of a distribution system changes when its state changes [23]. Thus, adding DG can adversely affect the accuracy of conventional fault locators. Therefore, with adding of DG-4MW at the end of branch-5, the ANN and FLS algorithm were tested based on group-1&2 of test data and Table 10 show the results. The results clearly demonstrate that the accuracy achieved in fault location is very high; being less than 1.5% error in all the test cases and shows that the ANNs give accurate evaluation of fault position that is largely independent on the DG adding. Thus it can be concluded that the ANNs and FLSs give accurate assessment significantly independent to the load variation, DG, inception angle and distance. 6. Conclusion In this paper at first, a new method is proposed to analyze power distribution system transient signals based-EMTP by using WT technique. This method offers important advantages over other methods such as FFT and STFT due to good time and frequency localization characteristics. Analysis presented results clearly show that particular wavelet components can be used as the features to locate the fault in UDS. Then an accurate fault location technique based on ANN and FLS is developed, as ANNs and FLSs are trained and designed to classify the fault type and separate ANNs and FLSs are designed to accurately locate the actual ungrounded fault position on a practical UDS. In this respect, three-layer feed-forward ANNs and the LM algorithm is used to adopt the weights and biases to achieve the desired non-linear mapping from inputs to outputs.

Group-2, OC M. error% FLS 0.72 0.93 0.79 0.66 0.44 0.94 1.6 0.5 0.81 0.86 0.83 0.9 0.7 0.67 0.83 0812

Group-2, HI M. error%

ANN

FLS

ANN

FLS

1.75 1.65 1.4 1.7 1.5 1.4 1.9 0.9 2.1 1.98 1.04 0.86 0.9 1.5 1.3

1.9 0.95 1.33 0.9 1.4 1.8 2.05 1.8 0.9 1.8 1.7 1.3 1.2 1.6 0.8

0.87 0.82 0.91 0.95 0.62 0.71 0.74 0.95 0.79 0.85 0.84 1.1 1.5 1.2 0.9

1.1 0.8 1.3 0.68 0.72 0.97 0.8 0.7 0.8 0.94 0.98 1.01 0.89 0.81 0.71

1.46

1.43

0.916

0.88

Through a series of test and modifications, it is evident that the ANNs and FLSs can classify the type of fault very accurately under different system and fault conditions. In order to illustrate the effectiveness of fault location based on ANNs and FLSs technique, each ANN and FLS is tested with a separate set of unseen data and their performance on the accuracy of the results are presented. The presented results herein, clearly show that the proposed method gives a high accuracy in fault location under a whole variety of different system and fault condition. Finally, it must be concluded that both methods have advantages and disadvantages that should be considered. Neural networks are fast and easy to run which allows the designer a more rapid designation to attain the target. The main disadvantage of neural networks is finding the appropriate features which can guide the neural networks to understanding the relationship between the input and the desired output seems to be very difficult in some conditions. On the other hand, in fuzzy logic not only the inputs can be designed by user in the best possible way but also the membership functions can be adjusted with more careful planning and manipulation to find the gaps of the individual membership function in appropriateness. One of the disadvantages of fuzzy logic method compared to neural network is in designation technique, the fuzzy systems; generally have lower degrees of desirability due to their large number of inputs and membership functions, as compared to the neural networks. This method is responsible for various topological configurations and operating conditions. It is enough to coordinate the neural networks and fuzzy logic according to the distributed network topology. The results clearly show that the neural network is more efficient than fuzzy logic. In the results of section, two combined methods including wavelet-ANN and wavelet-FLS have been compared under various fault and system conditions and their performance has been evaluated to determine the type and location of the faults. The proposed methods recommend the use of an ANN-FLS for the determination of fault types and locations. Therefore, the best method is to use a combination of wavelets, neural networks and fuzzy logic methods. In cases of more complexity and data abundance, the designer can use neural networks instead of fuzzy logic and whenever the artificial neural network fails to identify the relations between inputs and outputs, the fuzzy logic method can be used instead to solve the problem. Thus it can be concluded that the proposed approach based on combined WT, ANN and FLS is robust to different case studies; this

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is a signification advantage and can be directly attributed to the fact that WT technique effectively extracts the very crucial time– frequency features from UDS transient signals and ANN and FLS approach are able to give a very high accuracy in the fault classification and fault location. In order to avoid further economic and social costs because of load interruptions, the fault diagnosis has to be concluded as soon as possible. Intelligent systems have been successful in dealing with fault diagnosis problems [24]. Acknowledgements The authors would like to appreciate Reza Bashiri Khuzestani for editing the article. Appendix A. Various Elements The specifications of the various elements in Fig. 1 are as follows: Source: VL = 20 kV, f = 50Hz, Xs/Rs = 30, Xs = 6 X, Rs = 0.2 X Cable: XLPE, Three-phase pipe type cable (core + grounded sheath), cable length = 16,500 m Transformers: Load 2: S = 1 MVA Winding1: VL = 20 kV, Rp = 8.1 X, Lp = 112 mH Winding2: VL = 0.4 kV, Rp = 0.0032 X, Lp = 0.045 mH Loads 1, 3, 4, 5, 6, 7, 8, 12 and 15: S = 0.8 MVA Winding1: VL = 20 kV, Rp = 10.3125 X, Lp = 139.43 mH Winding2: VL = 0.4 kV, Rp = 0.00413 X, Lp = 0.0557 mH Loads 9, 10, 11, 13, 14 and 16: S = 0.5 MVA Winding1: VL = 20 kV, Rp = 18.72 X, Lp = 221.31 mH Winding2: VL = 0.4 kV, Rp = 0.0075 X, Lp = 0.0885 mH Load1: VL = 0.38 kV, f = 50 Hz, PL = 440KW, QL = 324 KVAR Load2: The combination of three-phase static and dynamic loads Static Load: VL = 0.38 kV, f = 50Hz, PL = 490 kW, QL = 360 KVAR Dynamic Load: VL = 0.38 kV, f = 50 Hz, P = 200 HP Load3: VL = 0.38 kV, f = 50Hz, PL = 398KW, QL = 298 KVAR Load4: VL = 0.38 kV, f = 50 Hz, PL = 390KW, QL = 288 KVAR Load5: VL = 0.38 kV, f = 50 Hz, PL = 388KW, QL = 286 KVAR Load6: VL = 0.38 kV, f = 50 Hz, PL = 380KW, QL = 280 KVAR Load7: VL = 0.38 kV, f = 50 Hz, PL = 242KW, QL = 178 KVAR Load8: VL = 0.38 kV, f = 50 Hz, PL = 220KW, QL = 164 KVAR Load9: VL = 0.38 kV, f = 50 Hz, PL = 280KW, QL = 210 KVAR Load10: VL = 0.38 kV, f = 50 Hz, PL = 274KW, QL = 204 KVAR Load11: VL = 0.38 kV, f = 50 Hz, PL = 262KW, QL = 200 KVAR Load12: VL = 0.38 kV, f = 50 Hz, PL = 260KW, QL = 190 KVAR Load13: VL = 0.38 kV, f = 50 Hz, PL = 240KW, QL = 180 KVAR Load14: VL = 0.38 kV, f = 50 Hz, PL = 200KW, QL = 150 KVAR Load15: VL = 0.38 kV, f = 50 Hz, PL = 175KW, QL = 130 KVAR Load16: VL = 0.38 kV, f = 50 Hz, PL = 142KW, QL = 68 KVAR

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