Intelligent Engineering Systems: ISSN 1472-8915 Volume 21 Number 1 March 2013
ANFIS Approach for Locating Precise Fault Points in Distribution System Rasli Abd Ghani, Azah Mohamed, Hussain Shareef, Hadi Zayandehroodi Department of Electrical, Electronic and Systems Engineering Universiti Kebangsaan Malaysia, Bangi Selangor, Malaysia 43600 Phone: +603 89216006/ email:
[email protected]
Abstract – Technology development in supplying electrical power should be in parallel with advancement of computer technologies. With these advancements in technologies, reliable and quality of energy to customers can be enhanced by classifying the fault types and determining the fault location precisely within few seconds. Without precise and fast fault location, power restoration plan will take longer time and surely loose customers profit and confidence towards the utilities. This paper presents an intelligent technique for locating fault points in term of their geometrical coordinates in a practical power distribution system. The proposed technique implements an ANFIS approach by using parallel ANFIS blocks for each selected fault types and fault points. Then, the designed ANFIS is presented as a GUI tool to display fault points in X-Y coordinates whenever the post-fault three-phase root-mean-square (RMS) currents is given as the input. The technique produces satisfactory results with an average percentage error of 1.193E-5% and 0.0214% for X and Y coordinates prediction, respectively. The proposed technique is validated through simulations using the commercial software package PSSADEPT. The results show that designed parallel ANFIS approach is fast and effective in determining precise fault location in a practical distribution system. Keyword – Fault points, ANFIS, coordinate geometry, practical distribution system, GUI tool 1.0 Introduction Level of quality and reliability of power supply system are measured according to the standards index such as customer average interruption duration index (CAIDI) and, therefore the power utilities are encouraged to keep a lower CAIDI index. One of the important factors to realize a lower CAIDI is by determining precise and fast fault location in a distribution system. For this reason, the power utilities are urged to implement an effective and stable fault location system. Previously various fault location methods have been proposed and they can be categorized as analytical methods, artificial intelligence (AI) based methods, travelling wave methods and software based methods. An analytical method was developed in [1] to recognize the fault section and malfunction of devices in a distribution system by combining model data and alarm data. It is shown that this method provide precise and fast solution. However, accurate alarm data for the distribution network is difficult to obtain in most of the cases. Similar, mathematical method based on impedance calculation to estimate fault location in a transmission line was presented in [2]. The technique only used post-fault phase magnitude current to calculate the estimation with satisfactory results but this method is not applicable to the distribution system due to asymmetrical network. Another analytical method based on an iterative algorithm was introduced in [3, 4] to identify fault location for all types of faults and single phase to ground fault, respectively. Using the algorithm and input data, the method defined the fault locations in meters from the feeding substation. However, the efficiency of the method was satisfactory only for a small distribution system with maximum span of 3.4 km. Also a method was suggested based on impedance calculated to determine fault location in meter and kilometer distances from feeding substation as presented in [5, 6, 7]. The authors introduced the method to calculate the fault distance in between two substations by considering voltage and current parameters on both sides. This method is not usable in the distribution
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system because the system does not have voltage and current measurements on both sides of feeding and ending substation. In [8], the Clarke-concordia transformation was used in analyzing a formal language dataset of a current signal. This approach gives fault location error of less than 0.6% in a test conducted on a very small radial distribution network. For fault location in practical distribution networks, impedance calculated from the recorded voltage and current data was used in [9]. However, this method is inaccurate for locating single phase faults only. The direct overcurrent values recorded by a feeder terminal unit (FTU) were utilized to develop a matrix algorithm [10]. The developed method is able to locate both single and multiple faults in fault section but it requires FTUs to be installed on the network, thus it incurs high costs. Likewise in [11], fault section was estimated based on current and voltage waveforms measured during fault events. The results show good performance on reducing the multiple estimation problems. In the artificial intelligence (AI) category, many methods were proposed to define precise fault location in power system. For example, adaptive neuro-fuzzy inference system (ANFIS) approach was used to define fault location in a transmission line. Wavelet signal with and without power swing were trained to predict km distance from feeding substation [12]. Same ANFIS approach with series-parallel design was used in [13] to produce precise fault points in terms of X-Y coordinates by using only post-fault three-phase RMS fault current at distribution network substation. The designed method relies on the trained ANFIS blocks for the simulated distribution network. Currently, fault location studies also consider the presence of distributed generator (DG) in the power network. In [14, 15], multilayer perceptrons neural network (MLPNN) was applied to determine fault location and to classify fault types by considering single and multiple DGs in the distribution network. This technique determined the fault location in km distance from feeding substation and the highest error for the trained neural network (NN) was found to be about 20 meters. Meanwhile, [16, 17] presented a support vector machine (SVM) approach to classify the fault location in zones or areas in distribution and transmission systems. However, according to the presented results, the SVM method provides imprecise fault point and requests several parameters to implement and detecting the faults including fault resistance, fault inception angle and fault distance. For the same purpose, [18] proposed a minimal radial basis function (RBF) to identify the types of faults by using current and voltage waveforms. This technique is not applicable to the distribution system due to asymmetrical nature of the network. Signal injection and travelling wave techniques were introduced in [19, 20] respectively. In these methods, the signal was recorded via FTU that is installed on each feeder in the selected distribution network. Then an algorithm analyzes this signal to calculate fault location. Despite of good results, this technique requires expensive devices to capture the faultgenerated travelling waves. In [21, 22], an enterprise-wide master software platform and a temporal constraint network was proposed to accomplish power system reliability according to CAIDI by determining fault section, fault types and expected state of circuit breaker (CB) accurately. Even though the methods are effective, especially for on-line system, it involves an expensive installation due to implementation of intelligent electronic devices and sophisticated fault recorders. This paper presents an improved approach for locating and identifying fault in a practical distribution system by using ANFIS. The system is configured under matlab environment and the codes are compiled to build a GUI tool. The purpose of building the GUI tool is to display the faults effectively to the operator for further actions. A comparison has also been made between previous methods in locating fault in a practical distribution network. It is found that the proposed approach is able to locate precise fault points in terms of geometrical coordinates, whereas other methods locate the faults in terms of line length and feeder sections. PSS-ADEPT software was used to generate the post-fault three-phase RMS current. 2.0 The ANFIS Concept As a simple data learning technique, ANFIS uses a fuzzy inference system model to transform a given input into a target output. This transformation involves membership functions, fuzzy logic 2
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operators and if-then rules. ANFIS is a Sugeno model from a development of fuzzy inference system (FIS) [23]. Sugeno-type ANFIS implement first-order polynomial to the output system in order to replace zero-order in Sugeno FIS model. There are five main processing stages in ANFIS operation including input fuzzification, application of fuzzy operators, application method, output aggregation and defuzzification [24]. The stages are demonstrated in the ANFIS structure as shown in Figure 1. Each ANFIS layer has specific functions for calculating input and output parameter sets as described below [23]. layer 1 X1
layer 2
w
W1 R
X2
layer 4
layer 3
П
N
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RYB
w f layer 5
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П
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w
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OT
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w
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П
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Figure 1 Basic ANFIS structure with three inputs and three rules Layer 1: Fuzzification of input data is performed in this layer. The following equations are used for this process: X (R ) =
, i=1,2,3
(1)
Y (Y ) =
, i=1,2,3
(2)
Z (B) =
, i=1,2,3
(3)
where Xi, Yi and Zi are the fuzzifier input values, and ai, bi and ci are the parameters sets that are calculated by Gaussian input membership function. Layer 2: This layer involves fuzzy operators and it is indicated by a circle node with labeled П. It uses the product (AND) operator to fuzzify the inputs. The following fuzzy relationship represents the products of fuzzy operators:
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w = X (R) × Y (Y) × Z (B) , i=1,2,3
(4)
Layer 3: This layer is marked with N in a circle node for the application method of rules, the activation degree and normalization are implemented by using the following relationship: w =
, i=1,2,3
(5)
Layer 4: The product of the normalized activation degree and individual output membership function for aggregating all output is given in this layer by: O = w f = w (p R + q Y + r B + s ) , i=1,2,3
(6)
where pi, qi, ri and si are the parameter sets of the output membership function. Layer 5: For defuzzification, the following equation is applied in the last layer: O = overall output = ∑
O
(7)
3.0 Proposed Fault Location Method Generally, fault location methods are based on line impedance in a transmission system and gives appropriate results. The methods do not comply with distribution system due to complex and asymmetrical nature of networks. Therefore, many researchers introduced an artificial intelligent (AI) system based on practical knowledge to define fault location in a distribution system through fault distance from feeding substation and fault zones. Outcome of such proposals is to provide fast and efficient restoration plan in the power system. Therefore, ANFIS concept is applied in this work to predict fault points precisely in term of the X and Y geometrical coordinates. The ANFIS approach uses post-fault three-phase RMS current to predict types of faults in integer between 1 to 10 and fault points in term of X-Y coordinates. For fault points, the distribution network is drawn in a two dimensional X and Y plane with possible faults on lines, underground cables and busbars. The considered fault types are; 1 - ‘A’ phase to ground (AG), 2 – ‘B’ phase to ground (BG), 3 – ‘C’ phase to ground (CG), 4 – three-phase (3P), 5 – ‘A’ phase to ‘B’ phase (AB), 6 – ‘B’ phase to ‘C’ phase (BC), 7 – ‘C’ phase to ‘A’ phase (CA), 8 – ‘AB’ phase to ground (ABG), 9 – ‘BC’ phase to ground (BCG) and 10 – ‘CA’ phase to ground (CAG) faults. 3.1 Test Distribution Network The DISCO-Net 34-substations test practical distribution network is used as a test system for the proposed fault location method as shown in Figure 2. The system has 11 feeders and 28 laterals lines with their loads respectively. The maximum length of the underground cable is 30 km from substation with respective coordinates of (7.8, 2) and (1, 2.2) in Figure 2. Post-fault three-phase RMS currents are collected at the substation bus 132 (1, 2.2) with the help of a metering network.
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Figure 2 The DISCO-Net test distribution network 3.2 Design of the Proposed ANFIS for Classifying the Fault Types Figure 3 shows the concept of hierarchical distribution ANFIS network for classifying the fault types. It is designed to identify the correct types of fault with minimum prediction error. The structure of the design consists of 10 ANFIS blocks to represent each types of fault as an integer number between 1 and 10. First block is ANFIS1 which predicts a value 1 for AG fault. Similarly ANFIS 2 to ANFIS10 blocks representing BG, CG, 3P, AB, BC, CA, ABG, BCG and CAG faults, respectively. Each ANFIS block receives a post-fault three-phase RMS current and yield predicted integer value as an output. Table 1 shows corresponding input and output parameters of each ANFIS module for classifying the types of fault. Post-fault 3-phase RMS current ANFIS1
Post-fault 3-phase RMS current
Post-fault 3-phase RMS current ANFIS10
ANFIS2
AG fault BG fault CAG fault Figure 3 ANFIS design for classifying the fault types Table 1 Input/output parameters of the ANFIS modules for classifying the fault types ANFIS Models Input Output ANFIS1 Post-fault 3-phase RMS current 1 AG fault ANFIS2 “ 2 BG fault ANFIS3 “ 3 CG fault ANFIS4 “ 4 3P fault ANFIS5 “ 5 AB fault ANFIS6 “ 6 BC fault ANFIS7 “ 7 CA fault ANFIS8 “ 8 ABG fault ANFIS9 “ 9 BCG fault ANFIS10 “ 10 CAG fault 5
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3.3 A Design of Proposed ANFIS for Determining Precise Fault Points Main function of the design is to determine precise fault points after classifying types of fault with average percentage error below 0.1%. The proposed design is shown in Figure 4 which produces X-Y coordinates as fault points by predicting the post-fault three-phase RMS current. It consists of 64 and 45 ANFIS modules for respective X and Y points for various faults. Each block produces predicted values of fault points. Table 2 shows the corresponding input and output of each of these ANFIS modules. Post-fault 3-phase RMS current
Post-fault 3-phase RMS current
Post-fault 3-phase RMS current
Post-fault 3-phase RMS current
ANFIS11 `
ANFIS64
ANFIS65
ANFIS119
X1
X64
Y1
Y45
X coordinate for 64 points
Y coordinate for 45 points
Figure 4 ANFIS design for determining fault points Table 2 Input/output parameters of the ANFIS modules for determining fault points ANFIS Module Input Output ANFIS11 Post-fault 3-phase RMS current X point, 1.7
ANFIS64 ANFIS65
“ “
X point, 9.1 Y point, 0.3
ANFIS119
“
Y point, 5.2
3.4 ANFIS Implementation for Fault Location The developed fault location technique consists of 10 ANFIS blocks for classifying correct fault type. About 64 and 45 ANFIS modules are used for determining precise fault points in X and Y coordinates, respectively. These ANFIS block sets are configured in Matlab software and the codes are compiled in GUI tool to build a usable fault location system tool (FLST). The ANFIS training method is carried out using the hybrid learning algorithm that combines the leastsquares estimation and the gradient descent method [23]. Sugeno-type ANFIS with membership function of generalized-bell (gbellmf) is chosen for this implementation due to its accurate prediction compared to others type of ANFIS. Before implementing the proposed fault location technique, ANFIS models are trained according to a given training data set. There are some steps in the training process that which are presented in the flowchart of Figure 5.
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Selected Distribution System (DISCO-Net 34-Substation) SIMULATION PROCESS (Number of simulations = 2119) Considering 10 types of fault with Rf of 10Ω and 40Ω (AG, BG, CG, 3P, AB, BC, CA, ABG, BCG and CAG)
Recording the post-fault three-phase RMS current for each fixed fault point (There are about 163 fixed fault points)
Classifying the recorded data as input and output to train ANFIS modules (50% of the data for training) Figure 5 Flowchart for ANFIS training process According to the work flow of training process, first step is to prepare a layout of the selected distribution network as shown in Figure 2 as well as their parameter that is required by the PSS-ADEPT package software. Then, the second step involves, simulation of the network for various faults by considering 10 types of fault having different fault resistance (Rf) of 10Ω and 40Ω respectively. This simulation records post-fault three-phase RMS current at feeding substation for each fixed fault point. There are about 163 points for this distribution network, so the number of simulation for A phase, B phase and C phase to ground fault is 978 considering the two Rf. For the rest of seven types with 163 fault points, it requires additional 1141 simulations. Therefore, the total number of simulations is about 2119. The recorded post-fault three-phase RMS currents are then managed to classify data inputs and outputs to the ANFIS blocks. The ANFIS modules of ANFIS1 to ANFIS119 use 50% of the data sets for training and the rest reserve for testing. Training and testing data sets are shown in Table 3, Table 4 and Table 5 for fault types, X and Y coordinates, respectively. Meanwhile, Figure 6 presents the training accuracies of the modules. Table 3 Composition of training and testing data sets for types of fault Total no. of No. of Training Testing ANFIS models generated ANFIS data set data set data set input ANFIS1-ANFIS3 445 223 222 3 ANFIS4 157 79 78 3 ANFIS5-ANFIS10 162 81 81 3
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Table 4 Composition of training and testing data sets for X coordinate Total no. of No. of Training Testing ANFIS models generated ANFIS data set data set data set input ANFIS11-ANFIS20 ±39 ±20 ±19 3 ANFIS21 109 55 54 3 ANFIS22-ANFIS31 ±35 ±18 ±17 3 ANFIS32 64 32 32 3 ANFIS33-ANFIS46 ±31 ±16 ±15 3 ANFIS47 144 72 72 3 ANFIS48-ANFIS62 ±44 ±22 ±22 3 ANFIS63-ANFIS74 ±26 ±13 ±13 3
No. of ANFIS output 1 1 1 1 1 1 1 1
Table 5 Composition of training and testing data sets for Y coordinate Total no. of No. of Training Testing ANFIS models generated ANFIS data set data set data set input ANFIS75-ANFIS80 ±39 ±20 ±19 3 ANFIS81 180 90 90 3 ANFIS82 170 85 85 3 ANFIS83-ANFIS93 ±59 ±30 ±29 3 ANFIS94-ANFIS103 ±56 ±28 ±28 3 ANFIS104-ANFIS119 ±42 ±21 ±21 3
No. of ANFIS output 1 1 1 1 1 1
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0
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RMS Error
Figure 6 ANFIS training accuracy 4.0 Fault Location System Tool Development of the fault location system tool (FLST) is based on Matlab GUI as shown in Figure 7. The purposes of building the GUI tool are to train the data using ANFIS block, display the layout of distribution network under testing with X-Y plane, produces fault types in integer and fault points in X-Y coordinates. By fast and precise fault location, faults can be isolated quickly and proceed with power restoration immediately. The FLST needs only a 8
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standard data format of post-fault three-phase RMS current and a layout of single line distribution network with X-Y plane as a reference to pin point the fault.
ADB
ASB
NLD
I/O
CDF
Figure 7 The application window of fault location system tool The FLST shown in Figure 7 consists of five main parts namely, ANFIS development block (ADB), ANFIS selection block (ASB), current directory files (CDF), input/output data (I/O) and network layout display (NLD). ADB part is used for only first time data training or update training. It consists of 7 text boxes for entering the data and 3 text boxes for graphing the training performance. The text boxes for data includes “File Folder”, “Write ANFIS”, “File Name”, “File Range”, “Number of Row Data”, “MF” and “Epoch” while for graphing part in the middle of GUI, represents the error curves, ANFIS output and ANFIS prediction errors. Next part consist of one text box only for retrieving trained data in the files with ‘fis’ extension by keying-in folder’s name as listed in CDF. The files with this extension are listed in the CDF. They are collected in a created folder for representing trained ANFIS blocks for a test distribution network. If it exists in the directory, that means the network has been trained and it is already in the fault location system. I/O part provides 9 blank text boxes, 3 on the top are the data file name, data range for X and Y coordinates. Meanwhile, another 3 on left side of the part are input post-fault threephase RMS current (IA, IB, IC) and 3 output parameters are on the opposite side. This part is operated when button “start” be pressed. There is one button labeled as “Distribution Network” in the NLD part for accessing another window of listing of distribution network in X-Y plane. 5.0 Test Result Computational time and prediction accuracy for every trained ANFIS modules were recorded to test the performance of the ANFIS based fault location method. The time taken was about 7 seconds for executing all functions when a computer with Intel(R) Core (TM) 2 Quad CPU Q6700 @ 2.67 GHz was used. Each ANFIS models were trained based on 50% of the collected simulation data. However, for testing, all data were used and it is shown in Figure 8 for all types of faults. 9
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9000 8000 RMS Current (A)
7000 6000 5000 4000 3000 2000 1000 1 104 207 310 413 516 619 722 825 928 1031 1134 1237 1340 1443 1546 1649 1752 1855 1958 2061 2164 2267 2370
0 Number of data IA IB IC
Figure 8 Patterns of the RMS currents collected data for all faults
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10 8 6 4 2 1 119 237 355 473 591 709 827 945 1063 1181 1299 1417 1535 1653 1771 1889 2007 2125 2243 2361
0
Percentage error
The ANFIS testing accuracy was presented in term of percentage errors (PE) for classifying types of fault and locating the fault points as shown in Figure 9 to Figure 11. Equation (8) gives the PE between the actual value obtained from the simulation and ANFIS output. From the Figure 10 and Figure 11, it can be seen that the maximum percentage error (MPE) for predicting X points and Y points were 1.86% and 10.68% respectively but the average percentage error (APE), is found to be 1.193E-5% and 0.0214% for the respective points. The highest error in the figures was for single phase faults compared to others fault types. This was due to large variation in trained data when considering two fault resistance values.
Actual target
Percentage error
Figure 9 Percentage errors between actual targets and predicted value for classifying type of faults
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Figure 10 Percentage errors between actual targets and predicted value for locating faults in the X coordinates
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-2 -4 1 104 207 310 413 516 619 722 825 928 1031 1134 1237 1340 1443 1546 1649 1752 1855 1958 2061 2164 2267 2370
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Percentage error
Figure 11 Percentage errors between actual targets and predicted value for locating faults in the Y coordinates The proposed ANFIS fault location technique was compared with the previous methods that utilizes artificial neural network (ANN) [25, 26]. The comparison was made to demonstrate the effectiveness of the proposed technique. The variations in both methods are presented in terms of PE for locating fault point in ‘X’ and ‘Y’ coordinates as shown in Figure 12 and Figure 13 respectively. From these figures, it can be noted that the ANN method has much higher average percentage error (APE) than the proposed ANFIS method by 0.027% and 1.193E-3% respectively for X while 0.1859% and 0.0214% for Y. From the comparison of both findings, the results indicate that the proposed design is effective as an approach for locating precise fault points and identifying accurate fault types in integers. In term of computation time and reliability, ANFIS approach has satisfactory performance in running the FLST tool. Overall speaking, the ANFIS design is more acceptable and accurate fault location system with low cost implementation. Incorporating the concept of geometrical coordinates in fault location makes it more suitable for large distribution networks. 11
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Figure 12 A comparison of percentage errors between proposed ANFIS and ANN methods for locating fault point in coordinate ‘X’ 20 10 8
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Figure 13 A comparison of percentages errors between proposed ANFIS and ANN methods for locating fault point in coordinate ‘Y’ 6.0 Conclusion This paper has presented an application of fault location method for locating faults in a practical distribution network. The Proposed ANFIS uses only post-fault three-phase RMS currents as inputs, and outputs were X-Y points and integers between 1 and 10. The results show that the approach can accurately locate the faults and identify the fault’s types in the DISCO-Net 34substations test practical distribution system. Its accuracy has been validated by comparing the actual fault locations and types of fault obtained from the PSS-ADEPT simulations and with ANN approach [25, 26].
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Acknowledgments This work of research has been financially supported by Universiti Kebangsaan Malaysia (UKM). References 1. Wenxin Guo, Fushuan Wen and Ledwich, G (2010). An Analytic Model for Fault Diagnosis in Power Systems Considering Malfunctions of Protective Relays and Circuit Breakers. IEEE Transaction on Power Delivery, 25(3) 1393-1401. 2. Ning kang and Yuan Liao (2010). Fault Location Estimation Using Current Magnitude Measurements. Proceedings of the IEEE Southest Conference (SECON ‘10). 3. Salim, RH, Resenar, M, Filomena, AD, Karen, RCO, et al. (2009). Extended FaultLocation Formulation for Power Distribution System. IEEE Transaction on Power Delivery, 24(2) 508-516. 4. Filomena, AD, Resenar, M, Salim, RH and Bretas, AS (2008). Extended ImpedanceBased Fault Location Formulation for Unbalanced Distribution System. General Meeting – Conversion and Delivery of Electrical Energy in 21st Century, IEEE Power & Energy Society. 5. Yuan Liao and Ning kang (2009). Fault-Location Algorithms Without Utilizing Line Parameters Based on the Distributed Parameter Line Model. IEEE Transaction on Power Delivery, 24(2) 579-584. 6. Ning Kang and Yuan Liao (2009). New Fault Location Technique for Double-Circuit Transmission Lines Utilizing Sparse Voltage Measurement. Power & Energy Society General Meeting (PES ’09), IEEE Power & Energy Society. 7. Kang, Ning and Liao, Yuan (2009). New Fault Location Technique for Double-Circuit Transmission Lines Based on Sparse Current Measurements. North America Power Symposium (NAPS ’09). 8. Martins, LS, Martinst, JF and Pines, VF (2008). A Formal language Approach in Fault Location on Distribution Power System. 9th International Developments in Power System Protection Conference (DPSP ‘08). IET. 9. Countney, DA, Littlert, TB and Livie, J (2008). Fault Location on a Distribution Network Using a Decentralized Analysis Process. 9th International Developments in Power System Protection Conference (DPSP ‘08). IET. 10. Nian Mei, Dongyuan Shi, Zengli Yong and Xianzhong Duan (2007). A Matrix Based Fault Section Estimation Algorithm for Complex Distribution System. 42nd International Universities Power Engineering Conference (UPEC ’07). 11. Mora-Florez, J, Barrea-Nurez, V and Carrillo-Caicedo, G (2007). Fault Location in Power Distribution Using a Learning Algorithm for Multivariable Data analysis. IEEE Transaction on Power Delivery, 22(3) 1715-1721. 12. Reddy, MJ and Mohanta, DK (2008). Adaptive-Neuro- Fuzzy Inference System Approach for Transmission Line Fault Classification and Location Incorporating Effects of Powering. IET Transaction on Generation, Transmission & Distribution, 2(2) 235-244. 13. Rasli A Ghani, Azah Mohamed and Hussin Shareef (2009). ANFIS Approach for Locating Precise fault Points with Coordinated Goemetries in a Test Distribution System. European Journal of Scientific Research (EJSR), 35(3) 461-473. 14. Javadian, SAM, Nasrabadi, AM, Haghifam, MR and Rezvantalab, J (2009). Determining Fault’s Type and Accurate Location in Distribution System with DG Using MLP Neural Networks. International Conference on Clean Electrical Power (ICCEP ‘09). 15. Javadian, SAM, Haghifam, MR and Rezaei, N (2009). A Fault Location and Protection Scheme for Distribution Systems in Presence of DG Using MLP Neural Network. Power & Energy Society General Meeting (PES ‘09), IEEE Power & Energy Society.
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16. Shakya, D and Singh, SN (2008). SVM Based Fault Location and Classification Using Fuzzy Classifier for PQ Monitoring. General Meeting – Conversion and Delivery of Electrical Energy. IEEE Power & Energy Society. 17. Malathi, V and Marimuthu, NS (2009). Support Vector Machine for Fault Detection in Transmission Line. International Journal of Engineering Intelligent Systems, 17(1) 1318. 18. Dash, PK and Samantray, SR (2004). An Accurate Fault Classification Algorithm Using a Minimal Radial Basis Function Neural Network. International Journal of Engineering Intelligent Systems, 12(4) 205-210. 19. Huifen Zhang, Zhiguang Tian and Enping Zhang (2008). An Improved Algorithm for Fault Location in Distribution Network. International Conference on Condition Monitoring and Diagnosis (CMD ‘08). 20. Alberto, B, Mauro, B, Mauro, D, Carlo, AN, et al. (2008). Continuous-Wavelet Transform for Fault Location Distribution Power Networks – Definition of Mother Wavelets Inferred from Fault Originated Transients. IEEE Transactions on Power System, 23(2) 380 – 388. 21. Branca, D (2008). A New Approach to Find Fault Locations on Distribution feeder circuits – part II. Rural Electric Power Conference (REPC ‘08). IEEE. 22. Yue, Ling, Liao, Zhiwei and Huang, Shaoxian (2010). Fault Diagnosis Based on Fault Recorder Data and Temporal Constraint Network in HV Transmission System. International Conference on Green Circuits and System (ICGCS ‘10). 23. Jang JSR (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transaction on System, Man, and Cybernetics, 23() 665 – 685. 24. Mora, JJ, Carrillo, G and Perez, L (2006). Fault Location in Power Distribution System Using ANFIS nets and Current Patterns. International Conference on Transmission and Distribution (TDC ‘06), Latin America, IEEE Power & Energy Society. 25. Salim, RH, Oliveira, D, Filomena, K, Resener, AD, et al. (2008). Hybrid Fault Diagnosis Scheme Implementation for Power Distribution Systems Automation. IEEE Transaction on Power Delivery, 23(4) 1846 – 1856. 26. Oliveira, KRC (2007). Unbalanced Underground Distribution Systems Fault Detection and Section Estimation. Springer Berlin, Heidelberg.
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