Radial Power Distribution System Fault Classification Model Based on ANFIS
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Abstract
The classification of problems in power systems plays an extremely important part and has evolved into a necessity that is of the utmost importance to the operation of energy grids. For the purpose of fault classification in IEEE 13 node radial distribution systems, this paper makes use of both an Artificial Neural Network (ANN) and a Neural Fuzzy adaptive Inference System (ANFIS). Simulations of the suggested models are carried out in MATLAB/SIMULINK, and fault currents from all three phases are analyzed in order to extract statistical characteristics. Input data vectors include the standard deviation and correlation factors between the currents of any two phases, while output data vectors include the different sorts of faults. The findings demonstrate that the devised method is appropriate for the classification of all symmetrical and unsymmetrical faults.
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S.S. Gururajapathy, H. Mokhlis, H. A. Illias, Fault location and detection techniques in power distribution systems with distributed generation: A review. Renewable and Sustainable Energy Reviews, 2017, 74: p. 949-958
MD Shafiullah and M. A. Abido, S-Transform based FFNN Approach for Distribution Grids Fault Detection and Classification. IEEE Access, 2018, 6: p. 8080-8088.
Lampley, G.C. Fault detection and location on electrical distribution system. in the 2002 Rural Electric Power Conference. Papers Presented at the 46th Annual Conference (Cat. No. 02CH37360). 2002.
M. Abdel-Akher and K. M. Nor, Fault analysis of multiphase distribution systems using symmetrical components. IEEE Transactions on Power Delivery, 2010, 25(4): p. 2931-2939.
Q. Cui, K. EI-Arroudi and G. Joos, An effective feature extraction method in pattern recognition based high impedance fault detection, International Conference on intelligent systems and applied power systems, 2017, 29: p. 1-6.
X. Z. Dong and S. X. Shi, Identifying single-phase to ground fault feeder in neutral non-effectively grounded distribution system using wavelet transform. IEEE Transactions on Power Delivery, 2008, 23(4): p. 1829-1837.
M. Michalik, W. Rebizant, M. Lukowicz, S. J. Lee and S. H. Kang, High impedance fault detection in distribution networks with use of wavelet based algorithms. IEEE Transactions on Power Delivery, 2006. 21(4): p. 1793-1802.
El Safty, S. and A. El-Zonkoly, Applying wavelet entropy principle in fault classification. International Journal of Electrical Power & Energy Systems, 2009, 31(10): p. 604-607.
El-Zonkoly, A.M. and H. Desouki, Wavelet entropy based algorithm for fault detection and classification in FACTS compensated transmission line. International Journal of Electrical Power & Energy Systems, 2011, 33(8): p. 1368-1374.
H. C. Shu and S. Y. Sun, A new method to detect single phase fault feeder in a distribution network by using S-transform, International Conference on Probabilistic Methods & Applied Power System, 2010, 44: p. 277-282.
N. Wang, V. Aravinthan and Y. Ding, Feeder-level fault detection and classification with multiple sensors: A smart grid scenario, IEEE Workshop on Statistical Signal Processing, 2014, 34: p. 37-40.
Mohamed, E., A. Abdelaziz, and A. Mostafa, A neural network-based scheme for fault diagnosis of power transformers. Electric Power Systems Research, 2005. 75(1): p. 29-39.
Das, B., Fuzzy logic-based fault-type identification in unbalanced radial power distribution systems. IEEE Transactions on Power Delivery, 2005, 21(1): p. 278-285.
Nag, A. and A. Yadav. Fault classification using Artificial Neural Network in combined underground cable and overhead line. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). 2016. IEEE.
Zhang, J., et al., An ANFIS-based fault classification approach in power distribution systems. International Journal of Electrical Power & Energy Systems, 2013. 49: p. 243-252.
A. C. Adewole and R. Tzoneva, Fault detection and classification in a distributed network integrated with distributed generators, IEEE Conference on Intelligent Grid Integration & Renewable Energy Resources, 2012, 55: p. 1-8
J. Klomjit and A. Ngaopitakkul, Selection of proper input pattern in fuzzy logic algorithm for classifying the fault type in underground distribution system, IEEE Conference TENCON, 2016, 67: p. 2650-2655.
18. M. Dehghani, M. H. Khobaan and T. Niknam, Fast fault detection and classification based on a combination of wavelet singular entropy theory and fuzzy logic in distribution lines in the presence of distributed generations, International Journal of Electric Power & Energy Systems, 2016, 78: p. 455-62.
M. Shafullah, M. Ijaz, M. A. Abido and Z. AI-Hamouz, Optimized support vector machine & wavelet transform for distribution grid fault location,. IEEE International Conference CPE-POWERENG, 2017, 45: p. 77-82.
Sanju Saini and Sarita Rani, Temperature Control using intelligent techniques, 2012. IEEE International Conference on Advanced Computing & Communication Technologies, 2012, 35: p. 138-145.
Hitesh Kumar and Sanju Saini, "Chaotic characterization of electric load demand time series & load forecasting by using GA trained artificial neural network," 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016, 23: p. 1423-1428.
K. Hasija, S. Vadhera, A. Kumar and A. Kishore, "Detection and location of faults in underground cable using Matlab/Simulink/ANN and OrCad," IEEE Power India International Conference, 2014, 57: p. 1-
A. Nag and A. Yadav, "Fault classification using Artificial Neural Network in combined underground cable and overhead line,” IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy System, 2016, 46: p.1- 4.
Aluka, M. ., Ganesan, S. ., & Reddy P., V. P. . (2023). A Comparative Study on Pre-Training Models of Deep Learning to Detect Lung Cancer. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 148–155. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2453.
J. Zhang and Zhengyou He and Sheng Lin and Yuanchang Zhang and Qingquan Qian, “An ANFIS-based fault classification approach in power distribution system”, International Journal of Electrical Power & Energy Systems, 2013, 49:p. 243-252.
Garima Tiwari, Sanju Saini, “Neuro- fuzzy approach for detection of faults in an underground cable distribution systems”, International Journal of Recent technology and Engineering, 2019, 8:p. 569-573
Garima Tiwari, Sanju Saini, “ A detailed Review on feature extraction for detection of fault and its classification in power systems”, American Institute of Management and Technology Conference Proceedings”, 2021, 1: p. 132-141
27. Samah el safty and A. El-Zonkoly, Applying wavelet entropy principle in fault classification. November, International Journal of Electrical Power & Energy Systems, 2009, 31(10): p. 604-607.
28. Upendar, J., C. Gupta, and G. Singh, Statistical decision-tree based fault classification scheme for protection of power transmission lines. International Journal of Electrical Power & Energy Systems, 2012. 36(1): p. 1-12.