Abstract
In the proposed work, Wavelet Transform analysis and wavelet entropy methods have been used to classify various types of fault in a nine bus microgrid system. Both methods are compared and analyzed. The simulation result shows that the proposed method successfully identifies the fault type and phase involved in the fault. The proposed algorithm is validated for different locations and fault types on nine bus microgrid system. In addition to the above, wavelet analysis and wavelet coefficients are also used with the Artificial Neural Network (ANN) for detecting and classifying the faults. The different fault cases have different fault resistances and inception angles. The fault detection process is done by the summation of sixth level detail coefficients of current obtained using Discrete Wavelet Transform (DWT) based Multiresolution Analysis (MRA) technique for all the three phases while, for the classification of fault type, wavelet entropy calculations for each phase currents are acquired.
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References
Prasad A, Belwin Edward J, Ravi K (2018) A review on fault classification methodologies in power transmission systems: Part—I. J Electr Syst Inf Technol 5(1):48–60. ISSN 2314-7172
Prasad A, Belwin Edward J, Ravi K (2018) A review on fault classification methodologies in power transmission systems: Part-II. J Electr Syst Inf Technol 5(1):61–67. ISSN 2314-7172
Chan PPK, Zhu J, Qiu Z, Ng WWY, Yeung DS (2011) Comparison of different classifiers in fault detection in microgrid. In: 2011 international conference on machine learning and cybernetics, Guilin, pp 1210–1213
Hare J, Shi X, Gupta S, Bazzi A (2016) Fault diagnostics in smart micro-grids: a survey. Renew Sustain Energy Rev 60:1114–1124. ISSN 1364-0321
Sivanandam SN, Sumathi S, Deepa SN (2015) Introduction to neural networks using MATLAB 6.0. 22nd reprint, McGraw Hill
Upendar J, Gupta CP, Singh GK (2008) ANN based power system fault classification. IEEE Region 10 annual international conference, Proceedings/TENCON. 1–6. https://doi.org/10.1109/tencon.2008.4766623
Karmacharya IM, Gokaraju R (2018) Fault location in ungrounded photovoltaic system using wavelets and ANN. IEEE Trans Power Delivery 33(2):549–559
Srinivasa Rao P (2013) Pattern recognition approach for fault identification in power transmission lines. Int J Eng Res Appl 3:1051–1056
Goharrizi A, Sepehri N (2018) Application of fast fourier and wavelet transforms towards actuator leakage diagnosis: a comparative study. Int J Fluid Power 14(2):39–51. Retrieved from http://journals.riverpublishers.com/index.php/IJFP/article/view/221
Chanda D, Kishore NK, Sinha AK (2003) A wavelet multiresolution analysis for location of faults on transmission lines. Int J Electr Power Energy Syst 25(1):59–69. ISSN 0142-0615
Kirubadevi S, Sutha S (2017) Wavelet based transmission line fault identification and classification, pp 737–741. https://doi.org/10.1109/iccpeic.2017.8290461
Cesar TM, Pimentel SP, Marra EG, Alvarenga BP (2017) Wavelet transform analysis for grid-connected photovoltaic systems. In: 2017 6th international conference on clean electrical power (ICCEP), Santa Margherita Ligure, pp 1–6
Liang J, Elangovan Saikishore, Devotta JBX (1998) A wavelet multiresolution analysis approach to fault detection and classification in transmission lines. Int J Electr Power Energy Syst 20:327–332. https://doi.org/10.1016/S0142-0615(97)00076-8
Manohar M, Koley E, Ghosh S (2017) A reliable fault detection and classification scheme based on wavelet transform and ensemble of SVM for microgrid protection. In: 2017 3rd international conference on applied and theoretical computing and communication technology (iCATccT), Tumkur, pp 24–28
Kar S, Samantaray SR (2016) High impedance fault detection in microgrid using maximal overlap** discrete wavelet transform and decision tree. In: 2016 international conference on electrical power and energy systems (ICEPES), Bhopal, pp 258–263
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423
El Safty S, El-Zonkoly A (2009) Applying wavelet entropy principle in fault classification. Int J Electr Power Energy Syst 31(10):604–607. ISSN 0142-0615
He Zhengyou, Chen **aoqin, Qian Qingquan (2007) A study of wavelet entropy measure definition and its application for fault feature pick-up and classification. J Electron 24:628–634. https://doi.org/10.1007/s11767-005-0253-0
Adewoleand AC, Tzoneva R (2012) Fault detection and classification in a distribution network integrated with distributed generators. In: IEEE power and energy society conference and exposition in Africa: intelligent grid integration of renewable energy resources (PowerAfrica), Johannesburg, pp 1–8
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Singh, P., Singh, N., Choudhary, N.K. (2021). Fault Detection and Classification in Microgrid Using Wavelet Transform and Artificial Neural Network. In: Harvey, D., Kar, H., Verma, S., Bhadauria, V. (eds) Advances in VLSI, Communication, and Signal Processing. Lecture Notes in Electrical Engineering, vol 683. Springer, Singapore. https://doi.org/10.1007/978-981-15-6840-4_2
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DOI: https://doi.org/10.1007/978-981-15-6840-4_2
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