Abstract
Microgrid is efficient and low-cost local grid. It consists of both local sources and loads. With the integration of more renewable energy sources, the chances of fault occurrence also increased. Fault is the abnormal condition that happen in microgrid, and it leads to change in the behavior of the system. These changes should be detected as fast as possible to avoid property damage and human loss. In this method, different machine algorithm-based classifiers are used to detect these changes in microgrid. Discrete wavelet transforms (DWT) based signal processing technique is used to analyze the transient signals pattern recognition method is used to classify the fault and non-fault data. An IEEE 9 bus test microgrid system is used to verify the proposed algorithm. The proposed methodology results are collated with dual-tree complex wavelet transform (DTCWT) based signal processing technique. Necessary simulation work is done in the MATLAB.
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References
Ramana Kumar Joga S, Sinha P, Maharana MK (2019) Artificial intelligence in classifying high impedance faults in electrical power distribution system. In: Proceedings of international conference on recent trends in computing, communication and networking technologies (ICRTCCNT). Available at SSRN: https://ssrn.com/abstract=3430316 or https://doi.org/10.2139/ssrn.3430316
Joga S, Sinha P, Maharana MK (2021) Genetic algorithm and graph theory approach to select protection zone in distribution system. In: Zhou N, Hemamalini S (eds) Advances in smart grid technology. Lecture notes in electrical engineering, vol 688. Springer, Singapore. https://doi.org/10.1007/978-981-15-7241-8_13
Jarrahi MA, Samet H, Ghanbari T (2020) Novel change detection and fault classification scheme for AC microgrids. IEEE Syst J 14(3):3987–3998. https://doi.org/10.1109/JSYST.2020.2966686
Sharma NK, Samantaray SR (2020) PMU assisted Integrated impedance angle-based microgrid protection scheme. IEEE Trans Power Delivery 35(1):183–193. https://doi.org/10.1109/TPWRD.2019.2925887
Gong R, Ruan T (2020) A new convolutional network structure for power quality disturbance identification and classification in micro-grids. IEEE Access 8:88801–88814. https://doi.org/10.1109/ACCESS.2020.2993202
Yu JJQ, Hou Y, Lam AYS, Li VOK (2019) Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks. IEEE Trans Smart Grid 10(2):1694–1703. https://doi.org/10.1109/TSG.2017.2776310
Dubey K, Jena P (2021) Impedance angle-based differential protection scheme for microgrid feeders. IEEE Syst J. https://doi.org/10.1109/JSYST.2020.3005645
Pradhan R, Verma P, Jena P (2020)Fault detection in Islanded microgrid based on positive power sequence component. In: IEEE international symposium on sustainable energy, signal processing and cyber security (iSSSC), Gunupur Odisha, India, pp 1–6.https://doi.org/10.1109/iSSSC50941.2020.9358875
Baghaee HR, Mlakić D, Nikolovski S, Dragicčvić T (2020) Anti-islanding protection of PV-based microgrids consisting of PHEVs using SVMs. IEEE Trans Smart Grid 11(1):483–500. https://doi.org/10.1109/TSG.2019.2924290
Haque M, Shaheed MN, Choi S (2018)Deep learning based micro-grid fault detection and classification in future smart vehicle. In: IEEE transportation electrification conference and expo (ITEC), Long Beach, CA, USA, pp 1082–1107.https://doi.org/10.1109/ITEC.2018.8450201
Sahoo A, Arunan A, Mahmud K, Ravishankar J, Nizami MSH, Hossain MJ (2019)Teager-Huang based fault detection in inverter-interfaced AC microgrid. In: IEEE international conference on environment and electrical engineering and 2019 IEEE industrial and commercial power systems europe (EEEIC / I&CPS Europe), Genova, Italy, pp 1–5. https://doi.org/10.1109/EEEIC.2019.8783453
Swain PH, Hauska H (1977) The decision tree classifier: design and potential. IEEE Trans Geosci Electron 15(3):142–147. https://doi.org/10.1109/TGE.1977.6498972
Martinez-Arroyo M, Sucar LE (2006) Learning an optimal Naive Bayes classifier. In: 18th International conference on pattern recognition (ICPR’06), Hong Kong, China, pp 958–958. https://doi.org/10.1109/ICPR.2006.749.
Zhang Y (2012) Support vector machine classification algorithm and its application. In: Liu C, Wang L, Yang A (eds) Information computing and applications. ICICA 2012. Communications in computer and information science, vol 308. Springer, Berlin. https://doi.org/10.1007/978-3-642-34041-3_27
Guo G, Wang H, Bell D, Bi Y, Greer K (2003) KNN model-based approach in classification. In: Meersman R, Tari Z, Schmidt DC (eds) On the move to meaningful internet systems 2003: CoopIS, DOA, and ODBASE. OTM 2003. Lecture notes in computer science, vol 2888. Springer, Berlin. https://doi.org/10.1007/978-3-540-39964-3_62
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Joga, S.R.K., Kumar, A., Sinha, P., Maharana, M.K. (2022). Detecting the Change in Microgrid Using Pattern Recognition and Machine Learning. In: Mahapatra, R.P., Peddoju, S.K., Roy, S., Parwekar, P., Goel, L. (eds) Proceedings of International Conference on Recent Trends in Computing . Lecture Notes in Networks and Systems, vol 341. Springer, Singapore. https://doi.org/10.1007/978-981-16-7118-0_23
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