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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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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