Abstract
Accurate identification of power quality (PQ) disturbance sources is the key to solve PQ problems. In order to improve the accuracy of classifying PQ disturbance sources, this paper proposes a data-driven PQ disturbance sources identification method. It takes some PQ data characteristics into account, such as diversity of indicators, non-linearity and time sequence characteristics. Firstly, the massive data are sifted and sampled. Then the feature subset is extracted by the sequence backward selection algorithm after evaluating the feature importance based on random forest (RF). Finally, the data are aggregated at fixed intervals, smoothed by the sliding average method, and put into Long Short-Term Memory (LSTM) network for model learning and prediction. Experimental results demonstrate that the proposed method is more effective than the traditional RF method.
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Acknowledgement
This work is supported by “The National Key Research and Development Plan (No. 2017YFC0804406), Public Safety Risk Prevention and Control and Emergency Technical equipment”, and “Key Project of the National Natural Science Foundation of China, Research on Big Service Theory and Methods in Big Data Environment (No. 61832004)”.
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Li, Q., Fang, J., Sheng, J. (2019). Data-Driven Power Quality Disturbance Sources Identification Method. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_57
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DOI: https://doi.org/10.1007/978-3-030-30952-7_57
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