Abstract
Transformer state predicting is important for the power equipment’s stable operation of power systems. By analyzing the historical change of oil level, abnormal conditions can be found in time, which can assist operation and maintenance personnel to find abnormal conditions such as respirator blockage and oil leakage. In this work, a method based on Rolling-EEMD is proposed to predict the oil level of transformer and deal with the problem that the decomposition results of same period will not keep exactly the same, or the number of decomposition results may change. First, the Rolling-EEMD is used to smooth out the oil level historical data curves to eliminate spikes and peaks. Then the predicting models with quantile loss are used for transformers oil level interval predicting. Finally, after extensive experiments, the method proposed can effectively predict the trend of oil level changes and provide effective reference for operation and maintenance personnel.
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Acknowledgments
This research is supported by Joint Funds integration project of the National Natural Science Foundation of China (U1866603). Fundamental Research on Multivariable-Based Adaptive Protection and Safe Operation for Power Transformers.
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Lv, S., Shan, M., Wang, W., Ding, J., Zhang, H. (2023). A Rolling-EEMD Method for Transformer Oil Level Prediction. In: **e, K., Hu, J., Yang, Q., Li, J. (eds) The Proceedings of the 17th Annual Conference of China Electrotechnical Society. ACCES 2022. Lecture Notes in Electrical Engineering, vol 1014. Springer, Singapore. https://doi.org/10.1007/978-981-99-0408-2_25
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DOI: https://doi.org/10.1007/978-981-99-0408-2_25
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