Multilevel categorizing leakage current of 15 kV HDPE insulators based bidirectional gated recurrent unit

Abstract

Cleaning the polluted insulators is the most efficient way to enhance distribution lines' reliability and reduce contamination flashovers. The measurement of leakage current quantity is a critical issue that can be implemented. Many previous research and laboratory experiments prove that the leakage current levels could fully reflect the contamination developments. To predict the contamination levels of insulators and prevent flashover accidents, the multilevel categorizing leakage current system is installed to simulate the impact of different weather factors. In this project, the leakage currents of 15 kV HDPE insulators accumulate with varying weather elements in coastal areas of Taiwan. The Bidirectional Gated Recurrent Unit (Bi-GRU) is presented to multilevel categorize the leakage currents, which are utilized to evaluate the contamination flashovers. The hyperparameter optimization is employed to develop the most optimum deep learning Bi-GRU model architecture. Moreover, the effectiveness and stabilization of Bi-GRU are investigated and analyzed with other deep learning methodologies, which include the recurrent neural network (RNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The comparison performance demonstrates that the addition of bidirectional layers in the GRU method has outperformed the LSTM algorithm, which improves 5.27 %, 14.03 %, 0.35 %, and 0.06 % in category cross-entropy (CCE) accurate benchmarks in the training and testing operations. The prediction of multilevel leakage current could estimate the safety degree of HDPE insulators before the development of pollution flashover accidents, and the cleaning or replacing maintenance services are operated on time.