Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method

• Published in: PeerJ. Computer science Vol. 10; p. e2045
• Main Authors: Yang, Yang, Wu, Yongye, Gao, Yifei, Huang, Yixuan, Liu, Shukun, Wang, Yuanshi
• Format: Journal Article
• Language: English
• Published: PeerJ Inc 28.05.2024
• Subjects: BPNN; Dung beetle optimization algorithm; Hybrid high-voltage power transmission lines; Orthogonal contrastive learning; Partial discharge
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.2045

Due to their specially designed structures, the partial discharge detection of hybrid high-voltage power transmission lines (HHVPTL) composed of overhead lines and power cables has made it difficult to monitor the conditions of power transmission lines. A parallel recognition method for partial discharge patterns of HHVPTLs is proposed by implementing wavelet analysis and improved backpropagation neural network (BPNN) to address the shortcomings of low efficiency, poor accuracy, and inability to parallel analysis of current partial discharge (PD) detection algorithms for HHVPTLs. Firstly, considering the non-smoothness of the partial discharge of the HHVPTLs, the wavelet packet decomposition algorithm is implemented to decompose the PD of the HHVPTL and resolve the relevant signal indicators to form the attribute vectors. Then, BPNN is implemented as a classification model. A beetle optimization (DBO) algorithm based on orthogonal contrastive learning improvement is introduced to optimize the BPNN parameters since BPNN has a slow convergence problem and fails easily into a local optimum. The proposed IDBO-BPNN is employed as the model that recognizes and analyzes the parallel partial discharge patterns of HHVPTLs. Finally, the suggested model is implemented to investigate the local discharge data of an HHVPTL in the Kaggle Featured Prediction Competition and is compared with other algorithms. The experimental results indicate that the proposed model can more accurately identify whether PDs occur in an HHVPTL and detect phases where PDs occur, with higher overall accuracy and efficiency. An excellent practical performance is achieved. The proposed model can achieve the recognition accuracy of 95.5%, which is 5.3333% higher than that of the DBO-BPNN and far more than other recognition algorithms.