@inproceedings{3052c91db7af494ca89f06056804de3a,
title = "Diagnosis of Mechanical Faults in On-Load Tap Changers of Power Transformer by Using the Integrated Neural Network",
abstract = "The occurrence of mechanical faults in On-load tap changers (OLTC) is a significant factor contributing to power transformer failures. The analysis of motor current signals and OLTC vibration signals offers an effective means to determine the operational status of the equipment. The signals were processed and eigenvalues were extracted using wavelet transform (WT) and variational modal decomposition (VMD), while fault types were categorized using Optimized Support Vector Machines Based on the Gray Wolf Algorithm (GWO-SVM). This study proposes a neural network integrated decision model that combines the K-Nearest Neighbors (KNN), Hidden Markov Model (HMM), and GWO-SVM algorithms to establish a diagnostic model for OLTC faults. The identification accuracy rate of the proposed model exceeds that of conventional solutions, demonstrating its significant practicality in OLTC fault diagnosis.",
keywords = "Fault diagnosis, Integrated Neural Network, On-load tap-changer (OLTC), Variational mode decomposition (VMD)",
author = "Jianyang Huang and Bolan Lai and Zilin Guan and Shihao Fan and Weiwang Wang",
note = "Publisher Copyright: {\textcopyright} Beijing Paike Culture Commu. Co., Ltd. 2025.; 11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024 ; Conference date: 20-06-2024 Through 22-06-2024",
year = "2025",
doi = "10.1007/978-981-97-8812-5\_39",
language = "英语",
isbn = "9789819788118",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "384--392",
editor = "Qingxin Yang and Jian Li",
booktitle = "The Proceedings of the 11th Frontier Academic Forum of Electrical Engineering (FAFEE2024)",
}