Dry-Type Air-Core Series Reactor Turn-to-Turn Short Circuit Fault Detection Method Based on Multi-Parameter Data Fusion

• Published in: Dianli jianshe Vol. 46; no. 4; pp. 16 - 28
• Main Author: WANG Yin, WANG Yuanyuan, CAO Chengjun, ZHANG Lizhi, YIN Youpeng, JI Hongzhen, DOU Di
• Format: Journal Article
• Language: Chinese
• Published: Editorial Department of Electric Power Construction 01.04.2025
• Subjects: dry-type air-core series reactor|turn-to-turn short-circuit|maxwell|principal component analysis|k-nearest neighbor algorithm
• ISSN: 1000-7229
• DOI: 10.12204/j.issn.1000-7229.2025.04.002

[Objective] To address the problems of weak turn-to-turn short-circuit faults in dry-type air-core series reactors， which are difficult to recognize， and the lack of an early warning mechanism in traditional methods， this study proposes a multi-dimensional feature and intelligent algorithm fusion of an early fault diagnosis method. This method can overcome the lack of sensitivity of a single fault feature as it is easily interfered with by the noise of the fault leakage judgment. [Methods] First， the unbalance degree， power factor， zero sequence voltage， and characteristic impedance of the shunt capacitor bank are extracted as fault feature quantities， and their respective evolution laws after the fault are analyzed. Second， principal component analysis （PCA） is used to reduce the dimension and denoise the original data to eliminate interfering information. Subsequently， the denoised features with high saturation are input into the k-nearest neighbors （KNN） algorithm to construct a fault identification and cl