State Grid Ningxia Electric Power Research Institute, Yinchuan 750002, China.
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China.
Math Biosci Eng. 2023 Jan 4;20(3):4877-4895. doi: 10.3934/mbe.2023226.
Aiming at the problem of on-load tap changer (OLTC) fault diagnosis under imbalanced data conditions (the number of fault states is far less than that of normal data), this paper proposes an OLTC fault diagnosis method based on an Improved Grey Wolf algorithm (IGWO) and Weighted Extreme Learning Machine (WELM) optimization. Firstly, the proposed method assigns different weights to each sample ac-cording to WELM, and measures the classification ability of WELM based on G-mean, so as to realize the modeling of imbalanced data. Secondly, the method uses IGWO to optimize the input weight and hidden layer offset of WELM, avoiding the problems of low search speed and local optimization, and achieving high search efficiency. The results show that IGWO-WLEM can effectively diagnose OLTC faults under imbalanced data conditions, with an improvement of at least 5% compared with existing methods.
针对不平衡数据条件下有载分接开关（OLTC）故障诊断（故障状态数量远小于正常数据）的问题，提出了一种基于改进灰狼算法（IGWO）和加权极限学习机（WELM）优化的 OLTC 故障诊断方法。首先，根据 WELM 为每个样本分配不同的权重，并基于 G-mean 衡量 WELM 的分类能力，从而实现对不平衡数据的建模。其次，该方法使用 IGWO 优化 WELM 的输入权重和隐藏层偏置，避免了搜索速度低和局部优化的问题，实现了高效的搜索。结果表明，IGWO-WELM 可有效诊断不平衡数据条件下的 OLTC 故障，与现有方法相比至少提高了 5%。
