【目的】目前，隔离开关已被广泛应用于电网中，然而对其故障诊断的研究相比于变压器、断路器等设备却较少。通过隔离开关运行时的振动信号来准确识别其故障类型对于电网的正常运行和工作人员的人身安全具有重要意义。【方法】本文采用了自适应t分布扰动策略来改进伞蜥优化(FLO)算法，得到改进后的融合自适应t分布扰动的伞蜥优化(tFLO)算法，进而对连续变分模态分解(SVMD)和最小二乘支持向量机(LSSVM)的参数寻优，以实现对隔离开关故障的识别。首先，引入自适应t分布扰动策略改进FLO算法；然后，利用tFLO-SVMD对试验数据进行分解得到最佳的模态分量；计算模态分量的精细复合多尺度模糊散布熵(RCMFDE)得到高维特征矩阵；最后，使用tFLO-LSSVM算法将核主成分分析法(KPCA)对高维矩阵降维后的多组低维特征值矩阵进行故障的分类。【结果】本文针对某220 kV高压隔离开关提出的基于tFLO-SVMD-LSSVM-RCMFDE的故障诊断方法的试验准确率达97.92%，能有效识别隔离开关故障类型。【结论】在传统VMD方法分解的本征模态函数(IMF)分量中存在计算速度慢、模态中心鲁棒性差及需要额外优化模态个数k等问题，SVMD算法能够很好地解决这些问题且分解地更细致。同时，熵值计算能有效量化时间序列的复杂性和不确定性，模糊散布熵(FDE)具有计算时间短，抗干扰强的优点。而RCMFDE相比于FDE稳定性更好，对特征地反映更加全面。tFLO-SVMD与RCMFDE结合能够有效地区分隔离开关不同类型故障的振动信号。综上，本文证明基于tFLO-SVMD-LSSVM-RCMFDE分类方法能有效识别隔离开关故障，具有较高的识别精度。

[Objective] Currently, isolating switches have been widely used in power grids, but research on their fault diagnosis remains limited compared to equipment such as transformers and circuit breakers. The accurate identification of fault types in isolating switches through vibration signals during their operation is crucial for the normal operation of power grids and the safety of maintenance personnel. [Methods] This paper proposes an improved frilled lizard optimization (FLO) algorithm by incorporating an adaptive t-distribution perturbation strategy (tFLO). Subsequently, the algorithm is applied to parameter optimization for both sequential variational mode decomposition (SVMD) and least squares support vector machine (LSSVM) to achieve accurate fault identification in isolating switches. Firstly, an adaptive t-distribution perturbation strategy was incorporated to improve the FLO algorithm. The tFLO-SVMD then decomposed experimental data to obtain optimal modal components. Next, the refined composite multiscale fuzzy dispersion entropy (RCMFDE) of the modal components was calculated to obtain a high-dimensional feature matrix. Finally, the tFLO-LSSVM algorithm was used to classify faults in multiple sets of low dimensional feature matrices obtained by reducing the dimensionality of the high-dimensional matrices through kernel principal component analysis (KPCA). [Results] The fault diagnosis method based on tFLO-SVMD-LSSVM-RCMFDE proposed in this study for a 220 kV high voltage isolating switch achieved an accuracy of 97.92%, demonstrating effective identification of various fault types in isolating switches. [Conclusion] There are problems of slow computation speed and poor robustness of modal centers in the intrinsic mode function (IMF) components decomposed by traditional VMD methods, necessitating additional optimization of the number of modes k. The SVMD algorithm can effectively address these issues while achieving more detailed decomposition. Additionally, entropy calculation can effectively quantify the complexity and uncertainty of time series, and fuzzy dispersion entropy (FDE) has the advantages of short computation time and strong anti-interference. Compared to FDE, RCMFDE demonstrates better stability and more comprehensive feature representation. The combination of tFLO-SVMD and RCMFDE can effectively distinguish vibration signals of different fault types. In summary, this study proves that the classification method based on tFLO-SVMD-LSSVM-RCMFDE can effectively identify faults in isolating switches with high accuracy.

国家自然科学基金项目(51577050);国网江苏省电力有限公司重点科技项目(J2024047)