气体绝缘开关（gas-insulated switchgear，GIS）设备在生产运行时存在多种绝缘缺陷，准确识别绝缘缺陷导致的局部放电信号对保障GIS设备及电力系统安全有重大意义。采用融合黄金正弦算法（golden sine algorithm，Golden-SA）改进减法优化（subtraction-average-based optimizer，SABO）算法，得到了融合黄金正弦改进SABO优化算法（GSABO），对改进的完全自适应噪声集合经验模态分解（improved complete ensemble empirical mode decomposition with adaptive noise）与核极限学习机（kernel extreme learning machine）进行参数寻优，以实现对GIS局部放电故障的识别。首先，针对SABO可能陷入局部最优、收敛速度不够理想等问题，引入混沌映射与黄金正弦对其进行改进。然后，搭建实验平台采集4种典型局部放电信号，利用GSABO-ICEEMDAN对其进行分解，并利用相关系数法筛选有效的模态分量。最后计算筛选后模态分量的样本熵形成特征矩阵，将其输入GSABO-KELM进行故障分类识别。通过实验分析表明，相比于未改进的SABO算法，GSABO在跳出局部最优、收敛速度与精度上有明显的优势。结合其他传统算法进行对比，GSABO-ICEEMDAN-KELM的识别准确率可达99.1667 %，验证了此算法的准确性与优越性，对于GIS局部放电故障诊断的工程应用具有参考意义。

Gas-insulated switchgear (GIS) exhibits various insulation defects during production and operation, and accurately identifying partial discharge signals caused by insulation defects is of significant importance for ensuring the safety of GIS equipment and power systems. By integrating the golden sine algorithm (golden-SA) to improve the subtraction-average-based optimizer (SABO), a fused golden sine-improved SABO optimization algorithm (GSABO) is obtained. This algorithm is applied to optimize parameters for the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the kernel extreme learning machine (KELM) to achieve recognition of GIS partial discharge faults. Firstly, to address issues such as SABO possibly falling into local optima and insufficient convergence speed, chaotic mapping and the golden sine are introduced to improve it. Then, an experimental platform is set up to collect four types of typical partial discharge signals, which are decomposed using GSABO-ICEEMDAN, and effective modal components are screened using the correlation coefficient method. Finally, the sample entropy of the selected modal components is calculated to form a feature matrix, which is input into GSABO-KELM for fault classification and recognition. Experimental analysis shows that, compared to the unimproved SABO algorithm, GSABO demonstrates significant advantages in escaping local optima, convergence speed, and accuracy. Compared with other traditional algorithms, GSABO-ICEEMDAN-KELM achieves a recognition accuracy of 99.1667%, verifying the accuracy and superiority of this algorithm, which holds reference value for engineering applications in GIS partial discharge fault diagnosis.