基于SVM与GA融合的电力系统继电保护定值优化  

Optimization of Relay Protection Settings in Power Systems Based on the Integration of SVM and GA

作　　者：黄宇轩 Huang Yuxuan(State Grid Huai'an Hongze District Power Supply Company,Huaian 223100,China)

机构地区：[1]国网淮安市洪泽区供电公司,淮安223100

出　　处：《办公自动化》2025年第6期107-109,共3页Office Informatization

摘　　要：电力系统继电保护是保障电力系统稳定与安全运行的关键环节。传统的定值优化方法在处理复杂电力系统时面临计算复杂度高和适应性差的问题。为此,文章提出一种基于支持向量机(SVM)与遗传算法(GA)融合的优化方法。SVM用于提取电力系统中的重要特征,GA则用于优化继电保护定值。通过将SVM与GA结合,能充分发挥SVM的分类能力和GA的全局搜索优势,从而提高定值优化的效率和效果。实验结果表明,该方法在优化精度和计算时间上均优于传统方法。Power system relay protection is a key component in ensuring the stable and safe operation of the power system.Traditional methods for optimizing fixed values face challenges such as high computational complexity and poor adaptability when handling complex power systems.To address these issues,this study proposes an optimization approach that integrates support vector machines(SVM)and genetic algorithms(GA).SVM is used to extract important features in the power system,while GA optimizes relay protection settings.By integrating SVM and GA,the classification capabilities of SVM and the global search advantages of GA are fully leveraged,enhancing the efficiency and effectiveness of fixed value optimization.Experimental results demonstrate that this approach achieves superior optimization accuracy and reduced computation time compared to traditional methods.

关 键 词：电力系统 继电保护 支持向量机(SVM) 遗传算法(GA) 优化 特征提取 

分 类 号：TM77[电气工程—电力系统及自动化]