基于RPCA-GELM数据驱动的保护测量回路误差评估  

Error assessment of protection measurement circuits based on RPCA-GELM data-driven method

作　　者：李振兴[1] 龚世玉 LI Zhenxing;GONG Shiyu(Hubei Provincial Collaborative Innovation Center for New Energy Microgrid,China Three Gorges University,Yichang 443002,China)

机构地区：[1]新能源微电网湖北省协同创新中心(三峡大学),湖北宜昌443002

出　　处：《电力系统保护与控制》2025年第8期24-33,共10页Power System Protection and Control

基　　金：国家自然科学基金项目资助(52077120)。

摘　　要：保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive principal component analysis and extreme learning machine optimized by grey wolf optimization,RPCA-GELM)数据驱动的保护测量回路误差评估方法。首先基于电力系统正常运行下历史数据与实时数据,应用RPCA技术在线更新主元特征模型以缩短评估时间,进一步引入4种统计算法生成4类误差监测特征量,构建误差综合评判方法进行特征优选,提升误差评估准确率。然后针对模型评估精度取决于关键参数C、s,引入国际无限折叠混沌映射策略对灰狼算法进行优化,以提升参数寻优精度和收敛速度,在此基础上结合ELM算法提出了基于GELM的保护测量回路误差评估方法。最后通过多组对比实验验证了所提方法能实现模型性能优化,且相对其他方法有效提升了保护测量回路误差评估准确率与精度。Protection measurement circuits are the cornerstone of power system relay protection,and their error assessment is crucial for the stable and secure operation of the power grid.Aiming at the risk that the static hidden errors in protection measurement circuits may lead to protection relay maloperation or failure and are difficult to monitor online,this paper proposes a data-driven error assessment method based on recursive principal component analysis and extreme learning machine optimized by grey wolf optimization(RPCA-GELM).First,using historical and real-time data of the power system under normal operation,RPCA is applied to update the principal component feature model online,reducing the assessment time.Then,four classical statistical algorithms are introduced to generate four types of error monitoring feature quantities,and a comprehensive error evaluation method is constructed to optimize feature selection to improve the accuracy of error assessment.Next,considering that the model assessment accuracy depends on the key parameters C ands,an infinite folding chaotic mapping strategy is introduced to optimize the gray wolf algorithm,improving parameter optimization accuracy and convergence speed.On this basis,combined with the ELM algorithm,an error assessment method for protection measurement circuits is proposed using the GELM algorithm.Finally,multiple sets of comparative experiments vilify that the proposed method can optimize the model performance and effectively improve the accuracy and precision of error assessment in protection measurement circuits compared with other methods.

关 键 词：保护测量回路 误差评估 递推主元分析 灰狼算法 极限学习机 

分 类 号：TM77[电气工程—电力系统及自动化]