电力系统保护与控制2025，Vol.53Issue(8)：24-33,10.DOI:10.19783/j.cnki.pspc.240653
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
摘要
Abstract
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 and σ,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Key words
protection measurement circuit/error assessment/recursive principal component analysis/grey wolf optimization/extreme learning machine引用本文复制引用
李振兴,龚世玉..基于RPCA-GELM数据驱动的保护测量回路误差评估[J].电力系统保护与控制,2025,53(8):24-33,10.基金项目
This work is supported by the National Natural Science Foundation of China(No.52077120). 国家自然科学基金项目资助(52077120) （No.52077120）
