继电保护系统的状态评估与风险评估

State Evaluation and Risk Assessment for Relay Protection System

为提升继电保护系统评估准确性以及评估方法的适应性,提出了一种基于半监督的马氏距离机器学习算法的继电保护系统风险评估模型。首先,针对智能变电站的网络拓扑,对继电保护系统评价指标进行了分析;其次,在层次分析法的基础上,将评价结果作为训练集进行模糊综合评价,从准确性、处理时间和可行性方面对比有监督的多元回归分析算法、无监督的k-means算法与文章提出的新算法的性能。对比结果表明,提出的算法评估准确性能比另外两种算法好,且适应能力更强。

In order to improve the evaluation accuracy of the relay protection system and the adaptability of the evaluation method,a risk evaluation model of the relay protection system based on the semi-supervised Mahalanobis distance machine learning algorithm is proposed.First,according to the network topology of the smart substation,the evaluation indicators of the relay protection system are analyzed;second,on the basis of the analytic hierarchy process,the evaluation results are used as the training set for fuzzy comprehensive evaluation.The performance of supervised multiple regression analysis algorithm and unsupervised k-means algorithm is compared with that of the new algorithm proposed in this paper in terms of accuracy,processing time and feasibility.The comparison results show that the proposed algorithm has better evaluation accuracy and stronger adaptability compared to the other two algorithms.