一种配电自动化系统状态操作评价方法

An evaluation method for state operation of distribution automation system

随着智能配电网的发展,配电自动化系统投运日益增加,迫切需要对配电自动化系统的状态操作情况进行综合评价。研究在建立配电自动化系统状态操作评价分层指标体系的基础上,首先对指标数据进行聚类分析,以训练决策树指标分类器,便于自动分析状态操作情况的不同类别模式。然后对各指标应用熵权法进行赋权,利用该加权结果和指标数据构造综合评价的训练样本集。最后运用多元回归算法学习建立每类模式下配电自动化系统状态操作的评估模型。实验结果表明,采用上述方法构建的评价模型能较客观反映配电自动化系统在相应时间断面的综合状态操作水平,且比传统神经网络模型准确度更高,可为配电自动化系统的运行管理提供有效评价手段和决策依据。

With the development of smart distribution network,the distribution automation system（DAS）is increasing rapidly.It is urgent to evaluate the state operation performance of DAS comprehensively.Based on the establishment of hierarchical evaluation index system for the performance,the indexes are clustered to train the index classifier by decision tree algorithm to analyze different types of the state operation automatically.Then,we use the entropy method to give weight to each index,and constructe the training sample set of comprehensive evaluation by using the weighted results and the original data of the indexes.Finally,we use the multiple regression algorithm to train the evaluation model for each state operation category.As shown by the experimental results,the evaluation model can objectively reflect the comprehensive state operation performance of DAS at the corresponding time section.Moreover,it obtains higher accuracy than the traditional neural network evaluation model and provides effective evaluation method and decision supporting for the operation management of DAS.