基于WOA-SVM的智能变电站二次系统故障参数映射模型

A WOA-based fault parameter mapping model for the secondary systems of intelli⁃gent substations

目前通过人工分析大量运行信息难以快速精确定位智能变电站二次系统故障,无法适应智能电网高可靠性运行要求。为此,提出一种智能变电站二次系统故障参数映射模型。首先根据特征信息建立故障定位推理知识库,对故障类型进行编码。然后基于智能变电站的历史运行数据构建模型训练集,引入多分类器对SVM(支持向量机)算法进行改进,用WOA(鲸鱼优化算法)优化其参数,以设备状态为输入,二次系统故障类型为输出,建立了智能变电站二次系统设备参数与运行状态之间的映射关系。最后以实际数据作为测试集对所提模型进行检验,证明了此故障参数映射模型的有效性。

Currently,it is challenging to rapidly and precisely locate faults in the secondary systems of intelligent substations through manual analysis of extensive operational data,making it inadequate for meeting the high reliabil⁃ity requirements of smart grids.To address this issue,a fault parameter mapping model for the secondary systems of intelligent substations is proposed.Firstly,a knowledge base for fault localization reasoning is established based on characteristic information to encode fault types.Subsequently,utilizing historical operational data from intelligent substations,a training set for the model is constructed.The support vector machine(SVM)is enhanced by introduc⁃ing a multi-classifier approach,and its parameters are optimized using the whale optimization algorithm(WOA).By taking equipment status as input and secondary system fault types as output,a mapping relationship is established between the parameters of secondary system equipment of intelligent substations and the operational state.Finally,the proposed model is validated using actual data as a test set,demonstrating the effectiveness of the proposed model.