基于改进支持向量机的智能电能表故障多分类方法

智能电表故障多分类对于制定合理及时的智能电表检修计划具有重要意义。针对智能电表故障多分类问题,采用支持向量机构建多分类模型,所建立的模型提取智能电表的输出电压,输出电流,输出功率,功率因素误差等数据作为分类依据构建多维空间,考虑包括误差超差,直流电流开路,直流电压短路,控制回路短线在内的智能电表模式识别故障分类。通过所建立的模型依据有限的样本信息在复杂性和学习性之间寻求平衡,对智能电表多维度运行信息在超平面之间进行最佳分类从而进行故障分类,通过引入一类对多类的最优分类平面集进行改进从而适用于多分类模型。采用混沌粒子群算法针对所建立的基于改进支持向量机的智能电表故障多分类方法进行求解流程设计。最后通过对某配电台区智能电表故障分类问题采用所建立的模型进行仿真,验证了模型的合理性。

Multi classification method of smart meter fault based on improved support vector machine

Multi classification of smart meter fault is of great significance for making reasonable and timely maintenance plan of smart meter. Aiming at the problem of multi classification of smart meter fault, support vector machine is used to build a multi classification model. The established model extracts the output voltage, output current, output power, power factor error and other data of smart meter as the classification basis to build a multi-dimensional space, including error tolerance, DC current open circuit, DC voltage short circuit, Intelligent meter pattern recognition fault classification including short line of control circuit. According to the limited sample information, the established model seeks the balance between complexity and learning ability, and makes the best classification of multi-dimensional operation information of smart meters between hyperplanes, so as to carry out fault classification. By introducing one class to improve the multi class optimal classification plane set, it is suitable for multi class model. The chaotic particle swarm optimization algorithm is used to design the solution flow of the intelligent meter fault multi classification method based on improved support vector machine. Finally, the model is used to simulate the fault classification of smart meters in a distribution station area, and the rationality of the model is verified.