基于平均影响值-启发式前向搜索的异常光伏用户识别方法

Identification method of abnormal photovoltaic users based on mean impact value and heuristic forward searching

随着国家对光伏产业的大力推进与扶持,以及国家补贴政策具有长期性,出现了很多不法用户以虚假记录发电量骗取国家补贴的行为。针对现有的分布式光伏防窃电技术,提出了一种基于平均影响值(MIV)-启发式前向搜索的异常光伏用户识别方法。通过获取同一地区的标杆光伏用户及其他光伏用户在同一时段的发电数据,利用原始数据训练BP神经网络,再根据MIV的计算原理构造2组新的训练样本,用新样本的仿真结果计算各光伏用户的MIV,结合启发式前向搜索算法筛选得到与标杆光伏用户发电数据关联性大的用户,未被筛选的用户就是异常光伏用户。仿真结果验证了所提方法对异常光伏用户识别的有效性。

With the country’s vigorous promotion and support of the photovoltaic industry and the long-term nature of the national subsidy policy,many illegal users cheat the state subsidy by falsely recording the electricity generation.Aiming at the existing distributed photovoltaic anti-theft technologies,an identification method of abnormal photovoltaic users based on MIV(Mean Impact Value)and heuristic forward searching is proposed.By obtaining the power generation data of benchmark photovoltaic users and other photovoltaic users in the same time segment and the same area,the original data are used to train the BP neural network.And then according to the MIV calculation principle,two sets of new training samples are constructed,and the simulative results of new samples are used to calculate the MIV of each photovoltaic user.Combining heuristic forward searching algorithm,the users with high power generation data correlation with the benchmark photovoltaic users are filtered,and the unfiltered users are abnormal photovoltaic users.The simulative results verify the effectiveness of the proposed method for identifying abnormal photovoltaic users.