基于GA-BP神经网络的能源互联网窃电行为识别方法

An Identification Method of Electicity Theft on Energy Internet Based on GA-BP Neural Network

伴随着我国能源互联网战略的不断推进,如何减少窃电行为的发生成为研究的焦点问题。首先,对用户用电的历史数据进行处理和分析,并对用电数据进行处理和转换,实现用户用电数据的整合;然后,通过对用户用电数据的分析,挖掘出窃漏电行为的关键特征指标,提出了基于GA-BP神经网络窃漏电用户的识别方法;最后,利用电力系统实时运行数据和窃漏电用户识别方法实现窃漏电行为的识别和诊断。通过电力系统实际运行数据分析验证了方法的合理性和有效性,提高了输电线路的稳定运行,保障能源互联网的安全运行。

With the continuous advancement of China’s energy Internet strategy,how to reduce the occurrence of electricity theft has become a focus of research. The historical data of electricity consumption was processed and analyzed,and the data of electricity consumption were processed and converted to realize the integration of the data of electricity consumption. Then,through the anticipating analysis of users’ electricity usage data,the key characteristic indicators of electricity theft behavior were excavated and a identification method of electricity theft users based on GA-BP neural network was proposed. Finally,the real-time operation data of power system and the user identification method were used to realize the identification and diagnosis of electricity theft behavior. The rationality and effectiveness of the method were verified by analyzing the actual operation data of the power system,which improved the stable operation of the transmission line and ensured the safe operation of the energy Internet.