含分布式光伏的新型电力系统负荷数据特征增强方法  

NEW POWER SYSTEM LOAD DATA FEATURE ENHANCEMENT METHOD WITH DISTRIBUTED PHOTOVOLTAICS

作　　者：回茜 刘俊宇 刘鑫 李长彧 宋晓文 Hui Qian;Liu Junyu;Liu Xin;Li Changyu;Song Xiaowen(Marketing Service Center of State Grid Liaoning Electric Power Co.,Ltd.,Shenyang 110015,China)

机构地区：[1]国网辽宁省电力有限公司营销服务中心,沈阳110015

出　　处：《太阳能学报》2025年第4期240-247,共8页Acta Energiae Solaris Sinica

基　　金：国网辽宁省电力有限公司管理科技项目(2023YF-157)。

摘　　要：针对含分布式光伏的新型电力系统异常用电事件检测难度大、异常用电行为特征复杂、难以满足新型电力系统对样本数据质量要求的问题,提出一种深度循环神经网络-改进生成对抗网络(DRNN-WGAN)模型的新型电力系统负荷窃电行为对应的数据特征增强方法。首先,针对电力用户窃电导致数据存在异常值的问题,对分布式光伏的用户窃电以及纯负荷用户窃电导致的数据异常情况进行分析。其次,通过Wasserstein距离对生成对抗网络进行改进,利用生成对抗网络模型中对新型电力系统负荷异常用电数据进行生成和判别,建立新型电力系统负荷异常用电样本的生成对抗网络模型。然后,提出DRNNWGAN网络的新型电力系统异常用电数据增强方法,并以LightGBM算法进行电网负荷数据分类与检测。最后,以辽宁省某地含高比例新能源的配电网数据进行仿真,仿真结果表明,与其他数据增强方法进行对比,该文提出的基于DRNN-WGAN网络的新型电力系统异常用电数据增强方法可加快模型的收敛速度,提升电网数据质量和检测精度。It is difficult to detect abnormal power consumption events in the new power system with distributed photovoltaic,and the characteristics of abnormal power consumption behavior are complex,which is difficult to meet the requirements of the new power system for sample data quality.A new data feature enhancement method for load stealing behavior of power system based on deep recurrent neural network-improved generative adversarial network(DRNN-WGAN)model is proposed.Firstly,aiming at the problem of abnormal data caused by power stealing by power users,the abnormal data caused by power stealing by distributed photovoltaic users and power stealing by pure load users are analyzed.Secondly,the generative adversarial network is improved by Wasserstein distance.The generative adversarial network model is used to generate and discriminate the abnormal power consumption data of the new power system load,and the generative adversarial network model of the new power system load abnormal power consumption sample is established.Then,a new power system abnormal electricity data enhancement method based on DRNN-WGAN network is proposed,and the LightGBM algorithm is used to classify and detect the power grid load data.Finally,the simulation is carried out with the data of a distribution network with high proportion of new energy in Liaoning Province.The simulation results show that compared with other data enhancement methods,the new power system abnormal electricity data enhancement method based on DRNN-WGAN network proposed in this paper can accelerate the convergence speed of the model and improve the data quality and detection accuracy of the power grid.

关 键 词：分布式光伏 生成对抗网络 异常用电 新型电力系统 Wasserstein距离 数据增强 

分 类 号：TK73[交通运输工程—轮机工程]