基于深度神经网络-近似线性网络混合模型的电力系统状态估计方法  

Power System State Estimation Method Based on Deep Neural Network-Proline Network Hybrid Model

作　　者：宋雨露 樊艳芳[1] 刘雨佳 刘牧阳 白雪岩 SONG Yu-lu;FAN Yan-fang;LIU Yu-jia;LIU Mu-yang;BAI Xue-yan(School of Electrical Engineering,Xinjiang University,Urumqi 830046,China)

机构地区：[1]新疆大学电气工程学院,乌鲁木齐830017

出　　处：《科学技术与工程》2022年第25期11041-11048,共8页Science Technology and Engineering

基　　金：国家自然科学基金(51767023)。

摘　　要：为提高电力系统实时状态估计的精度和计算效率,解决电网电压波动频发、潮流分布的不确定性剧增等问题,通过提出一种基于深度神经网络和近似线性网络模型的电力系统状态估计方法,研究了其在电网的应用。该方法将混合系统量测数据通过粒子滤波算法得到样本集,利用训练样本训练所提出的混合神经网络模型,最后将测试样本输入已建立的模型中获得系统状态的估计结果。通过IEEE118节点系统进行的负载数据仿真实验表明:基于混合神经网络模型的电力系统状态估计方法不仅能快速进行海量数据训练,还能有效避免过拟合;在实时状态估计的精度和计算效率方面,相较于高斯-牛顿法均有提高。可见所提方法在电力系统实时状态估计方面具有一定的应用价值。In order to improve the accuracy and computational efficiency of power system real-time state estimation,solve the problems of frequent,voltage fluctuations in grids and sharp increase in the uncertainty of power flow distribution in grids,a power system state estimation method based on deep neural networks(DNN)and approximately linear proline networks(PN)models were proposed,and its application in the power grids was researched.The mixed system measurement data was obtained through a particle filter algorithm to acquire a sample set,the training sample was used to train the proposed hybrid model,and finally the test samples were input into the established model to obtain the estimation result of the system state.Simulation result on the load data in IEEE118 bus system show that the power system state estimation method based on the proposed hybrid model not only allows rapid training for massive data,but also effectively avoids overfitting.The accuracy and computational efficiency of real-time state estimation compared with the Gauss-Newton method are both improved.It can be seen that the proposed method has the application value in real-time state estimation of power systems.

关 键 词：粒子滤波 状态估计 深度神经网络 近似线性网络 

分 类 号：TM721[电气工程—电力系统及自动化]