基于残差图卷积深度网络的电网无功储备需求快速计算方法  

Fast Calculation Method for Grid Reactive Power Reserve Demand Based on Residual Graph Convolutional Deep Network

作　　者：陈光宇 袁文辉 徐晓春 戴则梅[3] 闪鑫[3] Chen Guangyu;Yuan Wenhui;Xu Xiaochun;Dai Zemei;Shan Xin(School of Electric Power Engineering Nanjing Institute of Technology,Nanjing 211167 China;Huai'an Power Supply Branch of State Grid Jiangsu Electric Power Co.Ltd,Huan’an 223021 China;Nanrui Group Co.Ltd State Grid Electric Power Research Institute,Nanjing 211167 China)

机构地区：[1]南京工程学院电力工程学院,南京211167 [2]国网江苏省电力有限公司淮安供电分公司,淮安223021 [3]南瑞集团公司(国网电力科学研究院),南京211167

出　　处：《电工技术学报》2023年第17期4683-4700,共18页Transactions of China Electrotechnical Society

基　　金：智能电网保护和运行控制国家重点实验室资助(SGNR0000KJJS2302148)。

摘　　要：针对电网无功储备需求计算复杂度高、耗时长的问题,提出一种基于残差图卷积深度网络考虑冗余样本特征削减的电网无功储备需求快速计算方法。该文首先,给出一种基于深度学习的电网无功储备需求快速计算框架,采用残差图卷积深度神经网络(GCNII)对电网无功储备需求计算进行建模;其次,为克服传统相似性计算方法在拓扑属性样本度量问题上的局限,提出一种双尺度相似性度量方法,基于矩阵奇异值序列的余弦距离实现对拓扑结构样本的相似性度量;最后,提出一种冗余样本削减策略,基于双尺度相似性度量方法,结合改进谱聚类算法实现对样本集合的分层聚类,并通过样本局部密度分析,实现在维持数据集特征多样性的情况下,对冗余样本进行有效削减,提升模型训练效率。所提算例采用IEEE标准节点系统进行仿真,计算结果表明,该方法能够实现在模型计算精度基本不变的情况下大幅提升模型训练效率。Reactive power reserve plays a crucial role in maintaining voltage stability of power grid.Considering that the uncertainty of new energy output shortens the analysis period of reactive power reserve demand and gradually changes from offline calculation to online evaluation, traditional reactive power reserve demand analysis methods have the problems of high computational complexity and long time consumption. As a result, the calculation of reactive power reserve requirements cannot meet the requirements of online evaluation. To solve these problems, this paper proposes a fast calculation method of grid reactive power reserve demand based on residual graph convolution deep network considering redundant sample reduction. The sample reduction technology and deep learning technology are effectively combined to realize the fast calculation of grid reactive power reserve demand. Firstly, a fast grid reactive power reserve calculation framework based on deep learning is proposed, and residual graph convolutional neural network (GCNII) is used to model the grid reactive power reserve demand calculation. Secondly, aiming at the limitation of traditional similarity calculation methods in topological attribute sample measurement, a two-scale similarity measurement method was constructed based on feature similarity measure and topological similarity measure. Thirdly, the improved spectral clustering algorithm and densitometric analysis method are combined to deeply mine the redundant data with high similarity and dense distribution in the sample set, and the redundant data are reduced, so as to greatly improve the model training efficiency while ensuring the accuracy of model calculation. Finally, the reduced data sets are used to train and test the deep learning model, and the reactive power reserve requirements of the power grid are rapidly calculated.. The simulation results on IEEE standard system show that the calculation results of the model on the training set and the test set are close to the target value, indicatin

关 键 词：残差图卷积神经网络 无功储备需求计算 样本削减策略 矩阵奇异值序列 双尺度相似性 

分 类 号：TM734[电气工程—电力系统及自动化]