Fast Calculation Method for Grid Reactive Power Reserve Demand Based on Residual Graph Convolutional Deep Network
Chen Guangyu1, Yuan Wenhui1, Xu Xiaochun2, Dai Zemei3, Shan Xin3
1. School of Electric Power Engineering Nanjing Institute of Technology Nanjing 211167 China; 2. Huai 'an Power Supply Branch of State Grid Jiangsu Electric Power Co. Ltd Huan'an 223021 China; 3. Nanrui Group Co. Ltd State Grid Electric Power Research Institute Nanjing 211167 China
Abstract：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, indicating that the residual graph convolution model has strong generalization ability. Secondly, by comparing the GCNII model with the GCN model at different depths, it can be found that the deep GCNII model has better calculation accuracy, which verifies that the GCNII model can effectively solve the excessive smoothing problem of the GCN model. Finally, the sample reduction strategy is used to effectively process the dataset, and the sample sets before and after the reduction are used to train and calculate the model respectively. It is verified that the sample reduction strategy can greatly improve the model training efficiency while ensuring the model calculation accuracy. The following conclusions can be drawn from the simulation analysis: (1) Compared with the traditional machine learning model, the GCNII model has higher calculation accuracy, and its mean absolute error (MAE) is 1.777 and 2.779 lower than CNN and SVR models, respectively, indicating that the GCNII model has obvious advantages in reactive power reserve requirement calculation task. (2) The sample reduction strategy can improve the model training efficiency by more than 15%, and the mean absolute error (MAE) of the calculation results is less than 5%, which can greatly improve the model training efficiency while keeping the model calculation accuracy basically unchanged. (3) The simulation results in different node size systems show that the proposed method can effectively improve the model training efficiency of different node sizes, indicating that the proposed method has good universality.
陈光宇, 袁文辉, 徐晓春, 戴则梅, 闪鑫. 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[J]. 电工技术学报, 2023, 38(17): 4683-4700.
Chen Guangyu, Yuan Wenhui, Xu Xiaochun, Dai Zemei, Shan Xin. Fast Calculation Method for Grid Reactive Power Reserve Demand Based on Residual Graph Convolutional Deep Network. Transactions of China Electrotechnical Society, 2023, 38(17): 4683-4700.
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2023, Vol. 38 
