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

doi:10.19595/j.cnki.1000-6753.tces.221019

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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.

Key words： Residual graph convolutional neural network reactive power reserve demand calculation sample reduction strategy matrix singular value sequence two scale similarity

Received: 02 June 2022

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	Chen Guangyu
	Yuan Wenhui
	Xu Xiaochun
	Dai Zemei
	Shan Xin

Cite this article:

Chen Guangyu,Yuan Wenhui,Xu Xiaochun等. Fast Calculation Method for Grid Reactive Power Reserve Demand Based on Residual Graph Convolutional Deep Network[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4683-4700.

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[1] 吴浩, 郭瑞鹏, 甘德强, 等. 发电机有效无功储备的分析和计算[J]. 电力系统自动化, 2011, 35(15): 13-17, 39.
Wu Hao, Guo Ruipeng, Gan Deqiang, et al.Analysis and computation of effective reactive power reserve of generators[J]. Automation of Electric Power Systems, 2011, 35(15): 13-17, 39.
[2] 赵晋泉, 居俐洁, 罗卫华, 等. 计及分区动态无功储备的无功电压控制模型与方法[J]. 电力自动化设备, 2015, 35(5): 100-105.
Zhao Jinquan, Ju Lijie, Luo Weihua, et al.Reactive voltage control model and method considering partitioned dynamic reactive power reserve[J]. Electric Power Automation Equipment, 2015, 35(5): 100-105.
[3] 亢丽君, 王蓓蓓, 薛必克, 等. 计及爬坡场景覆盖的高比例新能源电网平衡策略研究[J]. 电工技术学报, 2022, 37(13): 3275-3288.
Kang Lijun, Wang Beibei, Xue Bike, et al.Research on the balance strategy for power grid with high proportion renewable energy considering the ramping scenario coverage[J]. Transactions of China Electrotechnical Society, 2022, 37(13): 3275-3288.
[4] 柯德平, 冯帅帅, 刘福锁, 等. 新能源发电调控参与的送端电网直流闭锁紧急频率控制策略快速优化[J]. 电工技术学报, 2022, 37(5): 1204-1218.
Ke Deping, Feng Shuaishuai, Liu Fusuo, et al.Rapid optimization for emergent frequency control strategy with the power regulation of renewable energy during the loss of DC connection[J]. Transactions of China Electrotechnical Society, 2022, 37(5): 1204-1218.
[5] 齐子杰, 陈天华, 王彬, 等. 基于多阶轨迹灵敏度的交直流混联受端电网无功储备优化方法[J]. 电力科学与技术学报, 2022, 37(1): 74-81.
Qi Zijie, Chen Tianhua, Wang Bin, et al.Reactive power reserve optimization method for AC/DC hybrid receiving network based on multi-order trajectory sensitivity[J]. Journal of Electric Power Science and Technology, 2022, 37(1): 74-81.
[6] 陈光宇, 吴文龙, 戴则梅, 等. 计及故障场景集的风光储混合系统区域无功储备多目标优化[J]. 电力系统自动化, 2022, 46(17): 194-204
Chen Guangyu, Wu Wenlong, Dai Zemei, et al.Multi-objective optimization of regional reactive power reserve in hybrid system with wind/photovoltaic/ energy storage considering fault scenario set[J]. Automation of Electric Power Systems, 2022, 46(17): 194-204
[7] Leonardi B, Ajjarapu V.An approach for real time voltage stability margin control via reactive power reserve sensitivities[J]. IEEE Transactions on Power Systems, 2013, 28(2): 615-625.
[8] 熊虎岗, 程浩忠, 徐敬友. 考虑提高系统无功备用容量的无功优化调度[J]. 电网技术, 2006, 30(23): 36-40.
Xiong Hugang, Cheng Haozhong, Xu Jingyou.Optimal dispatch of reactive power considering increase of system reactive power reserve[J]. Power System Technology, 2006, 30(23): 36-40.
[9] 苗长新, 李昊, 王霞, 等. 基于数据驱动和深度学习的超短期风电功率预测[J]. 电力系统自动化, 2021, 45(14): 22-29.
Miao Changxin, Li Hao, Wang Xia, et al.Data-driven and deep-learning-based ultra-short-term wind power prediction[J]. Automation of Electric Power Systems, 2021, 45(14): 22-29.
[10] 姚程文, 杨苹, 刘泽健. 基于CNN-GRU混合神经网络的负荷预测方法[J]. 电网技术, 2020, 44(9): 3416-3424.
Yao Chengwen, Yang Ping, Liu Zejian.Load forecasting method based on CNN-GRU hybrid neural network[J]. Power System Technology, 2020, 44(9): 3416-3424.
[11] 罗澍忻, 麻敏华, 蒋林, 等. 考虑多时间尺度数据的中长期负荷预测方法[J]. 中国电机工程学报, 2020, 40(增刊1): 11-19.
Luo Shuxin, Ma Minhua, Jiang Lin, et al.Medium and long-term load forecasting method considering multi-time scale data[J]. Proceedings of the CSEE, 2020, 40(S1): 11-19.
[12] 刘亚珲, 赵倩. 基于聚类经验模态分解的CNN-LSTM超短期电力负荷预测[J]. 电网技术, 2021, 45(11): 4444-4451.
Liu Yahui, Zhao Qian.Ultra-short-term power load forecasting based on cluster empirical mode decomposition of CNN-LSTM[J]. Power System Technology, 2021, 45(11): 4444-4451.
[13] 杨东升, 吉明佳, 周博文, 等. 基于双生成器生成对抗网络的电力系统暂态稳定评估方法[J]. 电网技术, 2021, 45(8): 2934-2945.
Yang Dongsheng, Ji Mingjia, Zhou Bowen, et al.Transient stability assessment of power system based on DGL-GAN[J]. Power System Technology, 2021, 45(8): 2934-2945.
[14] Wu Zonghan, Pan Shirui, Chen Fengwen, et al.A comprehensive survey on graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1): 4-24.
[15] Bruna J, Zaremba W, Szlam A, et al. Spectral networks and locally connected networks on graphs[EB/OL].2013, arXiv: 1312.6203. https://arxiv. org/abs/1312.6203.
[16] Zhao Ling, Song Yujiao, Zhang Chao, et al.T-GCN: a temporal graph convolutional network for traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(9): 3848-3858.
[17] Cui Zhiyong, Henrickson K, Ke Ruimin, et al.Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(11): 4883-4894.
[18] Liu Yabo, Liu Yi, Yang Cheng.Modulation recognition with graph convolutional network[J]. IEEE Wireless Communications Letters, 2020, 9(5): 624-627.
[19] Ding Yun, Guo Yuanyuan, Chong Yanwen, et al.Global consistent graph convolutional network for hyperspectral image classification[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-16.
[20] Song Tengfei, Zheng Wenming, Song Peng, et al.EEG emotion recognition using dynamical graph convolutional neural networks[J]. IEEE Transactions on Affective Computing, 2020, 11(3): 532-541.
[21] 王铮澄, 周艳真, 郭庆来, 等. 考虑电力系统拓扑变化的消息传递图神经网络暂态稳定评估[J]. 中国电机工程学报, 2021, 41(7): 2341-2350.
Wang Zhengcheng, Zhou Yanzhen, Guo Qinglai, et al.Transient stability assessment of power system considering topological change: a message passing neural network-based approach[J]. Proceedings of the CSEE, 2021, 41(7): 2341-2350.
[22] 李佳玮, 王小君, 和敬涵, 等. 基于图注意力网络的配电网故障定位方法[J]. 电网技术, 2021, 45(6): 2113-2121.
Li Jiawei, Wang Xiaojun, He Jinghan, et al.Distribution network fault location based on graph attention network[J]. Power System Technology, 2021, 45(6): 2113-2121.
[23] 王渝红, 沈靖, 曾琦, 等. 基于谱图论和图卷积神经网络的直流电网节点电压估计研究[J]. 电网技术, 2022, 46(2): 521-532.
Wang Yuhong, Shen Jing, Zeng Qi, et al.Voltage estimation for DC grid nodes based on spectral theory and graph convolutional neural network[J]. Power System Technology, 2022, 46(2): 521-532.
[24] 陈光宇, 孙叶舟, 江海洋, 等. 基于DIndRNN-RVM深度融合模型的AGC指令执行效果精准辨识及置信评估研究[J]. 中国电机工程学报, 2022, 42(5): 1852-1867.
Chen Guangyu, Sun Yezhou, Jiang Haiyang, et al.Research on accurate identification and confidence evaluation of AGC command execution effect based on DIndRNN-RVM deep fusion model[J]. Proceedings of the CSEE, 2022, 42(5): 1852-1867.
[25] Jahangir H, Tayarani H, Baghali S, et al.A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks[J]. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2369-2381.
[26] 郑吉祥, 钟俊. 基于节点类型和分区耦合性的复杂网络无功电压快速分区方法[J]. 电网技术, 2020, 44(1): 223-230.
Zheng Jixiang, Zhong Jun.A complex network theory fast partition algorithm of reactive voltage based on node type and coupling of partitions[J]. Power System Technology, 2020, 44(1): 223-230.
[27] 张怡, 张恒旭, 李常刚, 等. 深度学习在电力系统频率分析与控制中的应用综述[J]. 中国电机工程学报, 2021, 41(10): 3392-3406, 3665.
Zhang Yi, Zhang Hengxu, Li Changgang, et al.Review on deep learning applications in power system frequency analysis and control[J]. Proceedings of the CSEE, 2021, 41(10): 3392-3406, 3665.
[28] 徐冰冰, 岑科廷, 黄俊杰, 等. 图卷积神经网络综述[J]. 计算机学报, 2020, 43(5): 755-780.
Xu Bingbing, Cen Keting, Huang Junjie, et al.A survey on graph convolutional neural network[J]. Chinese Journal of Computers, 2020, 43(5): 755-780.
[29] Mou Lichao, Lu Xiaoqiang, Li Xuelong, et al.Nonlocal graph convolutional networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(12): 8246-8257.
[30] David K, Hammond A,Pierre Vandergheynst B,et al.Wavelets on graphs via spectral graph theory[J]. Applied and Computational Harmonic Analysis, 2011, 30(2): 129-150.
[31] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[C]//ICLR, 2016, abs/1609.02907.
[32] 张翼, 朱永利. 图信号与图卷积网络相结合的局部放电模式识别方法[J]. 中国电机工程学报, 2021, 41(18): 6472-6481.
Zhang Yi, Zhu Yongli.A partial discharge pattern recognition method combining graph signal and graph convolutional network[J]. Proceedings of the CSEE, 2021, 41(18): 6472-6481.
[33] Chen Ming, Wei Zhewei, Huang Zengfeng, et al.Simple and deep graph convolutional networks[EB/ OL]. 2020, arXiv: 2007.02133. https://arxiv.org/abs/ 2007.02133.
[34] 徐胜蓝, 司曹明哲, 万灿, 等. 考虑双尺度相似性的负荷曲线集成谱聚类算法[J]. 电力系统自动化, 2020, 44(22): 152-160.
Xu Shenglan, Si Caomingzhe, Wan Can, et al.Ensemble spectral clustering algorithm for load profiles considering dual-scale similarities[J]. Automation of Electric Power Systems, 2020, 44(22): 152-160.
[35] 林君豪, 张焰, 赵腾, 等. 基于改进卷积神经网络拓扑特征挖掘的配电网结构坚强性评估方法[J]. 中国电机工程学报, 2019, 39(1): 84-96, 323.
Lin Junhao, Zhang Yan, Zhao Teng, et al.Structure strength assessment method of distribution network based on improved convolution neural network and network topology feature mining[J]. Proceedings of the CSEE, 2019, 39(1): 84-96, 323.
[36] 姜雅男, 于永进, 李长云. 基于改进TOPSIS模型的绝缘纸机-热老化状态评估方法[J]. 电工技术学报, 2022, 37(6): 1572-1582.
Jiang Yanan, Yu Yongjin, Li Changyun.Thermal aging state evaluation method of insulated paper machine based on improved TOPSIS model[J]. Transactions of China Electrotechnical Society, 2022, 37(6): 1572-1582.
[37] 林顺富, 田二伟, 符杨, 等. 基于信息熵分段聚合近似和谱聚类的负荷分类方法[J]. 中国电机工程学报, 2017, 37(8): 2242-2253.
Lin Shunfu, Tian Erwei, Fu Yang, et al.Power load classification method based on information entropy piecewise aggregate approximation and spectral clustering[J]. Proceedings of the CSEE, 2017, 37(8): 2242-2253.
[38] 王卓, 王玉静, 王庆岩, 等. 基于协同深度学习的二阶段绝缘子故障检测方法[J]. 电工技术学报, 2021, 36(17): 3594-3604.
Wang Zhuo, Wang Yujing, Wang Qingyan, et al.Two stage insulator fault detection method based on collaborative deep learning[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3594-3604.
[39] 陈立帆, 张琳琳, 宋辉, 等. 基于图卷积神经网络的输电线路自然灾害事故预测[J/OL]. 电网技术, 2022, DOI: 10.13335/j.1000-3673.pst.2021.2520.
Chen Lifan, Zhang Linlin, Song Hui, et al.Natural disaster accident prediction of transmission line based on graph convolution network[J/OL]. Power System Technology, 2022, DOI: 10.13335/j.1000-3673.pst. 2021.2520.
[40] Oono K, Suzuki T. Graph neural networks exponentially lose expressive power for node classification[EB/OL].2019, arXiv: 1905.10947. https://arxiv.org/abs/1905.10947.