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基于门控图神经网络的高容错配电网状态估计方法 被引量:1

High fault-tolerant distribution network state estimation method based on gated graph neural network
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摘要 随着电力系统可再生能源渗透率的逐渐提升,为应对可再生能源的间歇性特点并确保电网安全运行,对配电网进行实时、准确、高容错的状态估计至关重要。针对配电网量测装置装配水平不完备、模型驱动状态估计对高不确定性环境适应性不足的问题,基于数据采集与监视控制(SCADA)系统和相量测量单元(PMU)获得的多个时间尺度的混合量测,提出了一种基于门控图神经网络(GGNN)的高容错配电网状态估计方法,利用图卷积层和类门控循环单元提取量测时空高维特征,挖掘量测量与状态量的时空与因果关系。最后基于IEEE 33节点系统与IEEE118节点系统进行了仿真验证,结果表明,GGNN能有效拟合配电网量测量到状态量的时空映射,相比传统最小二乘法和多层感知机具有更高的精度和容错性。 With the increase of renewable energy's penetration rate in power systems,it is essential to make real-time,accurate and highly fault-tolerant state estimations for distribution networks to cope with the intermittency of renewable energy and keep safe operation of the power grid.Since the assembly level of distribution network measurement devices is incomplete and the model-driven state estimation can hardly adapt to the high uncertainty of environment,the fused measurement data from SCADA/PMU is adopted to train the gated graph neural network(GGNN).Then,a high faulttolerant distribution network state estimation method based on GGNN is proposed.It can obtain the spatio-temporal relationship between the measurement and the state estimation by using the graph convolutional layer and GRU-like to extract high-dimensional spatio-temporal features of the measurement.The proposed algorithmic solution is assessed and validated based on an IEEE 33-bus system and an IEEE 118-bus system,respectively.The assessment result show that GGNN can effectively fit the space-time mapping of measurement and state data with a higher accuracy and robustness compared with the traditional Weighted Least Squares(WLS)and Multi-layer Perceptron(MLP).
作者 刘艺娴 王玉彬 杨强 LIU Yixian;WANG Yubin;YANG Qiang(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;Polytechnic Institute,Zhejiang University,Hangzhou 310027,China)
出处 《综合智慧能源》 CAS 2023年第6期1-8,共8页 Integrated Intelligent Energy
基金 国家自然科学基金项目(52177119)。
关键词 可再生能源 配电网 状态估计 高容错 融合量测 门控图神经网络 最小二乘法 多层感知机 renewable energy distribution network state estimation high fault tolerance fused measurement gated graph neural network least squares method multi-layer perceptron
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