Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power systems.The wide deployment of phasor measurement units(PMUs)promotes t...Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power systems.The wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS prediction.This paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU data.The grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power grid.Both the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are considered.Case studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN model.Results show that the TTEDNN model performs best among existing deep learning models.Furthermore,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.展开更多
含高比例新能源交直流混联电网的稳定特性已发生深刻变化,功角稳定依然是威胁系统安全运行的关键问题,相关研究对标准算例的真实性、合理性及代表性提出更高的要求。该文根据实际电网拓扑和数据,构建适用于功角稳定特性研究的功角稳定...含高比例新能源交直流混联电网的稳定特性已发生深刻变化,功角稳定依然是威胁系统安全运行的关键问题,相关研究对标准算例的真实性、合理性及代表性提出更高的要求。该文根据实际电网拓扑和数据,构建适用于功角稳定特性研究的功角稳定机电暂态仿真算例(Chinese society for electricalengineering-rotoranglestability,CSEE-RAS),该系统以500kV为主网架,包含2个区域、1个交流通道、1个直流通道。提供2种运行方式,分别对应动态、暂态功角稳定场景,上述场景新能源出力占比均在50%以上。考虑新能源出力占比、机组接入位置和控制策略等因素,量化不同因素对稳定水平的影响。敏感性分析结果表明,该算例较为全面地反映了机电暂态尺度下的不同功角稳定特性,且具有灵活的拓展能力,可为功角稳定分析与控制的相关研究提供基础平台,有助于不同结论的横向比较和研究人员科研效率的提升。展开更多
基金supported in part by the National Natural Science Foundation of China(No.21773182)the HPC Platform,Xi’an Jiaotong University。
文摘Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power systems.The wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS prediction.This paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU data.The grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power grid.Both the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are considered.Case studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN model.Results show that the TTEDNN model performs best among existing deep learning models.Furthermore,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.
文摘含高比例新能源交直流混联电网的稳定特性已发生深刻变化,功角稳定依然是威胁系统安全运行的关键问题,相关研究对标准算例的真实性、合理性及代表性提出更高的要求。该文根据实际电网拓扑和数据,构建适用于功角稳定特性研究的功角稳定机电暂态仿真算例(Chinese society for electricalengineering-rotoranglestability,CSEE-RAS),该系统以500kV为主网架,包含2个区域、1个交流通道、1个直流通道。提供2种运行方式,分别对应动态、暂态功角稳定场景,上述场景新能源出力占比均在50%以上。考虑新能源出力占比、机组接入位置和控制策略等因素,量化不同因素对稳定水平的影响。敏感性分析结果表明,该算例较为全面地反映了机电暂态尺度下的不同功角稳定特性,且具有灵活的拓展能力,可为功角稳定分析与控制的相关研究提供基础平台,有助于不同结论的横向比较和研究人员科研效率的提升。
