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
This study presents a comprehensive impact analysis of the rotor angle stability of a proposed international connection between the Philippines and Sabah,Malaysia,as part of the Association of Southeast Asian Nations(...This study presents a comprehensive impact analysis of the rotor angle stability of a proposed international connection between the Philippines and Sabah,Malaysia,as part of the Association of Southeast Asian Nations(ASEAN)Power Grid.This study focuses on modeling and evaluating the dynamic performance of the interconnected system,considering the high penetration of renewable sources.Power flow,small signal stability,and transient stability analyses were conducted to assess the ability of the proposed linked power system models to withstand small and large disturbances,utilizing the Power Systems Analysis Toolbox(PSAT)software in MATLAB.All components used in the model are documented in the PSAT library.Currently,there is a lack of publicly available studies regarding the implementation of this specific system.Additionally,the study investigates the behavior of a system with a high penetration of renewable energy sources.Based on the findings,this study concludes that a system is generally stable when interconnection is realized,given its appropriate location and dynamic component parameters.Furthermore,the critical eigenvalues of the system also exhibited improvement as the renewable energy sources were augmented.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This study presents a comprehensive impact analysis of the rotor angle stability of a proposed international connection between the Philippines and Sabah,Malaysia,as part of the Association of Southeast Asian Nations(ASEAN)Power Grid.This study focuses on modeling and evaluating the dynamic performance of the interconnected system,considering the high penetration of renewable sources.Power flow,small signal stability,and transient stability analyses were conducted to assess the ability of the proposed linked power system models to withstand small and large disturbances,utilizing the Power Systems Analysis Toolbox(PSAT)software in MATLAB.All components used in the model are documented in the PSAT library.Currently,there is a lack of publicly available studies regarding the implementation of this specific system.Additionally,the study investigates the behavior of a system with a high penetration of renewable energy sources.Based on the findings,this study concludes that a system is generally stable when interconnection is realized,given its appropriate location and dynamic component parameters.Furthermore,the critical eigenvalues of the system also exhibited improvement as the renewable energy sources were augmented.