Transient stability assessment(TSA)based on security region is of great significance to the security of power systems.In this paper,we propose a novel methodology for the assessment of online transient stability margi...Transient stability assessment(TSA)based on security region is of great significance to the security of power systems.In this paper,we propose a novel methodology for the assessment of online transient stability margin.Combined with a geographic information system(GIS)and transformation rules,the topology information and pre-fault power flow characteristics can be extracted by 2 D computer-vision-based power flow images(CVPFIs).Then,a convolutional neural network(CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency set.The network consists of two components:the classification network classifies the input samples into the credibly stable/unstable and uncertain categories,and the prediction network is utilized to further predict the generator stability indices of the categorized samples,which improves the network ability to distinguish between the samples with similar characteristics.The proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system,and the simulation results validate the effectiveness of the method.展开更多
A new transient stability margin is proposed based on a new expression of dynamic security region(DSR)which is developed from the existing expression of DSR.Applications of the DSR based transient stability margin to ...A new transient stability margin is proposed based on a new expression of dynamic security region(DSR)which is developed from the existing expression of DSR.Applications of the DSR based transient stability margin to contingency ranking and screening are discussed.Simulations in the 10-machine 39-bus New England system are performed to show the effectiveness of the proposed DSR based tran-sient stability margin.展开更多
基金supported in part by the National Natural Science Foundation of China(No.51877034)
文摘Transient stability assessment(TSA)based on security region is of great significance to the security of power systems.In this paper,we propose a novel methodology for the assessment of online transient stability margin.Combined with a geographic information system(GIS)and transformation rules,the topology information and pre-fault power flow characteristics can be extracted by 2 D computer-vision-based power flow images(CVPFIs).Then,a convolutional neural network(CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency set.The network consists of two components:the classification network classifies the input samples into the credibly stable/unstable and uncertain categories,and the prediction network is utilized to further predict the generator stability indices of the categorized samples,which improves the network ability to distinguish between the samples with similar characteristics.The proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system,and the simulation results validate the effectiveness of the method.
基金Supported by Chinese National Basic Research Program(Grant No.2004CB217900)the National Natural Science Foundation of China(Grant Nos.50525721,50595411,50707035)China Postdoctoral Science Foundation(Grant No.20060400518)
文摘A new transient stability margin is proposed based on a new expression of dynamic security region(DSR)which is developed from the existing expression of DSR.Applications of the DSR based transient stability margin to contingency ranking and screening are discussed.Simulations in the 10-machine 39-bus New England system are performed to show the effectiveness of the proposed DSR based tran-sient stability margin.
