Power System Operation State Identification Considering the Process of Transient Stability
The power system state identification in the data-driven background includes two scenarios,pre-fault and post-fault,which have the analysis characteristics under the concepts of safety domain and stability domain,respectively.Limited by the inconsistency of sample characteristics and application scenarios,existing studies usually model it as two separate problems.On the one hand,independent modeling ignores the coupling between the two.On the other hand,the operating state of the power system is changing all the time;the network model established based on a single scenario is only suitable for the current scenario;independent modeling based on two scenarios has problems such as complex model switching and long time for parameter update.To solve this problem,this paper proposes a power system state identification method based on multi-scale dense network,which is suitable for both pre-fault and post-fault transient stabilization scenarios.First,the adaptive pooling layer structure is used to establish a power system operation status identification model suitable for the input of time section features and time series feature types,which can be applied to both pre-fault and post-fault scenarios.Then,considering the temporal correlation of transient stable mathematical models under the concepts of safe domain and stable domain,an adaptive selection mechanism of samples based on knowledge reasoning is designed to express the temporal relationship between pre-fault and post-fault scenarios through the"dynamic"characteristics of the network,so as to improve the computational efficiency.Finally,the effectiveness and superiority of the proposed method are verified in the New England 10-machine 39-node study system and the actual power grid.
power system situation identificationdynamic neural networkdeep learning
万方数据
维普
