Dynamic State Estimation Method of Distribution Network Based on Adaptive H∞ Cubature Kalman Filter
Due to the random variation of load,the participation of demand response,the fluctuation of distributed power supply,and the variety of measurement devices,the measurement data of distribution network is prone to abnormal values,which leads to the decline of dynamic state estimation accuracy.In order to improve the accuracy of distribution network state estimation,this paper proposed a dynamic state estimation method for distribution network based on adaptive H∞ cubature Kalman filter.Firstly,based on the cubature Kalman filter,the adaptive factor and H∞ filter were combined to deal with and limit the model error.Secondly,combined with the noise estimator,the parameters in the process noise were estimated online to reduce the influence of noise on the prediction error.Finally,a typical distribution network system with 69 nodes was simulated.The simulation results show that the estimation accuracy of the proposed method is improved by more than 10%under three scenarios:system normal operation,demand response participating in peak load shaving and load mutation,maintaining a relatively high estimation accuracy.
state estimationcubature Kalman filterH∞ filternoise statistic estimatordemand response
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