Research On Dynamic State Estimation Of Distribution Network Based On Multi-source Measurement Information

With the construction of smart grid,the importance and attention of distribution network are increasing.There are many kinds of measurement devices in distribution network,kinds of measurement information will exist in the distribution network.In addition,the introduction of new energy technology and more forms of electricity use make the distribution network structure more complex and the operation state more changeable,which leads to the distribution network present strong dynamics and nonlinearity at this stage.In order to improve the monitoring capability of distribution network,it is necessary to study the adaptive dynamic state estimation method for complex systems while improving the utilization rate of existing measurement information.Based on the above tasks,the main work of this paper is as follows:(1)In order to solve the problems of low utilization rate of the data with high frequency and precision and insufficient redundancy of measurement in distribution network,an adaptive self-optimizing state estimation method for improving observability of distribution network is proposed.This method is composed of an optimizing state estimation module in the context of measurement information redundancy,a state refresh estimation module in the local area,and a robust state estimation module in the case of missing measurement information.The optimizing state estimation realizes the self-optimization of the algorithm model and numerical stability.Dynamic state estimation is introduced to track the state in real time and to provide high-precision supplementary information when the measurements are missing,so as to achieve global estimation and improve the estimation accuracy.(2)An adaptive dynamic state estimation method based on interactive multiple model is proposed to deal with the situation that the operation state of distribution network is changeable,dynamic and non-linear.Different dynamic estimation methods are improved to be adaptive and suitable for different system change scenarios respectively.Then,the improved estimation algorithms are embedded in the framework of the interactive multiple model.With the help of the proposed performance index of state estimation,the drastic changes in the operation state of the distribution network are immediately perceived and the main reasons for the changes are analyzed.The corresponding adaptive algorithm is given higher weight to achieve high-precision estimation and fast convergence of the distribution network with the dramatic changes.(3)In order to improve the robustness of dynamic state estimation in distribution network with strong uncertainty,a robust dynamic state estimation method based on adaptive H∞ filtering is proposed.On the basis of dynamic state estimation using H∞filter,the proportional factor is introduced to adjust the performance boundary of the filter.The performance index of the state estimation is used to analyze the model deviation,and the proportional factor is adaptively adjusted according to the result of the deviation analysis,so that the performance boundary under the proportional factor can better match the operation environment and improve the estimation accuracy.In addition,the algorithm uses system and measurement information to modify model parameters online,reduce model deviation and further improve estimation performance.