Research And Application Of Robust State Estimation For Power Systems

In order to improve the security and reliability of power system network,a large number of data is used to monitor and control systems for monitoring the status of the network in real time.Raw data must be processed into mature data through state estimation before it can be sent to the system database in order to analyze in the future.The state estimation adopts filtering algorithm to obtain the real state information of the system through setting the high precision data with heavy weight and the low precision data with small weight.In order to improve the security and reliability of power system network,the robust estimation method and its application are studied in this paper.The main work are as follows:Firstly,six kinds of exponential weighted state estimation methods with more practicability are studied.These six methods have strong resistance to measurement error and bad data,and are in line with the relevant conditions for online operation.In order to illustrate the application of these methods,the performance of these six methods is comprehensively analyzed based on the IEEE118 node standard system for these six methods.Further,the data of actual system was selected for analysis and testing,and the performance and advantages and disadvantages of these several methods were compared.Then,the voltage amplitude and phase angle on the non-zero injection node are used to represent the voltage amplitude and phase angle on the zero injection node.An improved robust state estimation model is derived to ensure the robustness of the model under the condition that the zero injection power is strictly equal to zero.On this basis,the performance of the proposed algorithm is comprehensively evaluated by the evaluation index of zero-injection constraint and the evaluation index of fit.The simulation test shows that the proposed method can reduce the number of iterations,and ensure that the power of the zero-injection node is strictly equal to zero,and has good robustness under the condition that the result of state estimation is credible.