Research On Power Flow Tracking And Dynamic Identification Method For Bad Data Of State Estimation

With the development of smart grid in our country and the improvement of power quality requirements,the electric power company put forward higher requirements for efficient maintenance of state estimation base data and on-line safety analysis and scheduling control of power system.The efficient identification of bad data is one of the important functions of power system state estimation,and it is also an important guarantee for on-line safety analysis and scheduling control of power grid.The energy management system of actual power dispatching center has reliable state estimation function,but no specialized bad data identification and correction of auxiliary decision software.In order to ensure the eligibility rate of the state estimation,for the frequent occurrence of bad data,the power sector only rely on the experience of the operation and maintenance engineers,by manually finding a large number of state estimates of basic data to identify the causes of incorrect measurement residuals,correct suspicious error parameters,bad measurement and error switch status.Although the expert experience can accurately identify some of the bad data to some extent,but it is not only demanding the operation and maintenance engineers have high professional level,but also a large number of artificial basic data analysis is very time-consuming.Therefore,how to efficiently and accurately identify bad data has important practical significance.In the actual state estimation problem,there may be one or more bad data(measurement data,network parameters,and switch status)at a certain time,and may be the same type or different when there are multiple bad data.However,how to effectively identify multiple types of bad data,which is not yet effectively solve.Therefore,this paper makes a research on complicated bad date identification method of state estimation.some specific research work has been done as follows.(1)A method for forward-backward identification the complex and bad data of state estimation is proposed.The method first considers the relationship between the measured data and between the measured data and the network parameters,based on the demand of balance power of node,and balance of power and voltage in the head end of branch,defined the criteria of the power voltage measurement data and grid equipment parameters.And then based on the node degree is equal to 1 topology search principle and unmarshalling principle have the minimum unbalance power of node,divided the power grid into radiated net structure through topology search,formed level matrix L of branch of power grid and corresponding branch’s node information matrix M of head and end,then use the L and M matrix and the definition criteria of bad data in this paper to carry out the power and voltage synchronization Forward-Backward calculation and bad data to determine,finally identified bad data and parameter error effectively.The simulation results based on the IEEE 39 node system have indicated that compared with the existing method of using the static state to estimate residual identification bad data,and using the Lagrangian multiplier identification parameter bad data,the proposed method has a higher identification accuracy in terms of simultaneous identification of multiple associated bad data and error parameters.(2)A method of topological error identification based on power flow tracking and dynamic state estimation is proposed.The method first uses the power flow tracking method to detect the bad measurement in the grid and uses the estimated value in the tracking process instead of the bad measurement.Then identification of topological errors using dynamic state estimation residual based on correction measurement data.The filtering and correction of the bad measurement by the power flow tracking method reduces the influence of the bad measurement on the topology error identification;the use of dynamic state estimation residuals to identify topological errors avoids the influences of the load suddenly changes,Which improves the accuracy of topological error identification based on dynamic state estimation.The simulation results based on the IEEE39 node system have indicated that the proposed method has higher accuracy than the existing dynamic state estimation topology error identification method.