Dynamic State Estimation For Power System Based On The Compressed Sensing

State estimation for power system is the core component of modern power grid management system,and plays a key role in ensuring the normal operation of power system.The main role of state estimation is based on monitoring equipment and modeling data to achieve real-time monitoring of power grid operation,So that the scheduling center for the next step analysis and control.The current power dispatching center to master the real-time operation of the power system is mainly dependent on the static state estimation,does not have the predictive function and is susceptible to unknown time-varying noise,can not meet the requirements of the future smart grid.As new equipment continues to access the grid,the data collected by the traditional power signal monitoring system will increase the geometric multiple,The current method of monitoring and estimating the power system has been unable to meet the requirements of data storage cost,transmission efficiency and estimation accuracy.In order to solve the above problems,a dynamic state estimation method of power system based on compression perception is presented.Aiming at the increase in the amount of data,the theory of compression perception is introduced.This paper studies the basic theory of compression perception and several common data reconstruction methods,According to the characteristics of the power system to improve accordingly,the improved particle swarm optimization algorithm is introduced into the compression perception,this algorithm replaces the selection process of the optimal atom in the old reconstruction algorithm,can be selected in a short time to select the optimal atom,increase the precision of reconstruction,in the original signal to maintain a high degree of compression,greatly reducing the amount of data information while maintaining the original sampling structure,accurately restore the original data within the tolerance allowable range.In order to solve the traditional Unscented Kalman Filter in the Sigma point proportional correction,free parameters often take the fixed value,using the intelligent optimization algorithm to achieve the optimal value of free parameters,so as to improve the filtering accuracy of Adaptive Unscented Kalman Filter.In order to reduce the influence of unknown time-varying noise on the system,the improved time-varying noise estimator is introduced to further improve the accuracy of state estimation.Combining the compression sensing technique with the improved dynamic state estimation method,a dynamic state estimation method based on compression perception is obtained.Finally,the use of MATLAB software for different operating conditions for simulation analysis,the simulation results show that the dynamic state estimation method of power system based on compression sensing is given in this paper,the dynamic state estimation can be accurately performed while the measurement data is highly compressed,reducing the data transmission and storage equipment pressure.