Research On Data Collection And State Estimation Of Intelligent Substation

Data collection in the power system is a key component of the power management system,playing a crucial role in fault analysis,network reconstruction,economic operation,and real-time control of the power system.With the continuous upgrading of power grid intelligence,higher requirements have been put forward for real-time and accurate data collection in the power grid.In order to quickly and accurately collect data and estimate the state of power systems,this paper studies the implementation of robust unscented Kalman filtering algorithm in power system state estimation based on existing unscented Kalman filtering algorithms.This article takes distribution network substations as the research object,and provides the substation structure,data acquisition system model,data preprocessing methods,and types of collected data.In order to improve the accuracy of the collected data,this article conducted research on data filtering and state estimation.Considering that the conventional unscented Kalman filtering algorithm has poor estimation effect when the scale correction factor is fixed,this paper adopts an improved unscented Kalman filtering algorithm,which needs to correct the scale correction factor when performing state estimation,so as to improve filtering performance.This paper also deeply studies the influence of gross error on state estimation,and adopts a robust untrace Kalman filter algorithm.By setting gross error criterion and introducing enhancement factor,the measurement noise is suppressed to reduce the interference of gross error on system state estimation,so as to improve the accuracy of state estimation.Secondly,in IEEE33 and IEEE118 node distribution system,the state estimation algorithm used in this paper is verified by simulation,and the estimation results are compared with other algorithms.The simulation results show that the robust unscented Kalman filter algorithm can obtain good estimation results and meet the requirements of power systems.Finally,a data acquisition and state estimation testing system was designed.Using VS programming software on the Microsoft.NET platform,the Modbus TCP/IP communication protocol was written in C # language,connecting MATLAB and MYSQL databases.Simulation results showed that the robust unscented Kalman filtering algorithm using SACADA/PMU hybrid measurement method has good estimation performance in distribution systems with distributed power sources.