Non-gaussian Filtering Algorithm And Its Application In Power System State Estimation

Power system state estimation is an integral part of energy management systems(EMS).The main sources of data for power system state estimation are the Supervisory Control and Data Acquisition(SCADA)and the Phasor Measurement Unit(PMU).With the continuous development of modern society,in order to meet people’s needs for different electricity consumption,the power grid system is becoming more and more complicated.Many devices in the power grid system are nonlinear loads,such as electronic equipment,frequency converters,etc.These devices may introduce higher harmonics that distort voltage and current waveforms.This distortion can cause the signal in the grid system to become non-Gaussian.At the same time,in the process of data collection and monitoring,it is inevitable that there will be uncontrollable interference.In the process of network transmission,there are also phenomena such as asynchronous transmission,delay and packet loss,which bring difficulties to power system state estimation.In order to avoid the occurrence of power accidents,it is necessary to eliminate the errors caused by random interference in the power system detection data.Power system state estimation can use the redundancy of measurement information to estimate the voltage amplitude and phase angle in the power system,so as to realize the safety monitoring of the power system operating state.For the interference of power system monitoring data,existing research usually models it as a type of Gaussian noise,and then uses the Kalman filter and its extended algorithm to estimate the operating state of the power system.In recent years,some scholars have modeled it as a type of non-Gaussian noise for power system state estimation methods.However,there is no related report on the state estimation method of power system under the mixed interference of multiple types of noise.In the power information system,when the monitoring data is transmitted through the network,there may be delay and packet loss.Most of the existing achievements are based on the assumption of Gaussian noise to carry out the research on multi-sensor asynchronous transmission fusion filtering method,and less consideration is given to the non-Gaussian power system monitoring data.noise characteristics.For this reason,on the basis of existing research methods,this topic considers the interference characteristics of various noises,and uses tools such as system splitting,algorithm fusion,maximum correlation entropy criterion,and pseudo-synchronization to develop non-Gaussian filtering methods and their Application research in power system state estimation.The research work of this paper is mainly as follows:1.In view of the mixed influence of Gaussian noise and non-Gaussian noise on power system monitoring data,based on the idea of "system splitting + algorithm fusion",a nonlinear filtering algorithm for a Gaussian noise and non-Gaussian mixed interference system is proposed.By designing a split coefficient,the system with mixed noise interference is split into several subsystems affected by a single type of noise.According to the interference noise characteristics of each subsystem,each subsystem is filtered by Extended Kalman Filter(EKF)and Maximum Correlation Entropy Extended Kalman Filter(MCEKF).Then,the distributed fusion and centralized sequential fusion are performed on the filtering algorithm results of each subsystem respectively.Finally,a simulation is carried out on the IEEE-33 test system to verify the effectiveness of the proposed algorithm.2.In the power information network,monitoring data needs to be transmitted by multiple nodes,which may cause asynchronous transmission problems such as network congestion and communication delays.Asynchronous transmission may also lead to data out-of-order problems.For this reason,this paper further develops the non-Gaussian filtering method of multi-sensor asynchronous transmission and its application research in power system state estimation.The asynchronous transmission measurement data is converted into pseudosynchronous measurement data through a pseudo-synchronous strategy,and a non-Gaussian fusion filter for multi-sensor asynchronous transmission is designed based on the maximum correlation entropy criterion.Finally,the algorithm is simulated on the IEEE-39 test system.The simulation results show that the algorithm proposed in this paper has higher estimation accuracy than the method of delay and discard.