Power distribution network state estimation method and system based on pseudo Monte Carlo particle filter

Abstract

The invention provides a power distribution network state estimation method and system based on pseudo Monte Carlo particle filtering. The method comprises the following steps: generating sampling points obeying uniform distribution by adopting a pseudo Monte Carlo sampling method; carrying out randomization processing on the sampling points obeying uniform distribution; placing the random sampling points in a sample space according to a prior probability density function to generate particles required by a particle filtering algorithm; and based on the Bayesian theory, performing three-phase state estimation of the power distribution network by using the particles generated by the randomized pseudo Monte Carlo sampling method and particle filtering to obtain a running state estimation value of the power distribution network. The method is based on the Bayesian theory, the advantage of high fitting error convergence speed of the pseudo Monte Carlo sampling method is exerted, the state transition process of the power distribution network is considered, historical state information and current measurement information are fully utilized, and the estimation precision the same as that of standard particle filtering can be achieved by adopting fewer particles; therefore, the calculation amount of power distribution network estimation is effectively reduced, and high-precision estimation of the power distribution network is realized.

Description

technical field [0001] The present disclosure relates to the related technical field of distribution network state estimation, and in particular, relates to a distribution network state estimation method and system based on pseudo-Monte Carlo particle filter. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] As the contradiction between rising power demand and fossil energy shortage becomes increasingly prominent, active power distribution systems have become a new trend in the development of modern power distribution systems. It integrates multiple technologies such as information communication, big data and power system, and aims to coordinate renewable energy and other controllable resources in the system through flexible and effective active control and management methods, so that it has a certain degree of active adjustment and optimi...

AI Technical Summary

Problems solved by technology

[0006] (1) The traditional estimation method based on weighted least squares only uses the current measurement information to estimate, but fails to use prior state information. When the distribution network load changes greatly, it may not meet the estimation accuracy requirements

Although the dynamic estimation method based on Kalman filtering takes the state transition process into account, it is limited by the assumption of Gaussian noise, so it is difficult to apply to the actual situation where non-Gaussian noise is common.

[0007] (2) Although the particle filter is suitable for dealing with the state estimation problem of nonlinear and non-Gaussian systems, its computational complexity is high and time-consuming, and it is difficult to meet the real-time requirements of online state estimation

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