基于差分进化粒子群算法的配电网状态估计

State Estimation of Distribution Network Based on Differential Evolution Particle Swarm Algorithm

为防止不良数据对状态估计结果产生不良影响,提出了一种辨识与修正配电网母线上不良电流数据的简易算法,引入质量标签来表示数据的可信度,提高输入电流的数据质量。同时,提出了采用基于差分进化的混合粒子群(DEPSO)算法来进行配电网状态估计,能够解决分布式电源接入后造成的非线性问题。算例仿真表明,输入状态估计的数据质量得到有效提高;与PSO算法相比,DEPSO算法更适用于配电网状态估计,计算结果更加准确。

In order to prevent the negative impact of bad data on the state estimation, this paper proposes a simple method which can identify and correct the electric current in distribution network bus bar.The method introduces the credibility of the quality label introduced to represent data, as far as possible to improve the quality of input current data.At the same time, this paper proposes a hybrid particle swarm optimization algorithm based on differential evolution （DEPSO） for distribution network state estimation, in order to solve the nonlinear characteristics of distributed generation.The simulation shows that the data quality of the input state estimation is improved effectively.Compared to the PSO algorithm, the DEPSO algorithm is more applicable to the grid state estimation and the calculation results are more accurate.