一种改进的人工蜂群配电网状态估计方法

针对配电网状态估计模型的计算量大、精度低以及数值计算不稳定等问题,提出一种改进的基于人工蜂群算法的配电网状态估计方法。首先,根据网络拓扑结构和量测配置,建立配电网状态估计的目标函数；然后,采用人工蜂群算法对目标函数进行优化求解；最后构造当前最优解的适应度函数,以此作为迭代终止准则的判定条件,以更高的效率获得全局最优解。采用中国电力科学研究院搭建的16节点系统开展了大量的实验,结果表明本文方法可以有效地估计节点电压的幅值和相位。不仅如此,在收敛速度上也比现有的人工蜂群算法提高一倍,并且平均绝对误差比加权最小二乘法和等效功率变换法分别减小60%和50%。

An improved distribution state estimation method based onartificial bee colony algorithm

According to the problems that the calculating amount is large, the precision is low and the calculation results are unstable in the distribution state estimation, an improved state estimation method based on artificial bee colony (ABC) algorithm is proposed. The state estimation objective function of distribution network is built firstly depending on network topology and measurement configuration. Secondly, the objective function is optimized by using the ABC algorithm. Thirdly, the fitness function of the current optimal solution is constructed, which can be used as the condition of iteration stopping rule,in order to achieve global optimal solution with high efficiency. The 16-bus test system built by China Electric Power Research Institute is applied to verify the proposed method. The experimental results show that the improved method can estimate the magnitude and the phase of node voltage. Besides, the convergence speed of the proposed method has nearly doubled compared to the conventional artificial bee colony algorithm. The mean absolute error of the proposed method is dropped by 60% and 50% compared with weighted least squares method and the equivalent power transformation method, respectively.