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针对三相不平衡配电网状态估计问题,设计了带约束整合项的多智能体协同智能水滴优化方法。首先,对三相不平衡配电网数学模型进行研究,并结合协同进化算特点,设计带约束整合项的状态估计适应函数,从而实现算法的无约束运行;其次,利用多智能体协同进化方式对智能水滴算法(IWD)进行改进,每个智能体(Agent)负责一个群体优化,不断引入各子Agent进化结果对全局最优解进行维度更新,实现高维优化问题的协同均衡分解。最后,通过在IEEE 57-bus和123-bus标准算例上的状态估计对比实验,显示所提方法在状态估计误差指标上具有更高的估计精度,并且给出不同网络规模下对比情况,均显示所提算法具有较高计算效率。
Aiming at the three-phase unbalanced distribution network state estimation problem, a multi-agent collaborative intelligent water droplet optimization method with constrained integration is designed. Firstly, the mathematic model of three-phase unbalanced distribution network is studied, and the state estimation adaptive function with constraint integration term is designed according to the characteristics of the co-evolutionary evolutionary algorithm to realize the unconstrained operation of the algorithm. Secondly, by using the multi-agent co-evolutionary approach The Intelligent Water Droplet Algorithm (IWD) is improved. Each Agent is responsible for a population optimization, and continuously introduces the evolutionary results of each sub-agent to update the global optimal solution dimensionally and achieve the coordinated equilibrium decomposition of the high-dimensional optimization problem. Finally, by comparing the state estimation experiments on the IEEE 57-bus and 123-bus standard examples, it shows that the proposed method has higher estimation accuracy on the state estimation error index and gives the comparison under different network scales It shows that the proposed algorithm has higher computational efficiency.