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针对既有配电网架结构下的分布式电源选址与定容问题,提出了一种抑制局部最优的带惯性权重的粒子群优化算法(PSO),在算法搜索过程中通过引入变异算子和偏移算子,根据适应度标准差及当前最优适应度值,确定目前部分粒子的畸变程度,摆脱了寻优过程中局部最小点的束缚,改善了多目标优化的非线性准确度。同时改进了PSO中对于速度和位置的更新方法和终止判据,提高了算法的收敛特性。通过IEEE-33节点配电网和延边配电网算例表明,改进的粒子群算法能够有效地寻找到全局最优解并且具有良好的收敛特性。
In order to solve the problem of sizing and locating distributed generation with distributed power grid structure, a Particle Swarm Optimization (PSO) algorithm with local optimal inertia weight is proposed. By introducing mutation in the search process Based on the standard deviation of fitness and the current optimal fitness, the partial and offset operators are used to determine the degree of distortion of some of the current particles and get rid of the shackles of the local minimum in the optimization process, thus improving the nonlinear accuracy of the multi-objective optimization . At the same time, the updating method and the termination criterion for speed and position in PSO are improved, and the convergence property of the algorithm is improved. The results of IEEE-33 node distribution network and Yanbian distribution network show that the improved particle swarm optimization algorithm can find the global optimal solution effectively and has good convergence characteristics.