一种改进粒子群优化算法在多目标无功优化中的应用  

Application of Improved Particle Swarm Optimization Algorithm to Multi-Objective Reactive Power Optimization

作　　者：李鑫滨[1,2] 朱庆军[1] 

机构地区：[1]燕山大学电气工程学院,秦皇岛066004 [2]河北省数学研究中心,石家庄050000

出　　处：《电工技术学报》2010年第7期137-143,共7页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金(60874026);河北省自然科学基金(07M007)资助项目

摘　　要：针对粒子群优化算法容易陷入局部最优等问题,提出了一种新的模糊自适应-模拟退火粒子群优化算法。该算法首先是基于模糊推理的思想,将规范化的当前最好性能评价和粒子群算法的惯性权重、学习因子作为模糊控制器的输入,以算法参数变化量的百分数作为模糊控制器的输出,并根据参数设置经验建立了相应的模糊控制规则,使其能够自适应地调节粒子群优化算法的参数;对调节后粒子新位置的优劣,则通过采用模拟退火算法调节粒子的适应度来加以评价。最后,采用改进后的粒子群优化算法对多目标无功优化模型进行了求解。IEEE30节点和IEEE118节点的标准电力系统算例验证了本文所提出的模糊自适应-模拟退火粒子群优化算法的有效性和可行性。In order to avoid the defect that a conventional particle swarm optimization （PSO） algorithm is easy to trap into a local optimization, a new fuzzy adaptive-simulated annealing PSO algorithm is proposed in this paper. Based on the principle of fuzzy logic, the inputs to the fuzzy controller are the normalized current best performance valuation, inertia weighing of the PSO algorithm and the learning factor, the outputs of the controller are the parameters rate of change. The fuzzy rules are formulated based on the experience of parameters settings so as to adjust the PSO parameters adaptively. The quality of particles’ new location after the adjustment is valued by simulated annealing （SA）. Then, the modified PSO algorithm is introduced to solve multi-objective reactive power optimization problem. IEEE 30-bus and IEEE118-bus system are simulated to verify the effectiveness and feasibility of SA- fuzzy self-adaptive particle swarm optimization algorithm.

关 键 词：粒子群优化 多目标无功优化 模糊 自适应 模拟退火 

分 类 号：TM714.3[电气工程—电力系统及自动化]