电力系统保护与控制
電力繫統保護與控製
전력계통보호여공제
POWER SYSTM PROTECTION AND CONTROL
2014年
5期
129-135
,共7页
多目标无功优化%电压稳定%有功损耗%人工智能%多策略融合粒子群优化算法
多目標無功優化%電壓穩定%有功損耗%人工智能%多策略融閤粒子群優化算法
다목표무공우화%전압은정%유공손모%인공지능%다책략융합입자군우화산법
multi-objective reactive power optimization%voltage stability%active network loss%artificial intelligence%particle swarm optimization with multi-strategy integration algorithm
电力系统无功优化属于典型的多目标非线性复杂优化问题，求解非常困难。近年来，众多智能优化算法应用于该问题，其中粒子群优化(Particle Swarm Optimization, PSO)算法最具代表性；但PSO算法性能仍有待提高，如可能陷入局部极值。提出一种多策略融合粒子群优化（Particle Swarm Optimization with Multi-Strategy Integration，MSI-PSO）算法，对速度更新公式引入选择操作，分阶段加速因子调整和惯性权重动态调整，以平衡粒子局部搜索与全局探索能力；同时，随机选取部分性能差的粒子，将其速度更新公式中的个体认知部分修改为社会认知部分，以提高算法搜索精度和收敛速度。建立以系统网络损耗最小和系统电压稳定裕度最大为目标的无功优化仿真模型，分别考虑加权法、隶属度函数法和Pareto法实施多目标处理。针对 IEEE30节点测试系统进行仿真实验，结果表明，和其他几种改进 PSO 算法以及基于 pareto 最优解集PSO算法进行对比，所提MSI-PSO算法具有更好的性能，能够有效求解电力系统多目标无功优化问题。
電力繫統無功優化屬于典型的多目標非線性複雜優化問題，求解非常睏難。近年來，衆多智能優化算法應用于該問題，其中粒子群優化(Particle Swarm Optimization, PSO)算法最具代錶性；但PSO算法性能仍有待提高，如可能陷入跼部極值。提齣一種多策略融閤粒子群優化（Particle Swarm Optimization with Multi-Strategy Integration，MSI-PSO）算法，對速度更新公式引入選擇操作，分階段加速因子調整和慣性權重動態調整，以平衡粒子跼部搜索與全跼探索能力；同時，隨機選取部分性能差的粒子，將其速度更新公式中的箇體認知部分脩改為社會認知部分，以提高算法搜索精度和收斂速度。建立以繫統網絡損耗最小和繫統電壓穩定裕度最大為目標的無功優化倣真模型，分彆攷慮加權法、隸屬度函數法和Pareto法實施多目標處理。針對 IEEE30節點測試繫統進行倣真實驗，結果錶明，和其他幾種改進 PSO 算法以及基于 pareto 最優解集PSO算法進行對比，所提MSI-PSO算法具有更好的性能，能夠有效求解電力繫統多目標無功優化問題。
전력계통무공우화속우전형적다목표비선성복잡우화문제，구해비상곤난。근년래，음다지능우화산법응용우해문제，기중입자군우화(Particle Swarm Optimization, PSO)산법최구대표성；단PSO산법성능잉유대제고，여가능함입국부겁치。제출일충다책략융합입자군우화（Particle Swarm Optimization with Multi-Strategy Integration，MSI-PSO）산법，대속도경신공식인입선택조작，분계단가속인자조정화관성권중동태조정，이평형입자국부수색여전국탐색능력；동시，수궤선취부분성능차적입자，장기속도경신공식중적개체인지부분수개위사회인지부분，이제고산법수색정도화수렴속도。건립이계통망락손모최소화계통전압은정유도최대위목표적무공우화방진모형，분별고필가권법、대속도함수법화Pareto법실시다목표처리。침대 IEEE30절점측시계통진행방진실험，결과표명，화기타궤충개진 PSO 산법이급기우 pareto 최우해집PSO산법진행대비，소제MSI-PSO산법구유경호적성능，능구유효구해전력계통다목표무공우화문제。
Reactive power optimization is a typical multi-target nonlinear optimization problem, which is complex and difficult to solve. In recent years, many intelligent optimization algorithms are applied to solve the problem. The particle swarm optimization (PSO) algorithm is one of the most typical reactive power optimization intelligent optimization algorithms, while it still needs to be improved because it is easy to fall into local minima. This paper proposes an algorithm of particle swarm optimization with multi-strategy integration (MSI-PSO). Selection operation, phased adjustment of acceleration factor and the dynamic adjustment of inertia weight are introduced to the speed updating formula to balance the local and global search ability of particles. Some particles with poor performance are selected randomly to amend the individual cognitive part in the speed updating formula as social cognition to improve the accuracy and convergence speed of the particle search. Reactive power optimization simulation model is established with a target of minimum loss of the active network and maximum system voltage stability margin. The weighted method, membership function method and Pareto method are used to deal with the multi-objective problem. Simulation on the IEEE30 bus testing system is conducted. The results show that compared with several other improved PSO algorithms and the PSO algorithm based on Pareto optimal solution set, the proposed MSI-PSO algorithm has better performance and can effectively solve the multi-objective reactive power optimization.
