CAJ | 학술논문

采用粒子群优化算法（particle swarm optimization,PSO）和菌群优化算法（bacterial foraging optimization,BFO）相结合的、带有随机惯性因子和异步时变学习因子的改进型BFO-PSO优化算法,解决混合型有源滤波器中无源滤波器参数优化设计问题。通过将滤波器的无功补偿容量、补偿后滤波效果及初期投资成本作为优化目标,采用重要目标加动态常数制约法给出综合适应度函数,进而求解多目标优化问题。仿真验证了理论分析和设计的正确性,相关设计方法也可为其他类型的无源滤波器的优化设计提供借鉴。
채용입자군우화산법（particle swarm optimization,PSO）화균군우화산법（bacterial foraging optimization,BFO）상결합적、대유수궤관성인자화이보시변학습인자적개진형BFO-PSO우화산법,해결혼합형유원려파기중무원려파기삼수우화설계문제。통과장려파기적무공보상용량、보상후려파효과급초기투자성본작위우화목표,채용중요목표가동태상수제약법급출종합괄응도함수,진이구해다목표우화문제。방진험증료이론분석화설계적정학성,상관설계방법야가위기타류형적무원려파기적우화설계제공차감。
With combination of particle swarm optimization algorithm and bacterial foraging optimization,an improved BFO-PSO optimized algorithm based on the random inertia factor and asynchronous time-dependent learning factor is used to solve optimal design,s problems of passive filter parameters for hybrid active filter.It takes the capacity of reactive power compensation,the harmonic effect after compensation and the original investment cost as three objectives,and restricts the important goal and dynamic constants as a method to achieve comprehensive fitness function,and then solves the multi-objective optimization problem.Simulation result verifies the correctness of the mentioned theory and design.Such design method can be used as a reference for design optimization of other type passive power filters.