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本文提出一种自适应的粒子群算法,增加了动态权重、压缩因子和动态记忆因子,既克服了全局算法搜索不够精细的特点,又避免了陷入局部最优,该算法在搜索过程中刚开始步长较大,随着与目标的不断接近,步长越来越短,这样既保证了搜索效率,又保证了搜索精度。搭建了基于MATLAB中Smulink的水轮机调速系统,通过基于该算法的水轮机PID调速参数优化仿真结果表明:采用该算法可以有效改善水轮机控制系统过渡过程的动态性能和调节品质。
In this paper, an adaptive particle swarm optimization algorithm is proposed, which increases the dynamic weight, compression factor and dynamic memory factor. It not only overcomes the inaccurate search of the global algorithm, but also avoids falling into the local optimum. The algorithm just starts in the search process With larger steps, with the goal of continuous approach, shorter and shorter steps, so that not only ensures the search efficiency, but also to ensure the search accuracy. The turbine speed regulation system based on Smulink in MATLAB is built. The simulation results of the PID speed regulation parameters of the turbine based on this algorithm show that the algorithm can effectively improve the dynamic performance and regulation quality of the turbine control system in the transition process.