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Multi-stage fuzzy load frequency control using PSO

机译：基于粒子群算法的多级模糊负荷频率控制

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In this paper, a particle swarm optimization (PSO) based multi-stage fuzzy (PSOMSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operate under deregulation based on the bilateral policy scheme. In this strategy the control is tuned on line from the knowledge base and fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by PSO algorithm, that has a strong ability to find the most optimistic results. The motivation for using the PSO technique is to reduce fuzzy system effort and take large parametric uncertainties into account. This newly developed control strategy combines the advantage of PSO and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed PSO based MSF (PSOMSF) controller is tested on a three-area restructured power system under different operating conditions and contract variations. The results of the proposed PSOMSF controller are compared with genetic algorithm based multi-stage fuzzy (GAMSF) control through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes.
机译：本文提出了一种基于粒子群优化（PSO）的多级模糊（PSOMSF）控制器，用于解决基于双边策略方案而在放松管制下运行的重组电力系统中的负载频率控制（LFC）问题。在这种策略中，控制是根据知识库和模糊推理进行在线调整的，所需资源更少，并且具有两个规则库集。在提出的方法中，为了达到所需的鲁棒性能水平，对隶属函数的精确调整非常重要。因此，为减少设计工作量并找到更好的模糊系统控制，PSO算法自动设计了隶属函数，具有很强的发现最乐观结果的能力。使用PSO技术的动机是减少模糊系统的工作量并考虑大的参数不确定性。这种新开发的控制策略结合了PSO和模糊系统控制技术的优势，并带来了一种具有简单结构且易于实现的灵活控制器。建议的基于PSO的MSF（PSOMSF）控制器在三区域重组电力系统上在不同的工作条件和合同变动下进行了测试。通过一些性能指标，将提出的PSOMSF控制器的结果与基于遗传算法的多级模糊（GAMSF）控制进行比较，以说明其在各种系统参数和负载变化下的鲁棒性能。

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