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首页> 外文会议>2014 International Conference on Electrical Sciences and Technologies in Maghreb >Combined approach between FLC and PSO to find the best MFs to improve the performance of PV system
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Combined approach between FLC and PSO to find the best MFs to improve the performance of PV system

机译：FLC和PSO之间的组合方法以找到最佳的MF，以提高光伏系统的性能

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During designing of fuzzy logic controller (FLC), an expert knowledge of the process to be controlled can be used to determine the membership functions (MFs) and the rules. However there is no general procedure for designing a FLc seen that many of errors may be encountered in its implementation, and these FLC can not be adapted to other applications. The difficulties encountered in the design of CLF have guided researchers to move towards the optimization of these controllers. The present paper proposes an approach combined from FLC and particle swarm optimization algorithm (PSO) used to finding the optimum membership functions (MFs) of a fuzzy system with the aim of achieving the accurate and acceptable desired results. For improving and optimizing the performance of a photovoltaic system to deliver the maximum power available. It is clearly proved that the optimized MFs provided better performance than a fuzzy model for the same system, when the MFs were heuristically defined.
机译：在设计模糊逻辑控制器（FLC）时，可以使用要控制的过程的专业知识来确定隶属函数（MF）和规则。但是，由于没有设计FLc的通用程序，因此在其实现中可能会遇到许多错误，并且这些FLC无法适应其他应用程序。 CLF设计中遇到的困难已引导研究人员朝着优化这些控制器的方向发展。本文提出了一种将FLC和粒子群优化算法（PSO）相结合的方法，用于寻找模糊系统的最优隶属函数（MF），以期获得准确和可接受的期望结果。用于改善和优化光伏系统的性能，以提供最大的可用功率。显然证明，当启发式定义MF时，对于同一系统，优化的MF比模糊模型具有更好的性能。

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