首页> 外文会议>International Conference on Sustainable Energy and Intelligent Systems >COMPARISON OF MPPT USING GA-OPTIMIZED ANN EMPLOYING PI CONTROLLER WITH GA-OPTIMIZED ANN EMPLOYING FUZZY CONTROLLER FOR PV SYSTEM
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COMPARISON OF MPPT USING GA-OPTIMIZED ANN EMPLOYING PI CONTROLLER WITH GA-OPTIMIZED ANN EMPLOYING FUZZY CONTROLLER FOR PV SYSTEM

机译:使用PI控制器采用GA-Optimized ANN使用PI-Optimized Ann采用模糊控制器的PV系统的MPPT比较

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Solar energy is abundantly available that has made it possible to harvest it and use it properly. Solar energy can be a standalone generating unit or can be a grid connected generating unit depending on the availability of a grid nearby. Thus it powers rural areas where the availability of grids is very low. In order to tackle the present energy crisis one has to develop an efficient manner in which power has to be extracted from the incoming solar radiation. Maximum Power Point Tracking (MPPT) algorithms are necessary because PV arrays have a non linear voltage-current characteristic with an unique point where the power produced is maximum. This paper provides a comparison between MPPT method using Genetic Algorithm (GA) Optimized Artificial Neural Network (ANN) employing PI controller and that using Genetic Algorithm (GA) Optimized Artificial Neural Network (ANN) employing Fuzzy controller.
机译:太阳能大量可用,使得可以收获它并适当地使用它。 根据附近的网格的可用性,太阳能可以是独立的产生单元,或者可以是网格连接的产生单元。 因此,它为网格提供的农村地区很低。 为了解决目前的能源危机,必须开发一种有效的方式,其中必须从进入的太阳辐射中提取电力。 最大功率点跟踪(MPPT)算法是必要的,因为PV阵列具有非线性电压电流特性,其具有所产生的功率最大的唯一点。 本文提供了采用PI控制器的遗传算法(GA)优化人工神经网络(ANN)的MPPT方法与采用模糊控制器的遗传算法(GA)优化人工神经网络(ANN)的MPPT方法与采用模糊控制器的遗传算法(GA)优化的人工神经网络(ANN)进行比较。

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