An Adaptive Multilayer Neural Network-Based Power Point Tracking Technique for Solar Powered Battery Charging Network

The usage of nonrenewable power sources has decreased extensively because of the high emission of carbon monoxide, ethane plus hydrocarbons. So, these harmful gasses directly destroy the atmospheric conditions. Also, the nonrenewable networks required more initial functioning cost. So, at present, most of the human beings are working on renewable power sources. In this article, the Solar Photovoltaic Networks (SPN) are considered for supplying power to the battery charging systems. There are many dramatic advantages of SPN: less maintenance cost, easy way to handle, very less human resource is needed, plus more flexibility for the usage of all rural and urban peoples. However, the SPN supplies continuous variation of power to the consumers. So, to stabilize the SPN power, an Adaptive Multilayer Neural Network Controller (AMNNC) is used to find the Maximum Power Point (MPP) of the overall power system. The features of this AMNNC are high robustness, good reliability, easy handling, and greater accuracy. Here, the SPN output voltage is enhanced by using the power DC-DC converter. The merits of this converter are low component utilization, less development price, plus more flexibility.