Real time hybrid inductor capacitance pattern based power stability maximization for photovoltaic system using machine learning

Abstract

The growing use of electricity and electric devices increases the demand of electricity in higher volume. To meet the requirement of human society, the power generation organizations generate electric power from various sources like Coal, solar, wind, thermal and atomic resources. However, resources are scares due to the increased human population and usage of devices. Also, they are not renewable sources which cannot be utilized redundantly and not available throughout the year. The solar is the electric resource available throughout the year with little fluctuations. By using the electric resources above discussed, the electricity is being produced for the distribution on the society and organizations. Apart from this, the distribution of electricity has many challenges in terms of the power stabilization. The electric devices requires constant electric voltage to run the device. The power supplier would not be able to feed the constant electric power for the devices. This makes the device struggling and suffer to function in a proper way. The electric devices are used for different purposes like security in a organization, house hold things, automation, and so on. If there is higher fluctuation in the incoming voltage, then the entire system will struggle to function in a proper way. This would affect the performance of the entire system. This increases the requirement of power stabilization to be performed over the power distribution units. There exist numerous techniques in handling the problem, but suffer to achieve expected performance like voltage loss, impedance, fluctuation and so on. The identified problems have motivated the design of efficient power stabilization and regulation models to maximize stability and utilization. The method aims to trigger multiple circuits at any duty cycle and reduce voltage loss using an inductor-capacitor pattern in circuit selection. A hybrid model incorporating a genetic algorithm will enhance power utilization by considering multiple circuit features, increasing stab