Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms

In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both exhibit fast achievement MPP under varying environmental conditions and applicable in different PV systems. The only required knowledge system open-circuit voltage, short-circuit current power, all STC, which always provided by manufacturer. compared a Fuzzy Logic Controller universality proposed is highlighted. After implementation validation proper performance both methods, evolutionary optimization algorithms (Big Bang—Big Crunch Genetic Algorithm) applied. results demonstrate that achieve higher energy production time reduced, after application algorithms.