A Remote Diagnosis Using Variable Fractional Order with Reinforcement Controller for Solar-MPPT Intelligent System
Johny Renoald Albert, Thenmalar Kaliannan, Gopinath Singaram, Fantin Irudaya Raj Edward Sehar, Madhumathi Periasamy, Selvakumar Kuppusamy
Abstract
In various climatic conditions, the development of a Maximum Power Point controller is an integral part of the photovoltaic system to make certain continuous energy delivery in dynamic load conditions. Regarding the changes in environmental conditions and their parametric variation, the most difficult factor here is to design a model that can monitor the maximum power delivery. The model conceived in this work addresses both challenges because it Reinforces Learning Approach (RLA) of fractional order. Chennai gets the most solar energy in December from Tamilnadu (TN), and was the hot zone in 2020. TN Climate Change Information System offers science-based climate change information that increases user knowledge and understanding of climate change at the state level. To achieve the required accuracy and robustness, designing an RLA agent is a particularly necessary and crucial step. The main objective of fractional-order principle is to construct a novel model for tracking maximum performance.