De-Rated Concept Based Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy MPPT Technique for PV Power Grid

Abstract

PV power generation is attracting interest over other renewable energy sources due to its low cost, low pollution, and easy maintenance. To reduce the effects of changing environmental circumstances and enhance PV system power output, Maximum Power Point Tracking (MPPT) was adopted. It monitors the panel's maximum power output to maximize energy production. MPPT controllers are easy to use, affordable, have strong performance characteristics, and can be monitored easily in changing situations. This study provides a tracking system with an exceptional performance approach for max. PV system power generation. The ANFIS algorithm is used to model photovoltaic solar cells. ANFIS (Adaptive Neuro-Fuzzy Inference Systems) combine artificial neuralnetworks with the learning abilities of fuzzy logic combined with the flexibility of fuzzy logic. ANFIS is capable of dealing withnon-linear and time-varying issues, making it an excellent choice for detecting maximum power points (MPPT) in photo voltaic (PV) systems. When employing the ANFIS technique, it is possible to estimate this current and reproduce the output PV cell current by utilizing photo current. A distinct set of data from the training set is used for the model validation, which is done using gradient descent and rules applied to that data. The performance of the suggested technique is compared to that of a fuzzy logic-based MPPT algorithm to demonstrate its use.