Design and Optimization of Solar Powered Irrigation System using Artificial Intelligent Techniques
J. Environ. Nanotechnol., Volume 14, No 1 (2025) pp. 144-157
Abstract
The main objective of this study is the design and optimization of a solar photovoltaic (PV) powered irrigation system using artificial intelligence (AI) techniques. To employ AI approaches, including fuzzy logic, particle swarm optimization (PSO), artificial neural networks (ANN), and machine learning (support vector machine (SVM)), as controllers in maximum power point tracking (MPPT) systems to optimize PV systems. These techniques are also employed to regulate speed and torque in a moto-pump system. PV power generation has been predicted using the ML approach. The results indicate that the machine learning-based photovoltaic system achieves maximum power under varying weather conditions. At a reference speed of 300, the average speeds of Fuzzy, PSO, ANN, and ML are 292.3 rad/sec, 294.6 rad/sec, 298.4 rad/sec, and 300 rad/sec, respectively. In terms of overshoot and settling time, ML performs better. The ML-based system has 99.6% efficiency and is continuously maintained. The ML technique improves the performance of PV systems compared to the PSO, fuzzy, and ANN techniques. This work is highly beneficial for government agencies and stakeholders involved in irrigation systems.
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