Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

Engineering Reports Pub Date : 2024-07-15 DOI:10.1002/eng2.12963

Malhar Khan, Muhammad Amir Raza, Muhammed Faheem, Shahjahan Alias Sarang, Madeeha Panhwar, T. Jumani

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Abstract

The increasing global need for renewable energy sources, driven by environmental concerns and the limited availability of traditional energy, highlights the significance of solar energy. However, weather fluctuations challenge the efficiency of solar systems, making maximum power point tracking (MPPT) systems crucial for optimal energy harvesting. This study compares ten MPPT approaches, including both conventional and artificial intelligence (AI)‐based techniques. These controllers were designed and implemented using MATLAB Simulink, and their performance was evaluated under real environmental conditions with fluctuating irradiance and temperature. The results demonstrate that conventional techniques, such as incremental conductance (INC), Perturb and Observe (P&O), Incremental conductance and Particle Swam Optimization (INC‐PSO), Fuzzy Logic Control and Particle Swam Optimization (FLC‐PSO), and Perturb and Observe and Particle Swam Optimization (P&O‐PSO), achieved accuracies of 94%, 97.6%, 98.9%, 98.7%, and 99.3% respectively. In contrast, AI‐based intelligent techniques, including Artificial Neural Network (ANN), Artificial Neural Fuzzy Interference System (ANFIS), Fuzzy Logic Control (FLC), Particle Swam Optimization (PSO), and Artificial Neural Network and Particle Swam Optimization (ANN‐PSO), outperform achieving higher accuracies of 97.8%, 99.9%, 98.9%, 99.2%, and 99%, respectively. Compared to available research, which often reports lower accuracies for conventional techniques, our study highlights the enhanced performance of AI‐based methods. This study provides a comprehensive comparative analysis, delivering critical analysis and practical guidance for engineers and researchers in selecting the most effective MPPT controller optimized to specific environmental conditions. By improving the efficiency and reliability of solar power systems, our research supports the advancement of sustainable energy solutions.

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基于传统和人工智能的最大功率点跟踪技术，实现高效太阳能发电

在环境问题和传统能源供应有限的推动下，全球对可再生能源的需求日益增长，这凸显了太阳能的重要性。然而，天气波动对太阳能系统的效率提出了挑战，使得最大功率点跟踪（MPPT）系统成为优化能源采集的关键。本研究比较了十种 MPPT 方法，包括传统技术和基于人工智能（AI）的技术。使用 MATLAB Simulink 设计和实施了这些控制器，并在辐照度和温度波动的实际环境条件下对其性能进行了评估。结果表明，增量电导（INC）、扰动和观测（P&O）、增量电导和粒子游走优化（INC-PSO）、模糊逻辑控制和粒子游走优化（FLC-PSO）以及扰动和观测和粒子游走优化（P&O-PSO）等传统技术的精确度分别为 94%、97.6%、98.9%、98.7% 和 99.3%。相比之下，基于人工智能的智能技术，包括人工神经网络（ANN）、人工神经模糊干涉系统（ANFIS）、模糊逻辑控制（FLC）、粒子游动优化（PSO）和人工神经网络与粒子游动优化（ANN-PSO）的表现更为出色，准确率分别达到 97.8%、99.9%、98.9%、99.2% 和 99%。与现有研究相比，传统技术的准确率通常较低，而我们的研究则突出了基于人工智能的方法的更高性能。本研究提供了全面的比较分析，为工程师和研究人员选择最有效的 MPPT 控制器提供了关键分析和实用指导，并对特定环境条件进行了优化。通过提高太阳能发电系统的效率和可靠性，我们的研究有助于推动可持续能源解决方案的发展。

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