Modeling of intelligent controllers for solar photovoltaic system under varying irradiation conditions

Khan, Malhar; Raza, Muhammad Amir; Jumani, Touqeer Ahmed; Mirsaeidi, Sohrab; Ali, Aamir; Abbas, Ghulam; Touti, Ezzeddine; Alshahir, Ahmed

doi:10.3389/fenrg.2023.1288486

Cited by 7 publications

(2 citation statements)

References 62 publications

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“…Pakistan's ARE 2019 sets several overall objectives and some specific targets for the energy sector in the country. The first objective of increasing the share of renewable energy is accompanied by a target of increasing renewable energy generation to 20% by 2025, and then 30% by 2030 104 .…”

Section: Resultsmentioning

confidence: 99%

Managing the low carbon transition pathways through solid waste electricity

Raza,

Aman,

Abbas

et al. 2024

Sci Rep

Self Cite

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The potential of solid waste as an energy source is clear, owing to its wide availability and renewable properties, which provide a critical answer for energy security. This can be especially effective in reducing the environmental impact of fossil fuels. Countries that rely heavily on coal should examine alternatives such as electricity from solid waste to provide a constant energy supply while also contributing to atmospheric restoration. In this regards, Low Emissions Analysis Platform (LEAP) is used for simulation the entire energy system in Pakistan and forecasted its capital cost and future CO2 emissions in relation to the use of renewable and fossil fuel resources under the different growth rates of solid waste projects like 20%, 30% and 40% for the study period 2023–2053. The results revealed that, 1402.97 TWh units of energy are generated to meet the total energy demand of 1193.93 TWh until 2053. The share of solid waste based electricity in total energy mix is increasing from a mere 0.81% in 2023 to around 9.44% by 2053 under the 20% growth rate, which then increase to 39.67% by 2053 under the 30% growth rate and further increases to 78.33% by 2053 under the 40% growth rate. It is suggested that 40% growth rate for solid waste based electricity projects is suitable for Pakistan until 2053 because under this condition, renewable sources contributes 95.2% and fossil fuels contributed 4.47% in the total energy mix of Pakistan. Hence, CO2 emissions are reduced from 148.26 million metric tons to 35.46 million metric tons until 2053 but capital cost is increased from 13.23 b$ in 2023 to 363.11 b$ by 2053.

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Section: Resultsmentioning

confidence: 99%

Managing the low carbon transition pathways through solid waste electricity

Raza,

Aman,

Abbas

et al. 2024

Sci Rep

Self Cite

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“…Intelligent algorithms have the potential to solve many of the problems associated with conventional MPPT methods without complex arithmetic or precise parameters, such as increasing tracking speed, reducing computation time, and minimizing power fluctuations near the GMPP. Widely used intelligent algorithms include fuzzy logic control (FLC) [50], sliding mode control (SMC) [51], artificial neural networks (ANN) [52,53], and Gauss-Newton method [54]. Table 4 compares the parameters of these four intelligent algorithms.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

Maximum Power Point Tracking Technology for PV Systems: Current Status and Perspectives

Yang,

Xie,

Guo

2024

ENERGY

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Maximum power point tracking (MPPT) technology plays a key role in improving the energy conversion efficiency of photovoltaic (PV) systems, especially when multiple local maximum power points (LMPPs) occur under partial shading conditions (PSC). It is necessary to modify the operating point efficiently and accurately with the help of MPPT technology to maximize the collected power. Even though a lot of research has been carried out and impressive progress achieved for MPPT technology, it still faces some challenges and dilemmas. Firstly, the mathematical model established for PV cells is not precise enough. Second, the existing algorithms are often optimized for specific conditions and lack comprehensive adaptability to the actual operating environment. Besides, a single algorithm may not be able to give full play to its advantages. In the end, the selection criteria for choosing the suitable MPPT algorithm/converter combination to achieve better performance in a given scenario is very limited. Therefore, this paper systematically discusses the current research status and challenges faced by PV MPPT technology around the three aspects of MPPT models, algorithms, and hardware implementation. Through indepth thinking and discussion, it also puts forward positive perspectives on future development, and five forwardlooking solutions to improve the performance of PV systems MPPT are suggested.

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

Khan,

Raza,

Faheem

et al. 2024

Engineering Reports

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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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Modeling of intelligent controllers for solar photovoltaic system under varying irradiation conditions

Cited by 7 publications

References 62 publications

Managing the low carbon transition pathways through solid waste electricity

Managing the low carbon transition pathways through solid waste electricity

Maximum Power Point Tracking Technology for PV Systems: Current Status and Perspectives

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

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