article · Energies
Solar photovoltaic systems rely on maximum power point tracking algorithms to enhance energy efficiency and reduce panel requirements. These algorithms vary across design complexity, implementation mode, sensor needs, convergence speed, and hardware expenditure. A systematic literature review and simulation-based benchmarking examined the leading methods to identify their operational strengths and weaknesses. Quantitative and qualitative evaluations reveal that intelligent algorithms consistently outperform conventional alternatives in tracking efficiency. Specifically, artificial neural network methods achieved the top performance with an efficiency of 98.6 per cent. Closely following were adaptive neuro-fuzzy inference systems at 98.34 per cent and fuzzy logic control at 98.29 per cent. Consequently, intelligent approaches represent strong candidates for integration into future photovoltaic systems to maximise energy yield and overall system power output.
Solar power provides a sustainable pathway for clean energy generation. Choosing an optimal maximum power point tracking algorithm directly boosts solar panel electrical efficiency. This improvement allows operators to lower overall installation costs by generating the desired electrical output with fewer solar panels.
The findings guide solar system designers and engineers in selecting software algorithms for photovoltaic controllers. Because the benchmarking relies on simulation rather than hardware deployment, the work represents early-stage applied research. Adopting these high-efficiency intelligent techniques, particularly artificial neural networks, could help equipment developers reduce the panel surface area needed to achieve targeted power outputs.
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There are a variety of maximum power point tracking (MPPT) algorithms for improving the energy efficiency of solar photovoltaic (PV) systems. The mode of implementation (digital or analog), design simplicity, sensor requirements, convergence speed, range of efficacy, and hardware costs are the primary distinctions between these algorithms. Selecting an appropriate algorithm is critical for users, as it influences the electrical efficiency of PV systems and lowers costs by reducing the number of solar panels required to achieve the desired output. This research is relevant since PV systems are an alternative and sustainable solution for energy production. The main aim of this paper is to review the current advances in MPPT algorithms. This paper first undertakes a systematic literature review (SLR) of various MPPT algorithms, highlighting their strengths and weaknesses; a detailed summary of the related reviews on this topic is then presented. Next, quantitative and qualitative comparisons of the most popular and efficient MPPT methods are performed. This comparison is based on simulation results to provide efficient benchmarking of MPPT algorithms. This benchmarking validates that intelligent MPPTs, such as artificial neural network (ANN), fuzzy logic control (FLC), and adaptive neuro-fuzzy inference system (ANFIS), outperform other approaches in tracking the MPPT of PV systems. Specifically, the ANN technique had the highest efficiency of 98.6%, while the ANFIS and FLC methods were close behind with efficiencies of 98.34% and 98.29%, respectively. Therefore, it is recommended that these intelligent MPPT techniques be considered for use in future photovoltaic systems to achieve optimal power output and maximize energy production.
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DOI: 10.3390/en16083509
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