article · International Transactions on Electrical Energy Systems
Solar photovoltaic systems rely on maximum power point tracking methods to extract the highest possible amount of electrical energy from solar panels and deliver it to connected loads. This review maps the historical progression of photovoltaic cell research across decades and examines emerging technological trends. It provides a structured overview and comparative assessment of both conventional and artificial intelligence control techniques used in photovoltaic installations. Particular focus is given to performance under uniform illumination as well as complex partial shading conditions, detailing the operational strengths and weaknesses of each controller category. In addition, the work compiles and analyses operational datasets extracted from existing literature on diverse control processes, offering a consolidated reference resource to guide future investigations into advanced power tracking and sustainable energy system control.
Solar panels frequently lose efficiency when light conditions vary or when shadows fall across parts of an installation. Understanding how different control algorithms, from traditional systems to artificial intelligence, perform under partial shading helps developers and system designers identify optimal methods to capture maximum energy, making solar power systems more reliable and productive.
The review informs the selection of controller algorithms for solar equipment manufacturers, energy developers, and control system engineers seeking to improve power yield in shaded or changing environments. Because this is a broad review and literature synthesis that assesses existing methods and datasets rather than introducing a newly tested hardware implementation, the insights are at an early or advisory stage for commercial engineering teams evaluating algorithm designs.
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Due to their inherent ability and environmentally friendly nature, renewable energy sources are the only real option for producing pollution-free energy in the modern era. Solar energy is one of the best possibilities in this family for supplying civilization with the power and energy it needs. Researchers can efficiently boost a PV panel’s efficiency by using the maximum power point tracking (MPPT) approach to extract the most power from the panel and send it to the load. The authors of this study examined and surveyed the sequential advancement of solar PV cell research from one decade to the next, and they elaborated on the upcoming trends and behaviours. Many maximum power point tracking algorithms (MPPTs) that are employed in photovoltaic systems (PVSs) that function under both uniform and partial shade situations are structurally summarized in this work. Well-written descriptions of the features of photovoltaic modules are followed by a variety of effective control strategies, including both AI-based and traditional controllers. In addition, appropriate knowledge of the various controllers is essential when the PV system is exposed to partial shade, keeping in mind the different control systems’ classifications in this situation. A thorough analysis of several soft computing-based techniques is also included, as well as many classical controller-based PV systems. First, well-developed traditional MPPT methods are used, followed by artificial intelligence-based MPPT approaches. Later, a thorough comparison of the various MPPT-controlling approaches is established. For PV systems operating under partial shade conditions (PSCs), the advantages and disadvantages of the various MPPT techniques are outlined, contrasted, and assessed. Future research directions for MPPT are also being investigated. A collection of several datasets pertaining to various control processes that were gleaned from various research articles has also been presented. Researchers working on PV-based MPPT and those working in the sectors of renewable energy production and environmentally sustainable development would be very interested in the findings of this review study.
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DOI: 10.1155/2024/8363342
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