article · Scientific Reports
Partial shading from clouds and buildings creates significant efficiency losses in solar arrays by producing multiple local power peaks. To address this in Maroua, Cameroon, a tracking approach combines a Genetic Algorithm with a Backstepping Controller. The Backstepping Controller adjusts the duty cycle of a Single Ended Primary Inductor Converter to match the reference voltage established by the Genetic Algorithm. By optimising controller gains with the algorithm, the system accelerates tracking and suppresses oscillations around the global maximum power point. This design targets the global optimum rather than local maximum power points, which typically cause energy losses in shaded conditions. The technique stabilises in fewer than three iterations after shading occurs and delivers at least a 33 percent reduction in power losses compared to conventional control alternatives.
Solar panels lose substantial efficiency when partial shadows trick standard controllers into settling on inferior power peaks. By rapidly locating the true global maximum power point rather than a local maximum, this approach helps solar energy systems maintain high electrical output despite intermittent cloud cover and urban obstruction.
This technology is relevant to solar inverter manufacturers and renewable energy system integrators seeking to improve energy capture in partially shaded or urban environments. The abstract demonstrates applied research comparing control methods on a specific converter configuration, suggesting the technology is at an applied development stage that requires real-world industrial testing prior to commercial integration.
AI-generated from the published abstract. Always read the original work before citing.
As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua's dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua's solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses.
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DOI: 10.1038/s41598-024-53721-w
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