Behavioral modeling of a hybrid solar–wind energy system with PSO–GA optimization under heavy partial shading

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Chinese explanation / 中文解读

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Original abstract

An accurate determination of photovoltaic (PV) model parameters is particularly important in order to predict I–V and P–V characteristics, maximum power point tracking (MPPT), energy yield analysis and performance assessments of hybrid renewable energy systems. In this work, we propose a hybrid Particle Swarm Optimization–Genetic Algorithm (PSO–GA) for predicting the first five parameters of the single-diode PV model namely series resistance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="bold-italic">s</mml:mi> </mml:mrow> </mml:msub> </mml:math> shunt resistance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="bold-italic">p</mml:mi> </mml:mrow> </mml:msub> </mml:math> , diode saturation current <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">I</mml:mi> </mml:mrow> <mml:mrow> <mml:mrow> <mml:mi mathvariant="bold-italic">s</mml:mi> <mml:mi mathvariant="bold-italic">a</mml:mi> <mml:mi mathvariant="bold-italic">t</mml:mi> </mml:mrow> </mml:mrow> </mml:msub> </mml:math> , photo-generated current <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">I</mml:mi> </mml:mrow> <mml:mrow> <mml:mrow> <mml:mi mathvariant="bold-italic">p</mml:mi> <mml:mi mathvariant="bold-italic">h</mml:mi> </mml:mrow> </mml:mrow> </mml:msub> </mml:math> , and diode ideality factor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">Q</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="bold-italic">d</mml:mi> </mml:mrow> </mml:msub> </mml:math> . It combines the global search ability of PSO and features in diversity-preserving and exploratory of GA for better convergence performance and avoidance of local optima. Using standard test conditions, a 96-cell monocrystalline PV module was modeled using MATLAB/Simulink. An optimization objective was developed to minimize the error between simulated PV characteristics and their manufacturer's datasheet values. Optimized parameters were iteratively verified through I–V and P–V curves, MPPT performance evaluation, array scaling and partial shading studies. It is shown that the hybrid PSO–GA algorithm obtains lower estimation error values and better convergence performance than the single-stage PSO and GA approaches. In this framework, the PSO step offers faster global exploration by changing particle positions and velocities through inertia, cognitive as well as social learning mechanisms. The GA stage is improved by using selection, crossover, and mutation operations to maximize solution quality while increasing robustness of our optimization and avoiding premature convergence. A fitness-based analysis approach is utilized for high-fidelity optimization assessment. In addition, the analysis examines the torque–wind speed aspect of the wind subsystem and combines the solar and wind resources to a hybrid solar–wind system. The hybrid design improves the availability of energy and system reliability, particularly at partial shading. The resulting PSO–GA framework is considered an efficient and robust approach to PV parameter estimation, MPPT optimization, and renewable energy system hybridization modeling.

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