研究目的
To enhance the efficiency of a photovoltaic (PV) array by incorporating artificial intelligence techniques, specifically a genetic algorithm-based optimization technique, to track maximum power under varying environmental conditions.
研究成果
The GA-based MPPT controller can track maximum power with reduced oscillation and faster tracking speed compared to the conventional perturb & observe algorithm. The proposed technology is able to harvest maximum available power with significantly higher efficiency. Future studies may focus on the ability of the controller to track maximum power during a high dimensional change in local and global optima.
研究不足
The study focuses on the performance of the GA-based MPPT controller under varying environmental conditions but does not address the controller's ability to track maximum power during high dimensional changes in local and global optima. Future studies may focus on this aspect.
1：Experimental Design and Method Selection:
The study employs a genetic algorithm (GA) approach for controlling a DC/DC boost converter to enhance the performance of a PV array. The duty cycle of the boost converter is adjusted to achieve maximum power tracking.
2：Sample Selection and Data Sources:
The performance of the algorithm was tested under various environmental conditions using MATLAB/Simulink. A comparative study was conducted on the PV system using the conventional perturb & observe algorithm and the genetic algorithm.
3：List of Experimental Equipment and Materials:
The study uses a PV array composed of 96 PV cells connected in series forming a PV module; 5 modules are connected in series and 30 modules are connected in parallel. A boost converter is used to obtain a high regulated output voltage from the unregulated input voltage.
4：Experimental Procedures and Operational Workflow:
The GA optimization process starts with the population of randomly generated individuals. Each individual in the population represents one point in the search space, and is coded as a string of binary. The fitness of each individual in the population is evaluated using the fitness function. GA operators such as selection, mutation, and crossover are used to create the next generation of the population.
5：Data Analysis Methods:
The performance of the proposed algorithm was compared with the conventional perturb & observe algorithm under various environmental conditions.
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