Optimization strategies for wind turbine and cable layout in wind farms

With the promotion of bidding policies in the wind power industry, the demand for improving profits and reducing costs is becoming increasingly urgent. In the past, manual layout was mostly used for wind turbines and cable layout, and subjective factors had a significant impact, making it difficult to achieve true optimization of machine placement. This study introduces heuristic optimization, fully considers the environmental conditions and factors of wind farms, automatically searches for the optimal wind turbine layout through fuzzy genetic algorithm, fully utilizes wind farm resources, and improves economic benefits. The study also adopted a particle swarm optimization algorithm based on penalty functions to optimize cable layout. This study conducted experiments to verify the effectiveness of the proposed algorithm. The results show that the fitness of fuzzy genetic algorithm reaches 2.827 at around 35 iterations, while genetic algorithm and adaptive differential evolution algorithm reach 1.427 and 1.685 at around 38 and 41 iterations, indicating that fuzzy genetic algorithm can converge faster and has advantages. The optimized cable temperature is approximately 90°C, and the total load of the optimized cable is 6136.57A. Notably, the current carrying capacity of the cables has increased by 15.92%, demonstrating a significant improvement compared to pre-optimization values. These improvements highlight the effectiveness of the proposed fuzzy genetic algorithm in optimizing both wind turbine layouts and cable configurations. The results confirm that the optimization method enhances power generation efficiency and cable load distribution, providing a valuable reference for the design of wind turbine and cable layouts in wind farms.