Using BP Neural Networks to Seek Optimal Algorithms for Wind Turbine Systems

In addressing the problem discussed in this paper, we can employ Backpropagation (BP) neural networks to identify optimal algorithms for wind turbine systems. By utilizing BP neural networks, we can achieve more precise optimization of wind turbine parameters – such as blade pitch control, generator torque regulation, and yaw alignment – thereby enhancing overall performance and efficiency. This technique typically involves training the network with historical operational data (e.g., wind speed, power output, turbine stress) and implementing gradient descent optimization with activation functions like sigmoid or ReLU to minimize cost functions related to energy output and equipment wear. Such optimization would make wind turbines more competitive in energy generation and better equipped to meet evolving energy demands. Consequently, adopting BP neural networks to determine optimal algorithms for wind turbines represents a highly effective and viable methodology, where code implementation might include TensorFlow or PyTorch frameworks for network architecture design and MATLAB for simulating turbine dynamics.