This invention discloses a PIDNN-SO-based method for optimizing the
layout of offshore wind farms, belonging to the field of wind farm optimization
design technology. The method first inputs wind farm,
turbine, and
wind resource parameters, sets constraints on
turbine spacing, boundaries, and wake overlap rate, and constructs a high-precision wake flow field
physical model based on
actuator disk theory, Navier-Stokes equations, and a weighted quadratic wake superposition model. Secondly, it builds a
physical information dual neural network (PIDNN) with dual encoders, integrating physical equation loss and
data loss to
train the model, achieving rapid and accurate prediction of wake
wind speed. Then, it uses the snake optimization
algorithm (SO) to optimize the environmental evolution factor, completing an efficient search for
turbine layout. Finally, it constructs a PIDNN-SO collaborative optimization framework, trains a PIDNN
surrogate model through initial sampling, and outputs the optimal
layout that maximizes annual power generation. This invention can effectively reduce wake loss and significantly improve the power generation efficiency of offshore wind farms.