Generation Prediction Of Ultra-Short-Term Wind Farm Based On Quantum Genetic Algorithm And Fuzzy Neural Network

Wulanchabu City, Inner Mongolia Autonomous Region, as a typical area rich in new energy in China, its new energy power generation accounts for more than 30% of the total power generation. However, due to the intermittent and instability of wind power generation, the impact of wind farms on the power grid is huge after grid connection. Therefore, more accurate prediction of wind power system power generation is needed to reduce the impact of new energy power generation on the main grid. Aiming at the characteristics of non-linearity and non-stationarity of wind power, a wind power prediction method based on the combination of quantum genetic algorithm and fuzzy neural network is established in this paper to predict the wind power of wind farm in the short term. The prediction results show that no matter where the wind power is relatively smooth or where the wind power is suddenly changed, the change trend can be effectively tracked, and the prediction accuracy rate is 87.12%.