Multi-point wind speed prediction method in wind power plant based on convolutional recurrent neural network

Abstract

The invention provides a multi-point wind speed prediction method in a wind power plant based on a convolutional recurrent neural network, and the method comprises the following steps: 1, collecting the operation data of the wind power plant, and enabling the collected data to comprise the actual measurement wind speeds and actual measurement wind directions at the positions of a plurality of windturbine generators; 2, establishing a convolution module of a multi-point wind speed prediction model in the wind power plant based on a convolution recurrent neural network according to the data acquired in the step 1; 3, establishing an LSTM module of a multi-point wind speed prediction model in the wind power plant based on the convolutional recurrent neural network according to the step 1; 4,connecting the output of the convolution module with the output of the LSTM module; and 5, training a neural network model by using a mean absolute error (MAE) loss function index. For a power grid,optimization of power grid dispatching and reduction of spinning reserve capacity are facilitated, safe, reliable and economical operation of a power system is guaranteed, and the fatigue load of a unit is reduced.

Description

technical field [0001] The invention belongs to the technical field of new energy power generation, and in particular relates to a multi-point wind speed prediction method in a wind farm based on a convolutional cyclic neural network. Background technique [0002] Wind power has entered the stage of development from supplementary energy to alternative energy. However, wind power output has random fluctuations, and large-scale wind power grid integration will pose a serious threat to the safe and stable operation of the power system. High-precision ultra-short-term wind speed prediction for wind farms, on the one hand, helps optimize grid dispatching and reduce spinning reserve capacity for the power grid, ensuring safe, reliable and economical operation of the power system. On the other hand, for wind farms, due to the influence of wake effects, the optimal control of a single machine in a wind farm is difficult to ensure the optimal output of the entire farm. Knowing the ...

AI Technical Summary

Problems solved by technology

However, in the current various wind speed prediction methods, only the wind speed time series signal of a single point is considered, and only the historical data and real-time data of the wind speed at a single point are often needed in the prediction process, so the system of the prediction method and the prediction accuracy of the model are still to be determined. Further improve and improve

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