Wind Speed And Power Prediction Of Large-scale Wind Farm Considering Wind Speed Attribute Characteristics

The rational development and utilization of renewable energy is currently the central project in our country.After the goal of "double carbon" was put forward in the 14 th Five-Year Plan,the power industry will focus on the construction of new energy power system with wind,light and other resources.Wind power generation,as one of the more mature renewable energy generation technologies,has become an important part of energy generation in our country and even in the world.The intermittenity and volatility of wind speed have great influence on the utilization of wind energy.Therefore,combining the national key research and development program(2022YFB2404001)with the national key research and development program(2021YFB2400802),this paper takes the wind speed attribute data as the basis and mining the spatio-temporal correlation characteristics between the wind speed attribute information.Then the accurate wind speed prediction of the fan in the wind farm group is achieved.And further through the study of wind speed-power conversion,it is of great significance for the power system to absorb new energy,flexible scheduling and safe operation.This paper expounds the background significance of wind power forecasting technology and the importance of research.Data preprocessing is carried out for wind speed and wind power prediction considering wind speed attribute characteristics and the prediction evaluation index is constructed.The fast correlation filtering algorithm was used to sort and screen the correlation attributes of wind speed sequence.Based on this,the improved K-mediods method was used to cluster wind farms.Based on the improved grey correlation degree analysis of wind speed attribute sequence,the multi-order neighborhood of typical fans was divided,and the wind speed information matrix was reconstructed to improve the overall prediction efficiency.Based on deep learning theory,a general framework for wind farm wind speed and wind power prediction considering wind speed attribute characteristics is proposed.Based on the idea of convolutional neural network,dual layer memory unit is introduced to construct self-correcting convolutional bilevel memory network.The reconstructed space-time multi-dimensional information is input into the convolutional double-layer memory network,wind speed information is reduced by the convolutional neural network,and spatial feature extraction,and then the double-layer memory neural network is used for multi-position and multi-step ultra-short-term prediction.At the same time,self-correcting error correction units are introduced into the memory network based on the principle of reverse error propagation,which improves the accuracy and generalization ability of the model.The wind speed of the actual wind farm is predicted to verify the effectiveness of the method in this paper.Due to the large errors in the conversion according to the standard wind speed power characteristic curve,in order to enhance the accuracy of wind speed and power conversion and thus improve the accuracy of indirect prediction of wind power,this paper uses the measured data to construct a more regular wind speed power characteristic curve.A spatially related optimization method of time sequence control is constructed to search the representative fan in each cluster group after clustering,so as to construct the equivalent wind power prediction model of wind farm.By using the measured data to simulate the wind farm,the accuracy of the indirect wind power prediction model in this paper is verified.