An ultra-short-term wind power prediction method based on hybrid intelligent technology

Abstract

The invention discloses an ultra-short-term wind power prediction method based on a hybrid intelligent technology, to address the unpredictable challenges of ultra-short-term wind power generation. The proposed method employs a series of data processing techniques on the basis of available raw data, including input variable selection based on statistical analysis, attribute reduction based on principal component analysis (PCA), and attribute reduction based on K-Means clustering algorithm to obtain more relevant and efficient concentrated data as the input information of the prediction. The proposed method uses adaptive neuro-fuzzy inference system (ANFIS) to train and learn the input information in order to obtain the output prediction results. Particle swarm optimization (PSO) algorithmis used to optimize the parameters of ANFIS in order to reduce the prediction error. The hybrid intelligent method is evaluated by the forecasting results of actual wind farms. Experiments show that the method can achieve effective forecasting accuracy.

Description

technical field [0001] The invention relates to the field of wind power prediction for new energy power generation, in particular to an ultra-short-term wind power prediction method based on hybrid intelligent technology. Background technique [0002] The wind power forecasting system is of great significance to the operation of the power system connected with a large amount of wind power. The power system is a complex dynamic system, and it is the responsibility of the power grid to maintain the power balance among power generation, transmission and consumption. In the power system without wind power, the power grid dispatching organization can formulate a power generation plan according to the daily load curve to meet the power demand of the next day. The output power of wind farms is fluctuating and intermittent. The large-scale access of wind power has greatly increased the difficulty of making power generation plans. Wind power has brought great challenges to the dispa...

AI Technical Summary

Problems solved by technology

However, it is difficult for a single technology to achieve high-precision forecasting, and it is impossible to fully explore the relationship between future wind power output and various factors. Coupled with the influence of noise data, ultra-short-term wind power forecasting has become a multi-variable and highly nonlinear complex problem.

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