Wind power prediction method based on adaptive linear logic network

Abstract

The invention, which belongs to the renewable-energy generated power prediction technology field, discloses a wind power prediction method based on an adaptive linear logic network. In the method, historical data of a wind power station is used to carry out adaptive training to the linear logic network, wherein the historical data comprises: historical output of a wind turbine and the historical meteorological data. In a real time usage process, the system carries out prediction to the generated power of the wind power station through collecting value meteorology forecast data, actual measured wind data and real time power generation power data. By using the method of the invention, a prediction wind power modeling process is simple. The process is easy for mathematical analysis. A prediction result is stable and reliable. A training speed is fast. On-line repetition training is supported and a requirement to an operational processor is low. The system can provide a basis for power grid to schedule and compile a wind power station power generation plan curve. When the wind power accesses to the electric power system, an electric power value in an electric power market can be optimized and simultaneously, a beneficial reference can be provided for operation and maintenance of the wind power station.

Description

technical field [0001] The invention relates to a wind power prediction method based on artificial intelligence technology, in particular to a wind power prediction method based on an adaptive linear logic network, and belongs to the technical field of wind power generation power prediction. Background technique [0002] In the early 1990s, European countries have begun to develop wind energy forecasting and forecasting systems and apply them to forecasting services. Forecasting techniques mostly use medium-term weather forecast models nested with high-resolution limited-area models (or nested higher-resolution local area models) and power generation models to forecast the power generation of wind farms, such as the Prediktor forecast developed by Risoe Laboratory in Denmark The system has been applied to the short-term wind energy forecasting business in Denmark, Spain, Ireland and Germany, and the WPPT (wind power prediction tool) developed by the Technical University of D...

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Problems solved by technology

[0006] 1. The predicted wind speed is generally the average wind speed of the wind farm. The influence of the topography of the wind farm on the wind speed is not considered. The wind speed at the wind turbine is not predicted, so it cannot be accurately positioned, and the predicted calculation is generally based on the shear analysis of the exponential function relationship. , the prediction accuracy is poor, and the confidence level is not good

[0007] 2. In the process of using the predicted wind speed to calculate the fan power, a simple conversion method is generally used. The turbulence analysis from low to high places is not enough, and the influence of factors such as towers and wind shear differences are not considered in the wind speed prediction, which cannot be realized. High-precision hourly forecast

[0008] 3. A large number of wind farm areas still lack the original wind measurement data with detailed investigation function, which cannot effectively play the function of the wind power forecasting system. Even a better wind energy forecasting software needs a process of data accumulation

It is difficult for the wind power prediction system to interoperate with different types of wind turbines, which also restricts its application

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