Method for improving ultra-short-term power prediction accuracy of wind power plant
A technology of power forecasting and accuracy, which is applied in the lean development of wind power market and the field of wind power development, and can solve the problems of low accuracy of ultra-short-term power forecasting of wind farms
Active Publication Date: 2020-08-04
BEIJING TIANRUN NEW ENERGY INVESTMENT CO LTD
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Problems solved by technology
However, these methods currently have great limitations in terms of parameter optimization accuracy, prediction effect, and generalization capabilities, so the accuracy of ultra-short-term power prediction for wind farms is very low
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[0073] Before proceeding with the description of the specific implementation, it is necessary to clarify the meaning of the technical terms used:
[0074] 1. Numerical weather prediction: According to the actual situation of the atmosphere, under certain initial and boundary value conditions, numerical calculations are performed by large-scale computers to solve the fluid dynamics and thermodynamic equations describing the weather evolution process, and predict the state of atmospheric movement for a certain period of time in the future and methods of weather phenomena.
[0075] 2. Wind power prediction: establish a prediction model of wind farm output power based on the historical power of the wind farm, historical wind speed, topography, numerical weather forecast, wind turbine operating status, etc., and use wind speed, power or numerical weather forecast data as the model Input, combined with the equipment status and operating conditions of the wind farm unit, predict the ...
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The invention provides a method for improving the ultra-short-term power prediction accuracy of a wind power plant. The method comprises the following steps of 1, performing chaotic theory-based spacereconstruction on a sample; step 2, establishing a neural model based on chaotic time sequence prediction; and step 3, performing ultra-short-term power prediction through the reconstruction space. The ultra-short-term second hour harmonic average accuracy is improved from 56.39% to 90.5%, and two detailed rule assessments are greatly reduced; 2, a sample space is constructed by applying a chaostheory; after a neural model based on chaotic time sequence prediction is constructed and ultra-short-term power prediction is carried out based on a reconstruction space, the accuracy is obviously improved; the important and difficult problems of power prediction lag, inaccurate prediction under continuous change wind speed, wrong trend prediction and the like are solved step by step, and the ultra-short-term work power prediction accuracy is improved from 56.39% to 90.5%.
Description
technical field [0001] The invention relates to the technical field of wind power development, in particular to a method for improving the accuracy of ultra-short-term power prediction of wind farms, and belongs to the technical field of lean development of wind power market. Background technique [0002] In recent years, wind power generation and photovoltaic power generation have developed rapidly. Wind power generation is mainly determined by natural conditions such as wind speed, wind direction, air pressure, temperature, and humidity. Stable operation brings challenges. Wind power prediction (WPP) uses parameters such as gas phase forecast data, historical wind farm operation data, and wind farm operating status data to predict the changing trend of wind power output, bringing positive impacts on power grid security, power dispatching, and power operation, reflecting In: 1) reduce the impact of wind power volatility on the grid stability, improve the robustness of the ...
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Login to View More IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08G06N7/08
CPCG06Q10/04G06Q50/06G06N3/084G06N7/08G06N3/048G06N3/045Y04S10/50
Inventor 朱新宇景志林梁志平刘锐赵德强
Owner BEIJING TIANRUN NEW ENERGY INVESTMENT CO LTD