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Intelligent wind power prediction system

A wind power prediction and wind power technology, which is applied in wind power generation, circuit devices, AC network circuits, etc., can solve the problems of lack of wind farm power prediction and prone to errors, etc.

Active Publication Date: 2017-03-15
北京壬工智能科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although this method can calculate the power of wind farms through wind speed, it lacks power prediction for short-term wind farms and is prone to errors

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] Such as figure 1 As shown, the wind farm power forecasting system is divided into four modules.

[0058] Mesoscale numerical simulation module 100: a numerical model developed according to the weather and climate characteristics of my country and combined with the actual needs of wind farms. Fine adjustments can be made according to the regional environment of the wind farm to control the prediction error from the source of the calculation and improve the accuracy.

[0059] Micro-scale numerical simulation module 110: using the boundary layer wind field diagnosis model, the prediction results of the mesoscale model can be downscaled to obtain wind field data with extremely high spatial precision. In the model calculation, various factors affecting the temporal and spatial distribution of wind direction and wind speed, such as terrain, landform, roughness, and thermal stability inside the wind farm, are fully considered to make wind energy prediction more refined and ac...

Embodiment 2

[0065] Such as figure 2 , 2a As shown in , 2b, 2d, and 2e, the micro-scale numerical simulation module predicts the wind speed through a multi-model forecast model.

[0066] Topographical conditions and solar radiation intensity are two important factors affecting wind speed, which lead to different wind speed patterns for different terrains, different weather conditions, different seasons, and day and night. figure 2 The wind speed series under five typical weathers provided by the meteorological department are given, and it can be seen that the wind speed patterns are different under different weather conditions. We study the diurnal cycle of wind turbulence intensity and the wind turbulence intensity in different seasons and at different altitudes, as follows: Figure 2a , 2b , 2c shown.

[0067] Figure 2a It shows that the daytime wind speed turbulence intensity is much higher than the nighttime turbulence intensity in a day, indicating that the patterns of wind sp...

Embodiment 3

[0072] Such as image 3 , 3a As shown in , 3b, 3c, and 3d, the micro-scale numerical simulation module predicts the wind speed through a multi-scale forecast model.

[0073] 1) Short-term wind speed multi-scale forecast model based on statistical forecast model

[0074] There are many factors that affect wind speed (such as temperature, air pressure, surface roughness, atmospheric circulation, etc.), and the mechanism of action is complex. The wind speed signal shows strong multi-scale characteristics, that is, the signal frequencies generated by different sources are different, and the final wind speed sequence It can be seen as the result of multiple signals being coupled together. In recent years, scholars at home and abroad have begun to pay attention to the multi-scale characteristics of wind speed. The widely used idea is: firstly, the original wind speed sequence is decomposed into subsequences of different frequencies, a regression model is established on each subseq...

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Abstract

The invention provides an intelligent wind power prediction system, which comprises a mesoscale numerical value simulation module, a micro-scale numerical value simulation module, a generating capacity physics calculation module and an error correction statistics module, wherein the mesoscale numerical value simulation module is used for predicting wind power according to the actual requirements of a weather climate characteristic set wind power plant; the micro-scale numerical value simulation module is used for carrying out downscaling processing on a wind power prediction result obtained by a mesoscale numerical value simulation system; the generating capacity physics calculation module is used for calculating the generating capacity of each machine position according to a wind profile result generated in the micro-scale numerical value simulation module; and the error correction statistics module is used for building an error correction model through analyzing and computing an error between the prediction result and the actual generating capacity. According to the intelligent wind power prediction system, the functions of short-term and super-short-term multi-mode multi-scale wind power prediction, wind speed / wind power prediction fused with numerical weather prediction, wind power integration stable control, wind power curve reporting and optimizing and the like can be achieved.

Description

technical field [0001] The invention relates to the technical field of power generation, in particular to an intelligent wind power prediction system for wind power. Background technique [0002] Wind power is a renewable and clean energy. Wind power generation is an important form of wind energy utilization, and wind energy is a renewable, pollution-free, energy-rich and promising energy. Wind power technology and equipment is an important part of the wind power industry, as well as the foundation and guarantee for the development of the wind power industry. [0003] At present, China's wind power grid-connected installed capacity is the largest in the world. At the end of 2012, the wind power grid-connected installed capacity exceeded 60 million kilowatts, and the power generation exceeded 100 billion kWh, making it the third largest power source in my country after thermal power and hydropower. Global wind power is developing rapidly, and more than 70 countries have bui...

Claims

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Application Information

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IPC IPC(8): H02J3/46
CPCH02J3/46H02J2203/20Y02A30/00Y02E10/76
Inventor 梁轶李翕含刘鑫
Owner 北京壬工智能科技有限公司
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