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Uncertainty analysis method and system for wind power plant prediction

An analysis method, wind farm technology, applied in forecasting, system integration technology, information technology support system, etc.

Active Publication Date: 2019-12-10
CHINA ELECTRIC POWER RES INST +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology helps predict future energy production from wind farns more accurately than previous methods that were developed for older systems. By analyzing specific areas within these models, we can determine how well they are performing at certain points during their lifespan or failure mode. These results help us improve understanding about potential issues related to renewable sources such as solar panels.

Problems solved by technology

The problem addressed by this patented method relates to accurately estimating the amount of electricity generated from wind turbines due to environmental factors such as temperature variations that can affect their performance significantly during different stages of operation (either at startup) without being limited by current methods like time series models used on older systems). This limitation makes it difficult to use these techniques effectively across multiple generations of wind energy production.

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  • Uncertainty analysis method and system for wind power plant prediction
  • Uncertainty analysis method and system for wind power plant prediction
  • Uncertainty analysis method and system for wind power plant prediction

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Embodiment 1

[0065] figure 1 It is a flow chart of an uncertainty analysis method for wind farm prediction in the present invention, such as figure 1 shown, including:

[0066] S1. Based on the pre-acquired wind farm data processing, the turbulence intensity set is obtained;

[0067] S2. Dividing the turbulence intensity set according to a preset division threshold to obtain a turbulence intensity level;

[0068] S3. Combining the turbulence intensity levels and the conditional quantile regression model constructed based on each turbulence intensity level, to obtain uncertainty information of wind farm prediction under each turbulence intensity level.

[0069] The algorithm basis of the present invention is quantile regression estimation, that is, adopting the idea of ​​regression model between the conditional quantiles of the wind power prediction variable X and the wind power measured variable Y, adding the micro-regional turbulence intensity variable Z formed by the measured wind spee...

Embodiment 2

[0077] The input wind farm data involved in the present invention include: wind farm anemometer tower data, predicted power and measured power, wherein in this embodiment, the first sampling period is set to 5min, and the second sampling period is set to 15min, i.e. time resolution The time is 15 minutes. Among them, the wind farm anemometer tower data and the measured power are all from reliable sampling points of the wind farm. The specific technical steps used include:

[0078] S1. Based on the pre-acquired wind farm data processing, the turbulence intensity set is obtained;

[0079] (1) Basic data preparation.

[0080] According to the start and end time of the basic data set for wind power prediction uncertainty analysis, real-time meteorological observation data of wind farms are extracted, that is, information such as instantaneous wind speed, wind direction, and sampling period given by the anemometer at the hub height of the wind farm fan.

[0081] The data set is ob...

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Abstract

The invention discloses an uncertainty analysis method and system for wind power plant prediction. The method comprises: obtaining turbulence intensity levels from obtained wind power plant data according to a preset division threshold value; constructing a conditional quantile regression model based on the turbulence intensity level, the actually measured power and the predicted power; and combining the turbulence intensity levels and a conditional quantile regression model under each turbulence intensity level to obtain the uncertainty of the predicted power. On the basis that uncertainty isdetermined through a quantile regression method, a conditional quantile regression model is established according to research on statistical characteristics of turbulence intensity variables under all turbulence intensity levels, and therefore an original wind power prediction uncertainty analysis method is partially optimized.

Description

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Claims

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Application Information

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Owner CHINA ELECTRIC POWER RES INST
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