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Probability pre-judging and complementing method for missing data of anemometer tower

A wind measuring tower and data technology, applied in the direction of electrical digital data processing, special data processing application, digital data information retrieval, etc., can solve the problem of lack of weather station data, poor linear correlation between weather station and wind measuring tower data, and influence on wind resources Evaluation and power generation calculation and other issues, to achieve the effect of high autocorrelation and satisfactory accuracy

Pending Publication Date: 2022-02-15
YELLOW RIVER ENG CONSULTING
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional supplementary data method is highly dependent on the data of surrounding weather stations, and there is usually a lack of data from weather stations near the proposed wind farm; Affect subsequent wind resource assessment and power generation calculation

Method used

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  • Probability pre-judging and complementing method for missing data of anemometer tower
  • Probability pre-judging and complementing method for missing data of anemometer tower
  • Probability pre-judging and complementing method for missing data of anemometer tower

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] Such as figure 1 As shown, this embodiment is a probabilistic prediction and completion method for the missing data of the wind measuring tower, including the following steps:

[0047] S1. Verification of the rationality of wind measurement data, specifically including the following steps:

[0048] S1-1. Screen the annual wind measurement data one by one;

[0049] S1-2. Set the data that does not meet the measurement standard as unreasonable data;

[0050] S2. Missing test and missing test data verification

[0051] Screen the annual wind measurement data one by one, count unreasonable data, and missing data of wind measurement instrument failure, including the following steps:

[0052] S1-1. Screen the annual wind measurement data one by one;

[0053] S1-2. Set the data as unreasonable data if the average wind speed is greater than 40m / s, the wind speed changes in adjacent time periods exceed 6m / s, and the wind speed difference between adjacent heights exceeds 4m / s...

Embodiment 2

[0069] Such as figure 1 As shown, this embodiment is a probabilistic prediction and completion method for the missing data of the wind measuring tower, including the following steps:

[0070] S1. Verification of the rationality of wind measurement data, specifically including the following steps:

[0071] S1-1. Screen the annual wind measurement data one by one;

[0072] S1-2. Set the data that does not meet the measurement standard as unreasonable data;

[0073] S2. Missing test and missing test data verification

[0074] Screen the annual wind measurement data one by one, count unreasonable data, and missing data of wind measurement instrument failure, including the following steps:

[0075] S1-1. Screen the annual wind measurement data one by one;

[0076] S1-2. Set the data as unreasonable data if the average wind speed is greater than 40m / s, the wind speed changes in adjacent time periods exceed 6m / s, and the wind speed difference between adjacent heights exceeds 4m / s...

Embodiment 3

[0092] Such as figure 1 As shown, this embodiment is a probabilistic prediction and completion method for the missing data of the wind measuring tower, including the following steps:

[0093] S1. Verification of the rationality of wind measurement data, specifically including the following steps:

[0094] S1-1. Screen the annual wind measurement data one by one;

[0095] S1-2. Set the data that does not meet the measurement standard as unreasonable data;

[0096] S2. Missing test and missing test data verification

[0097] Screen the annual wind measurement data one by one, count unreasonable data, and missing data of wind measurement instrument failure, including the following steps:

[0098] S1-1. Screen the annual wind measurement data one by one;

[0099] S1-2. Set the data as unreasonable data if the average wind speed is greater than 40m / s, the wind speed changes in adjacent time periods exceed 6m / s, and the wind speed difference between adjacent heights exceeds 4m / s...

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Abstract

The invention provides a probability pre-judgment completion method for missing data of an anemometer tower. The method comprises the following steps: S1, verifying the reasonability of anemometer data; S2, verifying missing and leakage detection data; S3, removing unreasonable data; S4, verifying the integrity of the wind measurement data; and S5, complementing the missing and leakage detection data. The method solves the problem that the supplementary data of the current anemometer tower is difficult to reflect the real wind speed condition, and has the advantages of scientificity, reasonability and small actual deviation.

Description

technical field [0001] The invention relates to the technical field of wind resource analysis of wind farms, in particular to a method for probabilistic prediction and completion of missing data of an anemometer tower. Background technique [0002] Before the development and construction of wind farms, it is necessary to analyze and demonstrate the wind energy resources in the area, set up wind measurement towers in the area, and collect wind measurement data for a period of not less than one year. Use the measurement data of the wind measuring tower to analyze various indicators of wind resources, evaluate the general situation of wind energy and calculate the power generation. The wind resource assessment process is as follows: figure 1 shown. [0003] Weibull distribution, also known as Weibull distribution or Weibull distribution, is the theoretical basis of reliability analysis and life test. Weibull distribution is widely used in reliability engineering, especially s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/215G06F17/18
CPCG06F16/215G06F17/18
Inventor 姜勃胡会永吉晓红李相华曾桂平王欢欢张鹏
Owner YELLOW RIVER ENG CONSULTING
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