A Comprehensive Evaluation Method for Wind Farm Power Prediction

Abstract

The invention discloses a wind power prediction comprehensive evaluation method, which comprises the following steps: 1) determining the wind power prediction evaluation index and the evaluation grade of each index, and constructing a multi-level evaluation index framework; 2) using a cloud model to determine the index of each level Weight; 3) Construct a comprehensive cloud model of multi-level indicators belonging to each assessment level; 4) According to the actual wind farm index data, use the comprehensive cloud model to calculate the membership degree of each multi-level indicator belonging to each assessment level, through the membership degree Multiply with the weights of all levels of indicators to obtain the membership degree evaluation value matrix of the first-level indicators; 5) Select the column with the highest value in the membership degree evaluation value matrix of the first-level indicators, and the evaluation level represented by this column is the wind farm power prediction The final grade of the comprehensive evaluation. The invention realizes the scientific evaluation of wind power prediction, and has certain significance for guiding wind farms to improve the power prediction accuracy and satisfying the actual needs of the dispatching side.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a comprehensive evaluation method for wind farm power prediction. Background technique [0002] In recent years, renewable energy such as wind power has achieved rapid development, and the proportion of total installed capacity of wind power in the power grid has continued to increase. Different from conventional thermal power generation units, wind power, photovoltaic and other renewable energy sources are affected by factors such as wind speed, wind direction, and light intensity, and their output power has the characteristics of randomness, volatility, and intermittency. Large-scale grid connection will have adverse effects on the safety, stability and economic operation of the power system. [0003] A more accurate prediction of the output of wind farms can reduce the adverse impact of wind power output fluctuations on power system dispatching, and is the key t...

AI Technical Summary

Problems solved by technology

However, there are several problems in the existing evaluation system: (1) For a certain index, the judgment is only made according to a certain fixed threshold, and the actual predictions of the two evaluation objects closely distributed above and below the threshold are very close, but the evaluation results are different. level, this evaluation method is unreasonable

(2) For the power station to be evaluated, when its different evaluation indicators belong to different evaluation grades, it is difficult to obtain reasonable results for the final grade evaluation of the power station

In addition, the existing prediction and evaluation models lack the analysis of a large amount of measured data, and it is difficult to meet the needs of power grid dispatching for power prediction and evaluation results.

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