Comprehensive error correction method of short-period wind power prediction system

Abstract

The invention discloses a comprehensive error correction method of a short-period wind power prediction system. The method comprises wind power plant power output link error correction and numerical value weather forecast link error correction; the step of correcting an error of a wind power plant power output link comprises the steps of calculating the best modeling granularity of a power output model, accumulating after modeling by a plurality of fans instead of a single unit, rejecting an abnormal data point on a scatter diagram of the power output model with a times-variance method, and correcting the system error of the power output model by related factors; the step of correcting an error of a numerical value weather forecast link comprises the steps of obtaining a leading value weather forecast wind speed sequence by comparing actually measured wind speed with related coefficients of the numerical value weather forecast wind speeds in different time and space and correcting the system error, and correcting a cold front arrival time-delay error by a correlation analysis method. As to the problem of low input data quality of the existing short-period wind power prediction system, the comprehensive error correction method is generally applicable to various short-period wind power prediction methods, and can be conveniently applied to actual engineering, so that the modeling precision and the prediction precision of the short-period wind power prediction can be obviously improved.

Description

technical field [0001] The invention belongs to the technical field of power system prediction and control. In particular, it relates to a comprehensive error correction method for a short-term wind power forecasting system. Background technique [0002] With the increase of the proportion of wind power installed capacity in the power system, due to the inherent volatility, intermittency and uncontrollability of wind power, the traditional power system power generation planning based on the controllability of power supply and the predictability of load will change. become increasingly difficult. After long-term research and exploration in developed countries with wind power, it has been found that short-term wind power forecasting (0-48 hour forecasting) can effectively reduce the adverse impact of wind energy on power system dispatching, optimize day-ahead dispatching of power grids, improve wind power transmission efficiency, and repair and maintain wind turbines. aspect...

AI Technical Summary

Problems solved by technology

In particular, my country's wind power is in the stage of rapid and large-scale development. Many wind farms are built in areas with harsh environments, and the communication environment and equipment maintenance level is relatively lagging behind. Improper data preprocessing methods in the production process can easily have a great impact on statistical data, thus becoming a more significant source of error in the output power model

On the other hand, for short-term wind power prediction, due to the existence of numerical weather prediction errors, it is difficult to improve the prediction accuracy through the improvement of prediction algorithms.

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