Ultra-short-term wind power forecasting method based on self-learning arma model with real-time correction

Abstract

The invention discloses a real-time corrected self-learning ARMA model wind power ultra-short-term prediction method, which includes inputting data to obtain autoregressive sliding average model parameters; inputting wind resource monitoring system data and operation monitoring system data, and real-time correction according to the operation monitoring data Start-up capacity; establish an autoregressive moving average model to obtain ultra-short-term forecast results of wind power; introduce real-time anemometer data to correct the ultra-short-term forecast results of wind power in real time. By predicting the wind power in the process of wind power generation, and by introducing real-time wind tower data to correct the ultra-short-term prediction results of wind power in real time, it overcomes the defect of low ultra-short-term prediction accuracy of wind power in the existing ARMA technology and achieves high precision. The purpose of ultra-short-term forecasting of wind power.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting in the process of new energy power generation, in particular to a self-learning ARMA model wind power ultra-short-term forecasting method for real-time correction of wind measuring networks. Background technique [0002] Most of the large-scale new energy bases generated after my country's wind power enters the stage of large-scale development are located in the "three north regions" (Northwest, Northeast, and North China). Large-scale new energy bases are generally far away from the load center, and their power needs to be transmitted to load center for consumption. Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-scale new energy bases will fluctuate in a large range, which will further lead to fluctuations in the charging power of the transmission network, which will affect the safe...

AI Technical Summary

Problems solved by technology

The ARMA method has many advantages, so it is widely used in ultra-short-term forecasting of wind power, but the biggest disadvantage of ARMA is the hysteresis of its prediction, that is, when the wind power output changes, the change speed of the ARMA predicted result is generally slower than the actual wind power output change. The speed, therefore, strongly affects the predictive accuracy of ARMA

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