Multi-layer collaborative real-time classification early warning method for wind power climbing event

Abstract

The invention discloses a multi-layer cooperative real-time classification early warning method for a wind power climbing event. The method comprises the following steps: 1, obtaining wind power historical data for preprocessing; 2, formulating a wind power climbing event classification strategy; 3, establishing a multi-layer collaborative prediction model of a decomposition layer, a prediction layer, a correction layer and a feedback layer; and 4, identifying different types of wind power climbing events to carry out real-time early warning. According to the method, EMD decomposition, GRU prediction, SVR correction and multi-step rolling prediction of actual measurement information feedback are utilized, real-time early warning is carried out after different types of wind power climbing events are recognized according to the wind power climbing event classification criterion, corresponding measures can be taken in time before the climbing events happen, and therefore safe and stable operation of a power system is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of power system prediction and control, and in particular relates to a multi-layer collaborative real-time classification and early warning method for wind power power ramp events. Background technique [0002] In order to cope with the shortage of traditional fossil energy and severe environmental problems around the world, the proportion of new energy connected to the grid is increasing. Wind energy, as one of the green and clean renewable energy sources, has been rapidly developed and widely used around the world. With the increase of wind power penetration rate, affected by extreme weather, the wind power power changes greatly in a short period of time, and wind power power climbing events are very likely to occur, resulting in a large shortage of power in a short period of time in the system, affecting peak regulation and frequency regulation, resulting in Mass load loss. Therefore, accurate predictio...

AI Technical Summary

Problems solved by technology

The most widely used method is the indirect prediction method, which is to identify slope events based on wind power prediction information. However, in order to improve the overall prediction accuracy in wind power prediction, this type of method often ignores extreme data, which leads to the inaccuracy of slope information. missing, resulting in a low probability of correct prediction of the ramp event

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