Short-term wind power prediction method for weather fluctuation process division and matching

Abstract

The invention relates to a short-term wind power prediction method for weather fluctuation process division and matching, and the method comprises the steps: firstly constructing a historical data setand a current data set for weather fluctuation process division; secondly, constructing a historical combined weather fluctuation characteristic matrix as a clustering object; then, constructing a current weather fluctuation characteristic matrix; then, calculating the membership degree of the current weather fluctuation process and each historical weather fluctuation process aggregation, and determining the optimally matched historical weather fluctuation process aggregation; finally, predicting the wind power in the current weather fluctuation process based on an artificial intelligence prediction algorithm; and obtaining the wind power of the current circulation day from the next zero day to the third day. According to the method, refined division of the weather fluctuation process isachieved, multi-dimensional fluctuation characteristic parameter extraction and weather fluctuation characteristic matrix construction are achieved, a more accurate training sample is provided for short-term wind power prediction, and a more accurate short-term wind power prediction value from the next zero day to the next three days is obtained.

Description

Technical field[0001]The invention relates to the field of power system operation and control, in particular to a short-term wind power prediction method for dividing and matching weather fluctuation processes.Background technique[0002]As one of the most rapidly developing forms of renewable energy power generation in recent years, wind power has become the best choice to promote the revolution of energy production and consumption in my country and realize the sustainable development of energy and power. However, the volatility of wind resources determines that wind power is also highly volatile and intermittent. With the continuous increase of installed wind power capacity, the integration of a large amount of wind power into the power grid is bound to bring about the safe and stable operation of the power grid. huge challenge. Therefore, it is necessary to accurately predict the power of wind power in order to reduce the uncertainty caused by the integration of wind power into the...

AI Technical Summary

Problems solved by technology

However, the existing research often ignores this characteristic of wind speed in numerical weather forecasting, and does not dig deep into this characteristic and apply it to short-term wind power prediction

[0006] In addition, short-term wind power forecasting requires the forecasted active power of the wind farm or wind power cluster for 3 days from 00:00 of the next day, while the forecasting time scale of the existing short-term wind power forecasting is mostly limited to 1 day before or 12 hours before the day

Since the weather change process in nature often lasts for several days, considering the volatility and continuity of wind resources, it is difficult to capture the continuous and dynamic weather change process if a short prediction time scale is selected, so that the training samples for wind power prediction Improper selection of wind power sets leads to low accuracy of short-term wind power forecasting, and it is not conducive to the power grid dispatching department to formulate power generation planning and dispatch operation of wind farms

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