A method for historical wind power output data processing and fluctuation identification

Abstract

The present invention provides a historical wind power output data processing and fluctuation identification method, comprising the following steps: collecting historical wind power output data, and analyzing and sorting it; performing normalization processing and wavelet filtering processing on the analyzed and sorting historical wind power output data , and divide the fluctuations; use the visualization method to test the clustering effect of historical wind power output data and wind fluctuations. The invention provides a historical wind power output data processing and fluctuation identification method, which can be used as the early basis for wind power mid- and long-term output time series modeling, and can also be applied to power system planning and operation analysis including wind power.

Description

technical field [0001] The invention belongs to the technical field of new energy power generation, and in particular relates to a historical wind power output data processing and fluctuation identification method. Background technique [0002] In recent years, wind power technology has continued to develop rapidly, and wind power grid-connected installed capacity has grown rapidly. By the end of 2013, my country's new installed capacity was 16.1GW, and the cumulative installed capacity has reached 91.4GW. However, wind power has strong randomness and is a fluctuating power source. Large-scale access will inevitably bring great challenges to the safe and stable operation of the power system. Therefore, it is necessary to study the uncertainty of wind power output, grasp the change law of wind power output, and on this basis, carry out long-term scale wind power output time series modeling, simulate the change law of wind power, and effectively reduce the impact of wind power...

AI Technical Summary

Problems solved by technology

When the SCADA system is running, all links of data acquisition, measurement, transmission, and conversion may fail or be disturbed, resulting in abnormal data. If it is not processed, it will definitely affect the accuracy and reliability of time series modeling

In addition, the resolution of the time series of wind power output for a long time is low, which is not conducive to the extraction of wind power laws. Therefore, it is necessary to decompose the time series of wind power output and conduct research and analysis at a higher resolution.

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