Wind electricity power short-term combined prediction method considering run detection method reconstruction

Abstract

The invention relates to a wind electricity power short-term combined prediction method considering run detection method reconstruction. The method includes the following steps that: a plurality of groups of output time sequence sample data of a wind farm are decomposed through adopting empirical mode decomposition, so that a plurality of intrinsic mode functions (IMF) and trend items Res can be obtained; fluctuation degree classification is performed on the intrinsic mode functions obtained through decomposition and remaining components according to a run discriminating method, and EMD decomposition items with similar fluctuation frequencies are reconstructed; and data normalization processing is performed on the reconstructed components, and processed components are adopted as training and testing data of a neural network, and an EMD-Elman prediction model direct multi-step method is established to perform 72h day-ahead power prediction. According to the method of the invention, the accurate EMD-Elman neural network short-time multi-step combined prediction model is established, and therefore, the number of models built for predictive components can be decreased, and prediction accuracy and prediction speed can be improved.

Description

technical field [0001] The invention belongs to a method in the field of wind power prediction field of electric power system, and specifically relates to an EMD-Elman wind power short-term combination prediction method considering run length detection method reconstruction. Background technique [0002] With the depletion of non-renewable resources such as coal and oil and the increasing pollution emissions, the world is actively seeking environmentally friendly and clean renewable energy as an "alternative energy source" for traditional fossil fuels, and wind energy is a future The pollution-free, green, clean and renewable energy that is most promising to solve the greenhouse effect has important strategic significance in the sustainable development of global energy, so it has attracted great attention from all countries, and has achieved rapid development in the past decade, especially in China, which is rich in wind energy resources. [0003] China's wind power industry...

AI Technical Summary

Problems solved by technology

[0003] China's wind power industry is rapidly developing in the direction of scale, clustering, and bases. Wind power access has changed from decentralized and small-scale access in the early stages of development to centralized, large-scale access, and wind power is consumed remotely. It needs to “cross” the power grid, and the wind power output has the characteristics of strong random fluctuation, poor power regulation ability, and low annual utilization hours, etc., which are greatly affected by the meteorological environment and wind farm layout. The access of modernization has an important impact on the safety, reliability and stability of the power system, and brings great challenges to the safe operation and dispatch planning of the power system.

[0004] In addition, China's wind energy resources are unevenly distributed, with significant regional differences. They are mainly distributed in the "Three Norths" and the southeast coastal areas, and most of them are far away from the load center. The development is ahead of the corresponding regional power grid planning, and the planning and development of the two are not coordinated. The transmission capacity of the wind power base transmission grid is insufficient, the local consumption is limited, the power structure is single and flexible, and the power supply is insufficient. Wind phenomenon, the main reason for the difficulty of wind power grid connection and wind abandonment phenomenon is the random fluctuation of wind power output

Images