Wind field equivalence modeling and optimal control method based on fuzzy c-means clustering algorithm

Abstract

The invention discloses a wind field equivalence modeling and optimal control method based on a fuzzy c-means clustering algorithm. The method comprises steps: the number c of classes in the clustering algorithm is determined; the mean output power, the output power standard deviation, the inertia time constant, the longitude, the latitude and the height of a wind turbine are selected as clustering elements in the clustering algorithm for analysis; the above parameters are pre-processed to obtain a characteristic matrix of the wind turbine; a clustering distance and an objective function are determined; the wind field is subjected to the fuzzy c-means clustering algorithm to obtain a wind field equivalence model; a virtual wind turbine in the wind field equivalence model is subjected to parameter aggregation; the output power of the virtual wind turbine is optimized; and at each preset time interval, the above steps are repeated, the parameters are updated, and according to the updatedparameters, the output power of the virtual wind turbine is optimized. The actual operation state of the wind field can be reflected accurately while the wind field model is simplified, and the calculation process is relatively simple and easy to realize.

Description

Technical field [0001] The invention relates to the technical field of wind power generation, in particular to a wind field equivalent modeling and optimization control method based on a fuzzy c-means clustering algorithm. Background technique [0002] With the continuous development of the energy market, more and more new energy sources are being valued. As a clean and efficient new energy source, wind energy is also constantly developing related technologies. A feature brought about by the development of wind power technology is the ever-increasing scale of grid-connected wind farms. In the process of modeling wind farms, in general, due to the large scale of wind farms, modeling each unit will not only increase the scale of the model, but also increase the complexity of calculation, analysis and simulation. Very cumbersome. Therefore, in order to reduce the amount of calculation and simulation time, it is necessary to use the equivalent modeling method to describe the wind f...

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Problems solved by technology

[0006] In order to solve the above-mentioned problems existing in the prior art, the present invention proposes a wind field equivalent modeling and optimization control method based on the fuzzy c-means clustering algorithm, to solve the existing wind farm equivalent clustering algorithm. The equivalence method cannot accurately reflect the actual operating status of the wind farm in the study of large-scale wind farms, and the grouping index considered by the multi-machine equivalence method is relatively single or the calculation is too complex and difficult to realize.

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