Combined filling system for measured wind speed loss values of multiple neighboring wind motors in wind field

Abstract

The invention provides a combined filling system for measured wind speed loss values of multiple neighboring wind motors in a wind field. The combined filling system comprises a wind speed data similarity determination unit, a model parameter identification unit, a wavelet neural network submodel filling unit and a combined filling unit. The combined filling system for the measured wind speed loss values of multiple neighboring wind motors in the wind field is used for overcoming the technical defects of an existing method in filling of lost measured wind speed values when the measured wind speeds of multiple neighboring wind motors in the wind field have loss values simultaneously; the similarity of the wind speed data is analyzed by use of three methods, namely a dynamic time alignment method, a correlation coefficient method and a spatial neighbor method, in a two-dimensional time domain; the measurement wind speeds of a plurality of wind motors most similar to the wind motor having the lost measured wind speed in wind speed evolution near a loss sampling point are extracted, and a wavelet neural network is established for each measured wind speed to perform lost wind speed filling; the system is adaptive to the wind speed data of different wind field by use of adjustable parameters; a combined filling method based on entropy weight is adopted, and finally, a filling system for the measured wind speed loss values of multiple neighboring wind motors in the wind field is put forward.

Description

technical field [0001] The invention relates to a combined filling system for measuring wind speed defect values ​​adjacent to multiple wind generators in a wind power generation system. It is mainly aimed at the average wind speed data of all wind turbines collected from the supervisory control and data acquisition (SCADA) of the wind farm at fixed sampling intervals marked with defect values, and measures the time sequence of wind speed occurrence with defects Fill one by one. The system of the present invention is based on the wavelet neural network, adopts the dynamic time warping method, combines the correlation coefficient method and the spatial neighbor method to jointly search for the wind turbine set similar to the wind speed evolution of the defect measurement wind speed wind motor, and uses the measured wind speed of these wind motors to A system for wind speed filling for gap measurement wind turbines. Background technique [0002] In order to effectively integ...

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Problems solved by technology

[0005] The technical problem of the present invention is to overcome the existing single use of the spatial neighbor method or the correlation coefficient method, and the technical shortcomings of filling the missing value of the wind speed measured by the wind motor under the condition that the wind field is adjacent to multiple wind motors to measure the wind speed and the defect value occurs at the same time, from In the two-dimensional time domain, the dynamic time warping method is used, combined with the correlation coefficient method and the spatial neighbor method, to analyze the similarity of the wind speed data; For the measured wind speed of several typhoon motors, each constructs a wavelet neural network to fill in the missing wind speed; by changing the adjustable parameters of the filling system to adapt to the wind speed data of different wind fields; there is a lack of a universal missing wind speed filling model for all wind turbines in the wind field. Using the combined filling method based on entropy weight, a filling system and filling method for measuring the wind speed defect value of multiple wind turbines adjacent to the wind field are finally proposed

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