Wind turbine generator yaw error early warning analysis method based on multi-dimensional analysis

Abstract

The invention discloses a wind turbine generator yaw error early warning analysis method based on multi-dimensional analysis, which comprises the following steps: selecting the wind speed, the activepower, the pitch angle, a cabin and wind direction included angle operation data by taking a single unit as a unit, and screening the data; importing the data into a yaw early warning algorithm, slicing from the dimension of the included angle between the cabin and the wind direction, and performing dividing to obtain M opposite wind angle intervals; slicing each wind alignment angle interval according to a wind speed dimension, and dividing the wind alignment angle interval into N wind speed intervals; calculating the weight values of different wind speeds in the interval through Rayleigh distribution, calculating the annual power generation capacity of each wind alignment angle interval, and judging that the yaw of the unit is abnormal if the maximum annual power generation capacity interval is greater than an alarm threshold value. The method further comprises a step of achieving alarm strategy analysis through the alarm degree and the unilateral effect. According to the method, weight distribution is performed on the wind speed in each pair of wind angle intervals from three dimensions of wind angle, wind speed and power, the annual power generation capacity of the intervals iscalculated, double judgment is performed through the unilateral effect and the alarm degree, the yaw abnormal unit is accurately found, and the safety of the unit is protected.

Description

technical field [0001] The invention relates to the early warning field of wind turbine control systems, in particular to a wind turbine yaw error early warning analysis method based on multidimensional analysis. Background technique [0002] In recent years, with the continuous development of science and technology, the demand for energy is also constantly increasing. In order to balance the relationship between technological development and environmental protection, wind power has become one of the new energy sources with the most development potential due to its advantages such as abundant reserves and mature mining technology. one. With the popularization of wind power intelligence, in order to improve the operation and maintenance efficiency of wind turbines and mine the massive information contained in the data, early warning analysis technology has received more and more attention. The yaw system is an important control system for wind turbines to capture wind energy...

AI Technical Summary

Problems solved by technology

Such long-term operation will cause problems such as spindle wear, gear box tooth surface wear, and unit centering displacement, which will seriously affect the life of the unit.

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