Yawing angle positioning control method based on time partition and yawing sector

Abstract

The invention discloses a yawing angle positioning control method based on a time partition and a yawing sector. The method comprises the following steps: 1, variable data are collected, a yawing judgment module judges the variable data, and a main control system controls yawing, wherein for variable data collection, a wind speed, a wind direction, a yawing angle and a yawing motor action instruction conventional control variable parameter of a unit are collected and monitored, variable data of a wind speed mean value, a wind direction beta, a yawing angle mean value alpha and a yawing frequency accumulated value N are obtained, and the variable data are taken as input variables of the yawing judgment module. Variables such as the wind speed, the wind direction, the yawing angle, a yawingmotor action instruction and system time of a unit are collected, the maximum value of the yawing frequency of the unit in a certain time period is obtained, and the unit is in the dominant wind direction in a specific yawing sector at the moment; yawing wind alignment is realized in advance by combining a single machine with a time partition, so that the wind catching capability of the unit is improved, and the generating capacity loss caused by yawing under strong wind is reduced.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a yaw angle positioning control method based on time partitions and yaw sectors. Background technique [0002] Wind speed and wind direction are random in nature. Generally, when wind turbines are designed, the main wind direction of the wind farm is basically the same. In the electronic control design, according to the change of wind direction and wind speed, the yaw control of the unit is yawed against the wind according to the preset yaw angle. However, the micro-site selection of the wind farm unit is not completely consistent with the wind conditions of the actual unit, and there are cases where the main wind direction of the unit in the same wind farm is inconsistent. For example Figure 6 As shown, the main wind direction of a certain wind farm is consistent in the morning, but the main wind direction is not obvious in the afternoon. This caused the unit to...

AI Technical Summary

Problems solved by technology

CN 108547736A and CN 108825432A use a new algorithm to predict the wind speed change trend based on the operating data of the unit, and then realize the yaw and wind control of the unit. However, due to the complexity of the algorithm and the trouble of code conversion, more tests need to be done. Long cycle and test cycle

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