Wind turbine generator bearing temperature anomaly detection method and device and wind turbine generator
A technology of bearing temperature and wind turbines, which is applied in the direction of wind turbines, wind turbine monitoring, machines/engines, etc., can solve inaccurate problems, achieve the effect of improving accuracy and reliability, and long warning time in advance
Pending Publication Date: 2022-04-29
SANY ELECTRIC CO LTD
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[0005] The invention provides a method and device for detecting abnormal bearing temperature of a wind turbine, and a wind turbine to solve the problem of inaccurate detection of bearing temperature abnormality generally using a neural network in the prior art
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[0060] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.
[0061] It should be noted that, in the description of the embodiments of the present invention, the terms "comprising", "comprising" or any other variant thereof are intended to cover a non-exclusive inclusion, so that a process, method, article or device comprising a series of elements Not only those elements are included, but also other elements not expressly listed or inherent in such proc...
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The invention provides a wind turbine generator bearing temperature anomaly detection method and device and a wind turbine generator, and the method comprises the steps: obtaining a monitoring data set of all wind turbine generators in a wind field where a to-be-detected wind turbine generator is located under a condition close to full generation, and obtaining a bearing temperature and an environment temperature at each sampling moment from the monitoring data set; determining a bearing reference temperature from the bearing temperature trend threshold line according to the environment temperature, and calculating a temperature difference value between the bearing temperature and the bearing reference temperature; and determining the temperature state of the bearing according to the temperature difference values of the plurality of continuous sampling moments. The bearing temperature rise of the wind turbine generator can be accurately judged according to the temperature difference value between the bearing temperature at each current sampling moment and the bearing temperature trend threshold line, the bearing temperature rise is used as a reference standard for judging the bearing temperature state, the bearing temperature rise rule of the wind turbine generator can be deeply utilized, and the bearing temperature state can be accurately judged. The precision and credibility of the bearing temperature anomaly detection result are improved, and the early warning advance time is longer.
Description
technical field [0001] The invention relates to the technical field of electric new energy, in particular to a method and device for detecting abnormal temperature of a wind turbine bearing, and a wind turbine. Background technique [0002] Bearings are the core components of wind turbines (hereinafter referred to as wind turbines). Once the bearings are damaged, they need to be removed from the tower for replacement. The cost of hoisting and maintenance is high, and the spare parts cycle is long. Therefore, it is very necessary to establish an early warning model for bearing abnormality detection. [0003] In the prior art, a neural network is generally used to establish a temperature prediction model to compare the real-time detected bearing temperature with the output value of the temperature prediction model to determine whether the bearing temperature is abnormal. [0004] Since the training process of the temperature prediction model is complicated, and the output of t...
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IPC IPC(8): F03D17/00
CPCF03D17/00F05B2260/83
Inventor 杨凤梅吴平宇刘丽华
Owner SANY ELECTRIC CO LTD