Infrared automatic identification method for internal defect types of main beams of large wind turbine blades

Abstract

The invention relates to the technical field of operation and maintenance of wind power generation equipment, and is also applicable to the non-destructive detection of early defect types of other resin-based composite materials. It is accurate, applicable and can be applied to the field working environment. It includes the following steps: S1 Obtain data samples of ambient temperature, thickness of the main beam sample, distance from the heat source to the sample, wind speed on the surface of the sample, and thermal diffusivity of each defect part of the sample with defects, and establish the relationship between these parameters and the thermal diffusivity of different types of defects The nonlinear coupling relationship between them, while calculating the parameters of the neural network model; S2 continuously irradiates the surface of the wind turbine blade; S3 collects and extracts the surface temperature difference curve between the defective area and the non-defective area of ​​the blade; S4‑S5 records the main beam of the blade at the current irradiation position Thickness; measure the current ambient temperature; measure the average wind speed on the blade surface; S6 calculates the thermal diffusivity according to the BP neural network calculation method; S7 uses the relative error rate calculation formula of the thermal diffusivity to complete the defect type identification.

Description

technical field [0001] The invention relates to the technical field of operation and maintenance of wind power generation equipment, and is also applicable to non-destructive detection of early defect types of other resin-based composite materials. Background technique [0002] In my country, as an important renewable energy technology, wind power is currently in an important period of rapid development. Wind turbine blades, as the core components of wind turbines, have a decisive impact on the stable and safe operation of the wind turbine. The main beam is the most important load-bearing part of the blade, and its state directly determines the performance of the blade. Therefore, the state detection of the blade is mainly aimed at the state detection of the main beam of the blade. [0003] Affected by random factors such as manufacturing process, transportation and improper use, wind turbine blades inevitably have defects such as air bubbles, inclusions, and wrinkles. Und...

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Problems solved by technology

[0007] 1. Aiming at the problem that the current infrared measurement method for internal defects of the blade ignores the influence of environmental heat exchange and the influence of the blade shape structure, which makes the measurement method not suitable for on-site detection, an algorithm is proposed to establish the thickness of the blade, the irradiation distance, the ambient temperature and the blade shape. The coupling relationship between surface wind speed and thermal diffusivity improves the accuracy of on-site identification and makes it suitable for use in on-site environments

[0008] 2. In view of the fact that some current infrared measurement methods for internal defects of blades cannot realize the automatic analysis of defect types, it is necessary to propose a measurement method based on comparing the measured thermal diffusivity with the standard thermal diffusivity to realize automatic identification of defect types

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