Method for detecting blade cracking of wind generating set

Abstract

The invention relates to a method for detecting blade cracking of a wind generating set. When a blade cracks, the aerodynamic characteristics of the blade can change, the current of a variable pitch motor corresponding to the blade can increase correspondingly under the same external condition, and the change of the current of the variable pitch motor has a certain regular characteristic. According to the method, the current change of the variable pitch motor is analyzed and processed, and the current of the fan variable pitch motor , the blade azimuth angle, the hub rotating speed and the wind speed are analyzed, so that the influence of blade installation and the fault of the variable pitch motor on the current of the variable pitch motor is eliminated, the accuracy rate is high, whether the fan blade cracks or not can be pre-judged, and the state of the fan blade can be monitored in time, destructive damage to the blade is prevented, timely maintenance of the blade is facilitated, the maintenance cost of the fan is reduced, and the power generation loss is reduced.

Description

technical field [0001] The invention belongs to the technical field of detection of wind power generators, and in particular relates to a method for detecting cracking of blades of wind power generators. Background technique [0002] With the maturity of wind power generation technology, wind power generation has become an important part of my country's electric energy. At present, megawatt-level large-scale wind turbines are generally horizontal axis, 3-blade variable-speed constant-frequency wind turbines, and blades are one of them. Important components are the source of wind turbines converting wind energy into electrical energy, and their cost can reach 20% to 30% of the total cost of the wind turbine. During the operation of the wind turbine, if the blades are damaged and cannot be repaired on site, then It needs to be replaced or returned to the factory for maintenance. The replacement or maintenance cost of a single blade exceeds millions, and the loss is huge. Moreove...

AI Technical Summary

Problems solved by technology

[0002] With the maturity of wind power generation technology, wind power generation has become an important part of my country's electric energy. At present, megawatt-level large-scale wind turbines are generally horizontal axis, 3-blade variable-speed constant-frequency wind turbines, and blades are one of them. Important components are the source of wind turbines converting wind energy into electrical energy, and their cost can reach 20% to 30% of the total cost of the wind turbine. During the operation of the wind turbine, if the blades are damaged and cannot be repaired on site, then It needs to be replaced or returned to the factory for maintenance. The cost of replacing or repairing a single blade is more than one million, and the loss is huge; moreover, it takes a long time to replace the blades. During this process, the wind turbine cannot generate electricity, which seriously affects the power generation of the equipment.

[0003] At present, in order to prevent destructive damage to the blades and avoid replacement or return to the factory for maintenance, high-definition telescopes or drones are generally used for regular inspections. Once the blades are found to be cracked, they will be shut down immediately for repairs. However, both methods have certain limitations. Defects, the main problems are as follows: 1) The work intensity is high and the work efficiency is low; 2) For the detection method using the telescope, it needs to be shut down during the detection, the inspection time is long, and the loss of power generation is large; 3) The fan is generally Installed in places with relatively harsh natural environment, when the weather is bad, the inspection work cannot be carried out

Therefore, the traditional blade inspection method is limited by the external environment and the state of the wind turbine, the detection efficiency and accuracy of the detection results are low, and it will affect the wind power generation.

In recent years, with the development of big data technology, some related manufacturers have obtained the temperature change of the pitch motor through the monitoring system of the fan to analyze the blade cracking for early warning. Generally, due to the limitation of the accuracy of the temperature sensor and the delay of the temperature change, this method the accuracy of the

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