Wind Turbine Blade Crack Detection Using Internal Sensor Arrays
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Solution Overview
Problem
There is a lack of effective technical solutions for monitoring blade cracking in wind turbines, which can lead to safety hazards and economic losses due to undetected damage, particularly in high-corrosive environments like offshore wind farms.
Innovation Solution
A blade state monitoring method and device that utilizes a sensor array with acoustic, temperature, and air pressure sensors inside the wind turbine blades to detect abnormal sound data patterns, determining cracking positions, speeds, and lengths by analyzing regular changes in sensor data, thereby enabling timely detection and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic sensors are installed inside the blade to detect cracking sounds, then the detection capability for blade cracks is improved, but the device complexity and manufacturing difficulty increase
Solution Approach 1:
The blade is divided into multiple monitoring zones with sensor arrays distributed at different positions (leading edge, trailing edge, root, tip). Each sensor array independently monitors its local zone, enabling segmented detection of cracks at different locations while keeping each sensor unit relatively simple.
Solution Approach 2:
Multiple types of sensors (acoustic sensors, temperature sensors, air pressure sensors) are integrated into a unified sensor array system inside the blade. The sensor arrays are nested within the blade structure, with acoustic sensors detecting crack sounds, temperature sensors monitoring thermal changes, and pressure sensors detecting pressure variations, creating a multi-functional monitoring system.
2Adaptability or versatility
If multiple types of sensors (acoustic, temperature, pressure) are integrated into the blade, then the comprehensive monitoring capability is improved, but the manufacturing difficulty and installation complexity increase
Solution Approach 1:
Acoustic sensors, temperature sensors, and air pressure sensors are merged into a single integrated sensor array system. This combination allows the system to simultaneously monitor acoustic emissions from cracks, temperature variations, and pressure changes, providing comprehensive multi-dimensional monitoring of blade health status.
Solution Approach 2:
The sensor array is designed as a universal monitoring system that can detect multiple types of blade anomalies simultaneously. The same sensor array structure accommodates different sensor types, each serving different detection purposes, making the system versatile for monitoring various failure modes including cracks, delamination, and structural degradation.
3Measurement precision
If sensor arrays are distributed throughout the blade structure, then the coverage and detection accuracy are improved, but the installation complexity and maintenance difficulty increase
Solution Approach 1:
The blade is divided into multiple monitoring zones with sensor arrays distributed at strategically important positions including the leading edge, trailing edge, root, and tip. Each zone is monitored independently, allowing localized detection and diagnosis of cracks without requiring access to the entire blade structure for maintenance activities.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution allows for early detection of blade cracking, preventing safety hazards and reducing maintenance costs by providing multi-dimensional monitoring results that ensure the safety and efficiency of wind power generation.
Implementation Method 1
acoustic sensors... sound data... determining whether there is abnormal data in the sensor data, wherein the abnormal data is sensor data whose index value changes regularly
Data Source
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AI summary
The present disclosure provides a blade state monitoring method and device, a storage medium, and a wind power generator. The blade state monitoring method includes: acquiring sensor data from a sensor array, the sensor array including a plurality of sensors, the plurality of sensors including acoustic sensors, wherein the sensor array is arranged inside a blade of a wind turbine, and the sensor data includes sound data; determining whether there is abnormal data in the sensor data, wherein the abnormal data refers to the sensor data whose index value changes regularly; and determining that a state of the blade is cracked. The technical scheme of the present disclosure can satisfy the monitoring of the cracking of the blades of the wind power generator.