Wind Turbine Yaw Fault Early Warning Using Real-Time Monitoring
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Solution Overview
Problem
Existing fault early warning systems for yaw systems in wind turbine generator sets lack accuracy, leading to inefficient wind energy capture and prolonged downtime due to incorrect wind alignment.
Innovation Solution
A method and device for fault early warning in yaw systems that monitor real-time data, including effective output power and incoming wind speed, and utilize a fault identification model to determine fault types, such as actuator and wind vane faults, through similarity analysis and system parameter comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fault monitoring methods are used for yaw systems, then the system structure remains simple, but fault detection accuracy is poor
Solution Approach 1:
The fault detection system is segmented into multiple independent monitoring modules, each responsible for specific parameters (wind speed, power output, yaw angle, actuator position). This segmentation allows complex fault detection to be broken down into manageable parts, improving accuracy while maintaining modular simplicity
Solution Approach 2:
The system performs preliminary monitoring of multiple parameters before actual fault occurrence. By continuously collecting and analyzing data in advance, the system can detect early signs of failure and trigger warnings before the yaw system actually fails, improving detection accuracy without requiring complex real-time intervention mechanisms
2Reliability
If fault early warning is performed timely and accurately, then preventive maintenance can be conducted, but the system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where monitoring data is continuously fed back into the fault identification model. This feedback loop allows the system to learn from actual system behavior and refine its fault detection algorithms, improving reliability through adaptive learning rather than complex predetermined rules
Solution Approach 2:
The fault identification model performs self-diagnosis and self-adjustment by analyzing its own performance against monitored system parameters. The system automatically identifies patterns and adjusts its detection thresholds, reducing the need for external intervention and complex manual configuration while maintaining high reliability
3Productivity
If the yaw system operates without accurate fault detection, then the system remains simple, but wind energy capture efficiency decreases
Solution Approach 1:
The monitoring system is designed with multi-functionality, serving both as a simple operational monitor and a complex fault detection system. The same hardware infrastructure supports multiple functions: real-time operation monitoring, fault pattern recognition, predictive analysis, and system performance optimization, thereby improving wind energy capture without proportionally increasing complexity
Solution Approach 2:
The system dynamically adjusts monitoring parameters based on operating conditions. By changing which parameters are monitored and their sampling frequencies according to current wind conditions and system state, the system optimizes wind energy capture efficiency while avoiding unnecessary monitoring complexity during normal operation
Data Source
AI summary
The invention provides a method and a device for fault diagnosis of a yaw system in a wind turbine, and relates to the technical field of wind turbines. The method comprises the following steps: acquiring monitoring data collected in a yaw system, inputting the monitoring data into a yaw fault diagnosis model, and outputting a fault diagnosis result, wherein the fault diagnosis model is obtained by training known fault diagnosis results and corresponding monitoring data, and the fault diagnosis result comprises at least one of the following: the position of a yaw sensor is shifted, the yaw sensor is damaged, the yaw contactor is stuck, the hardware of a yaw motor/reducer is damaged, and the yaw motor is braked, In the working process, the monitoring data collected by the yaw system can be input into the yaw fault diagnosis model in real time, and the yaw fault diagnosis model can be used to determine whether the yaw system has a fault and the specific fault diagnosis results when the fault occurs. In this way, the operation and maintenance personnel can be prevented from going to the aircraft seat for inspection, and the fault diagnosis efficiency of the yaw system can be improved.