DFIG Winding Fault Detection via Self-Diagnostic Converter Signals
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Solution Overview
Problem
Wind turbines with double-fed induction generators (DFIGs) face inefficiencies due to winding faults caused by high current loads, which are difficult to detect efficiently with traditional manual methods, leading to prolonged periods of inefficient operation and potential failure to identify faults.
Innovation Solution
A system with a controller that operates the DFIG in both power generation and diagnostic modes, using a converter to send input signals to the rotor and stator, and analyzing output signals to identify winding faults without additional hardware, allowing for remote testing and reduced maintenance costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection using a micro-ohm meter is performed, then winding faults can be detected, but the inspection process becomes expensive and time-consuming
Solution Approach 1:
The DFIG performs self-diagnosis by using its own converter to generate test signals and measure its own windings. The controller automatically executes the diagnostic routine, eliminating the need for external operators and specialized equipment, thereby reducing both time and cost while maintaining detection accuracy.
Solution Approach 2:
The converter is designed to serve dual purposes: normal power conversion during operation and signal generation/measurement during diagnostic mode. This multi-functionality eliminates the need for separate diagnostic equipment, reducing inspection costs and time while maintaining fault detection capability.
2Measurement precision
If manual inspection using a micro-ohm meter is performed, then winding faults can be detected, but the inspection cost increases
Solution Approach 1:
The system uses its own built-in converter and controller to perform self-diagnosis, eliminating the need for external operators and specialized micro-ohm meters. This self-service approach reduces inspection costs while maintaining detection accuracy through automated signal generation and measurement.
Solution Approach 2:
The converter performs both power conversion and diagnostic functions using the same hardware components. By reusing existing converter infrastructure for both operational and diagnostic purposes, the system eliminates the need for additional expensive diagnostic equipment and reduces overall inspection costs.
3Productivity
If inspection intervals are extended to reduce maintenance frequency, then operational efficiency decreases due to undetected faults
Solution Approach 1:
The automated self-diagnostic capability allows the system to perform rapid inspections without requiring operator intervention or extended shutdown periods. This enables frequent monitoring intervals that maintain high operational efficiency while quickly detecting faults before they impact productivity.
Solution Approach 2:
The system performs diagnostic measurements during normal operation or brief maintenance windows, detecting winding faults before they lead to significant efficiency losses. This preliminary detection approach allows for timely interventions that maintain continuous high productivity.
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
Enables frequent and cost-effective detection of winding faults, reducing the time between maintenance intervals and improving the efficiency and reliability of wind turbine operations by identifying issues earlier.
Implementation Method 1
a rotating magnetic field is established between these windings, thereby inducing an output electric current
Data Source
AI summary
A system includes an induction generator controller configured to operate an induction generator via a converter. The induction generator controller includes a diagnostic mode configured to instruct the converter to send an input signal to a rotor of the induction generator, receive an output signal from the rotor and a stator of the induction generator, and identify winding faults within the rotor and/or the stator based on the output signals.