Stator Inter-Turn Fault Detection Using Current Signatures
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Solution Overview
Problem
Conventional systems for detecting stator inter-turn faults in induction machines face challenges in differentiating between similar fault signatures, such as stator inter-turn faults and voltage unbalance, leading to inefficient and non-real-time fault detection.
Innovation Solution
A method and apparatus that utilize time series data of stator currents to determine fundamental and third harmonic components, apply transformation matrices to extract signatures, and use an Unbalance Factor to scale and differentiate between fault signatures, enabling efficient detection of stator inter-turn faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems use search coils, flux coils, zero sequence impedance/voltage measurements, or spectral current analysis with neural networks to detect stator inter-turn faults, then fault detection capability is provided, but the circuit becomes complicated and fault differentiation becomes difficult
Solution Approach 1:
The invention extracts and analyzes specific signature components (positive sequence, negative sequence, zero sequence components at fundamental and third harmonic frequencies) from the stator current signal. By focusing on these extracted features rather than using complex search coils or flux coils, the system achieves reliable fault detection with simpler circuitry based on standard current sensors.
Solution Approach 2:
The invention introduces an intermediary processing layer that computes signature parameters (S1, S2, S3, S4) from current measurements and uses a discrimination factor (DF) as a mediator to differentiate between stator inter-turn faults and voltage unbalance conditions. This intermediary approach simplifies the detection system while maintaining reliability through mathematical analysis of current signatures.
2Reliability
If conventional systems use spectral current analysis or other methods to detect faults, then fault detection is provided, but the ability to differentiate between similar fault signatures (stator inter-turn fault and voltage unbalance) is compromised
Solution Approach 1:
The invention applies local quality analysis by examining specific local characteristics of the current signature at different frequency components. It calculates separate signature values for positive sequence (S1), negative sequence (S2), and zero sequence (S3) components at fundamental frequency, and similarly S4 at third harmonic frequency. This localized analysis of specific frequency components enables precise differentiation between fault types.
Solution Approach 2:
The invention changes parameters by introducing a discrimination factor (DF) that combines multiple signature parameters in a specific relationship: DF = (S2 + S4) / (S1 + S3). This parameter transformation converts complex multi-dimensional signature data into a single discriminative metric that clearly separates stator inter-turn fault conditions from voltage unbalance conditions, achieving high measurement precision in fault differentiation.
3Reliability
If conventional systems implement comprehensive fault detection, then multiple fault types can be detected, but real-time detection speed and efficiency are reduced
Solution Approach 1:
The invention segments the fault detection process into distinct computational steps: extracting positive sequence, negative sequence, and zero sequence components; calculating signature parameters S1, S2, S3, S4; and computing the discrimination factor DF. This segmentation allows the system to perform comprehensive fault detection through modular, computationally efficient operations that can be executed in real-time.
Solution Approach 2:
The invention replaces complex mechanical or hardware-based detection systems with mathematical and computational methods. By using digital signal processing to extract sequence components and calculate signature parameters from standard current measurements, the system achieves comprehensive fault detection at high speed without the complexity of specialized sensors or mechanical systems.
Data Source
AI summary
A system and method for detecting stator inter-turn fault in induction machines includes receiving a time series data of a stator current using a plurality of current sensors connected to a stator of the induction machine, determining first and second signature time series data of stator current using fundamental components and harmonic components of the stator current, determining first and second scaled signatures using an Unbalance Factor, determining a discrimination factor, and detecting a stator inter-turn fault when the discrimination factor is greater than a predetermined stator inter-turn fault threshold value.