Inter-turn Short Circuit Research Articles

Novel Generic Fault Model Considering Fundamental and PWM Current Components of PM Machines with Inter-Turn Short-Circuit

Novel Generic Fault Model Considering Fundamental and PWM Current Components of PM Machines with Inter-Turn Short-Circuit

A Novel Diagnosis Method for Interturn Short-Circuits in SRMs by Tracking Post Turn-Off Phase Currents Under Current Chopping Control

Current chopping control (CCC) is widely preferred for low and medium-speed applications in switched reluctance motor drives. With precise current regulation, the consequence of inter-turn short-circuits is not reflected much in the faulty phase current. Also, the fault features are difficult to extract in the online mode for minor short-circuits. Moreover, ITSCs are responsible for hot spots in the defective winding, ultimately leading to a complete winding short-circuit due to persisting heating effect. Even the associated complications of SRMs like torque ripple, noise and vibrations are amplified to a greater extent under ITSCs. Therefore, diagnosing ITSCs is critical to shun such unwanted characteristics and failures. This article proposes a simple and novel method to diagnose ITSCs even with minor short-circuits in SRMs operating under CCC. With the reduction in the inductance due to ITSCs, the decay time of the faulty phase current post turn-off also reduces. This variation is monitored for all the phase currents within their respective aligned rotor positions to diagnose ITSC. The proposed method has better detection reliability regarding diagnosis under varying load conditions. The scheme is validated experimentally on a test setup of a customized four-phase SRM having winding taps for emulating ITSCs.

Harmonic Subspace Signature-Based Detection and Localization of Inter-Turn Short Circuit Fault for Dual Three-Phase PMSM With VSD Scheme

Harmonic Subspace Signature-Based Detection and Localization of Inter-Turn Short Circuit Fault for Dual Three-Phase PMSM With VSD Scheme

Digital Twin-driven Hot Spot Capture for Power Transformer Inter-Turn Short Circuit Fault Diagnosis based on Microthermal Field

Digital Twin-driven Hot Spot Capture for Power Transformer Inter-Turn Short Circuit Fault Diagnosis based on Microthermal Field

Diagnosis of Inter-Turn Short Circuit Fault in BLDCM Based on Coupled High-Frequency Signal Injection

Diagnosis of Inter-Turn Short Circuit Fault in BLDCM Based on Coupled High-Frequency Signal Injection

Condition Monitoring and Fault Diagnosis of Permanent Magnet Synchronous Motor Stator Winding Using the Continuous Wavelet Transform and Machine Learning

Abstract Applying the condition monitoring technology to industrial processes can help detect faults in time, minimise their impact and reduce the cost of unplanned downtime. Since the introduction of the Industry 4.0 paradigm, many companies have been investing in the development of such technology for drive systems. Permanent magnet synchronous motors (PMSMs) have recently been used in many industries. Therefore, the issues of condition monitoring of PMSM drives are important. This study proposes and compares diagnostic schemes based on the stator phase currents (SPCSCs) signal for condition monitoring and fault diagnosis of PMSM stator winding faults. The continuous wavelet transform (CWT) is used for the extraction of the symptoms of interturn short circuits in PMSM stator winding. Machine learning algorithms are applied to automate the detection and classification of the faults. The concept for an original and intelligent PMSM stator winding condition monitoring system is proposed.

Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks

Abstract Modern permanent magnet synchronous motor (PMSM) diagnostic systems are now combined with advanced artificial intelligence techniques, such as deep neural networks. However, the design of such systems is mainly focussed on a selected type of damage or motor type with a limited range of rated parameters. The application of the idea of transfer learning (TL) allows the fully automatic extraction of universal fault symptoms, which can be used for various diagnostic tasks. In the research, the possibility of using the TL idea in the implementation of PMSM stator windings fault-detection systems was considered. The method is based on the characteristic symptoms of stator defects determined for another type of motor or mathematical model in the target diagnostic application of PMSM. This paper presents a comparison of PMSM motor inter-turn short circuit fault detection systems using TL of a deep convolutional network. Due to the use of direct phase current signal analysis by the convolutional neural network (CNN), it was possible to ensure high accuracy of fault detection with simultaneously short reaction time to occurring fault. The technique used was based on the use of a weight coefficient matrix of a pre-trained structure, the adaptation of which was carried out for different sources of diagnostic information.

Inter-Turn Short Circuit Fault Diagnosis and Localization of PMSM Based on Current Trajectory

Inter-Turn Short Circuit Fault Diagnosis and Localization of PMSM Based on Current Trajectory

A new on-line diagnosis method for inter-turn short circuit of hydro-generator rotor windings using a flexible equivalent coil

A new on-line diagnosis method for inter-turn short circuit of hydro-generator rotor windings using a flexible equivalent coil

Early Fault Detection of Stator Inter-Turn Short Circuit of Asynchronous Motor Based on Rotating High Frequency Voltage Injection

Early Fault Detection of Stator Inter-Turn Short Circuit of Asynchronous Motor Based on Rotating High Frequency Voltage Injection

Diagnosis of Oil Drilling Permanent Magnet Synchronous Motor Inter-turn Short Circuit Under Physical Fault Model Training Sample

Diagnosis of Oil Drilling Permanent Magnet Synchronous Motor Inter-turn Short Circuit Under Physical Fault Model Training Sample

Fault Detection of Interturn Short Circuit in Induction Motors Under Nonstationary Conditions and Unbalanced Supply Voltage

Fault Detection of Interturn Short Circuit in Induction Motors Under Nonstationary Conditions and Unbalanced Supply Voltage

Detection of Inter-turn Short Circuit in Stator Windings of Electric Machines using Magnetic Symmetry Index and Machine Learning Methods

Detection of Inter-turn Short Circuit in Stator Windings of Electric Machines using Magnetic Symmetry Index and Machine Learning Methods

Fault Phase Location for Interturn Short Circuit Faults in Symmetrical Six-Phase PMSMs Based on Subspace Current Residual

Fault Phase Location for Interturn Short Circuit Faults in Symmetrical Six-Phase PMSMs Based on Subspace Current Residual

Instantaneous Phase Estimation Based Single-Signal Diagnosis for Inter-Turn Short Circuit Fault in PMSMs

Instantaneous Phase Estimation Based Single-Signal Diagnosis for Inter-Turn Short Circuit Fault in PMSMs

Assessment of Interturn Short-Circuit Faults in PMSMs With Parallel-Connected Winding

Assessment of Interturn Short-Circuit Faults in PMSMs With Parallel-Connected Winding

Detection and location of inter-turn short-circuit in a PMSM based on square of negative sequence current vector modulus

Detection and location of inter-turn short-circuit in a PMSM based on square of negative sequence current vector modulus

Model-based Luenberger State Observer for Detecting Interturn Short-Circuits in PM Machines

Model-based Luenberger State Observer for Detecting Interturn Short-Circuits in PM Machines

Simplified Pre-Analysis of Fault Severity for Interturn Short-Circuit Faults in Fault-Tolerant Permanent Magnet Machines

Simplified Pre-Analysis of Fault Severity for Interturn Short-Circuit Faults in Fault-Tolerant Permanent Magnet Machines

Stator Winding InterTurn Short-circuit Fault Detection in WRIM Using Rise and Fall Times of Stator Currents

Stator Winding InterTurn Short-circuit Fault Detection in WRIM Using Rise and Fall Times of Stator Currents