Modeling and Diagnostic of Incipient Interturn Faults for a Three-Phase Permanent Magnet Synchronous Motor

Obeid, Najla Haje; Boileau, Thierry; Nahid‐Mobarakeh, Babak

doi:10.1109/tia.2016.2581760

Cited by 45 publications

(13 citation statements)

References 29 publications

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“…Hence, both sides of the Equation 3are simultaneously multiplied by the phase current in, the power balance is found by: 2 2 n n n n n n n n n n n dL (i ,θ) d i U i = R i + i ω + i L (i ,θ) dθ dt (5) When the winding is energized, if the losses of the phase winding can be ignored, a part of the input power is used to make the energy storage in the winding increased, and the other part is converted to output mechanical power. Hence, the electromagnetic torque per phase is found by:…”

Section: Model Of the Healthy Srmmentioning

confidence: 99%

Applications of a Strong Track Filter and LDA for On-Line Identification of a Switched Reluctance Machine Stator Inter-Turn Shorted-Circuit Fault

et al. 2019

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Reliability is pivotal significance for switched reluctance machine drives (SRD) applied to safety essential transportation and industrial fields. An inter-turn shorted-circuit fault (ISCF) could incite the machine to operate in unbalanced status, resulting in the noise increases. In the event such a fault remains untreated, the fault will further destroy the rest of the normal phases, even leading to a tragic incident for the entire drive application. To improve the reliability of SRD, an efficient on-line fault diagnosis method for ISCF should be proposed. This paper is focused on employing the strong track filter (STF) to achieve real-time phase resistance differences between before and after ISCF, which are used as features to diagnose the fault occurrence and the fault phase. Furthermore, a classification namely as linear discriminant analysis (LDA) is selected to estimate fault severity. Finally, simulation and experiments correspond to various running statuses are executed and their results can verify that the diagnosis method has accuracy and robustness.

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Section: Model Of the Healthy Srmmentioning

confidence: 99%

Applications of a Strong Track Filter and LDA for On-Line Identification of a Switched Reluctance Machine Stator Inter-Turn Shorted-Circuit Fault

et al. 2019

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“…Any kind of fault in induction motor causes drastic consequences in the devices connected with the motor and entire system. If pump is connected with faulty induction motor the head value will change, flow rate will change and huge vibration will create severe damage [2]. Breakdown of entire system not only causes system damage but also creates huge energy loss and, unplanned sudden downtime causes and huge maintenance cost.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning for Inter Turn Fault Detection in Pumping System

Dutta

Palanisamy

Shanmugam

2022

Preprint

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Pump fault diagnosis is essential for maintenance and safety of the device as it is an important appliance used in various major sectors. Fault diagnosis at proper time can reduce maintenance cost and save energy. In this article a Simulink model based on mathematical equations has been built for analyzing the effects of parameter estimation of three phase induction motor based centrifugal pump in inter turn fault condition. The inter turn fault causes huge increase of current which severely affects the parameters of both motor and pump and these have been analysed by simulation through Matlab Simulink model. Later, the results are verified by hardware in loop (HIL) based simulator. In this paper machine learning (ML) based artificial neural network (ANN) and ANFIS (ANN and Fuzzy) models have been applied for fault detection ANN and ANFIS based models provide a satisfactory level of accuracy. These models provide accurate training and testing results. Based on root mean square error (RMSE), R2, prediction accuracy and mean validation value these models are compared to find out which is more suitable for this experiment. Various supervised algorithms are compared with ANN, ANFIS and lastly found which is the most suitable for this experiment.

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“…However, they suffer from inaccuracy and time delay resulting from analysing the set of the measured data. The Wavelet Transform [13], [17], and Hilbert-Hang Transform [18] have been used to analyse the voltage and current signals on detecting the stator inter-turn fault. Knowledge-based techniques such as Neural Network [19], Fuzzy logic [20], and Particle Swarm Optimization [21] can be also used to detect the stator inter-turn fault and give acceptable results.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis Between Unscented and Extended Kalman Filters for PMSG Inter-Turn Fault Detection

Sayed

Aboelhassan

Madi

et al. 2021

2021 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

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This paper investigates the detection of inter-turn phase faults in Permanent Magnet Synchronous Generators (PMSGs) using the Unscented Kalman Filter (UKF) compared to the Extended Kalman Filter (EKF). PMSGs are subject to several faults such as bearing, eccentricity, demagnetization, short circuit, and inter-turn faults. Accurate and early detection of the fault type is crucial for robust operation. Several techniques can detect these faults. UKF and EKF are presented here as one of the model-based fault diagnosis techniques. In the presented simulation, a comparison between the UKF and EKF estimation response of the fault has been shown. Both techniques have provided the ability to detect the inter-turn fault with the proposed PMSG fault model. However, the difference between the estimation response accuracy and speed plays an important role to decide the most effective technique.

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Modeling and Diagnostic of Incipient Interturn Faults for a Three-Phase Permanent Magnet Synchronous Motor

Cited by 45 publications

References 29 publications

Applications of a Strong Track Filter and LDA for On-Line Identification of a Switched Reluctance Machine Stator Inter-Turn Shorted-Circuit Fault

Applications of a Strong Track Filter and LDA for On-Line Identification of a Switched Reluctance Machine Stator Inter-Turn Shorted-Circuit Fault

Application of Machine Learning for Inter Turn Fault Detection in Pumping System

Comparative Analysis Between Unscented and Extended Kalman Filters for PMSG Inter-Turn Fault Detection

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