Inter-turn fault detection in five-phase pmsms. Effects of the fault severity

Saavedra, Harold; Riba, Jordi-Roger; Romeral, L.

doi:10.1109/demped.2013.6645764

Cited by 22 publications

(9 citation statements)

References 11 publications

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“…At the same time, sample the drive voltage and the phase currents from the actual five-phase fault PMSM. And next, a reference model of the five-phase PMSM with ITSC fault is built as (5), and the drive voltage of the reference model is the same as the actual PMSM. After then, in order to detect and diagnose the fault severity of the five-phase PMSM with ITSC fault, or to identify the fault parameter (the ITSC ratio μ) by PSO algorithm, a quadratic fitness function is utilized as…”

Section: B Fault Parameter Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and fault diagnosis of inter-turn short circuit for five-phase PMSM based on Particle Swarm Optimization

Yang

Dou

Dai

et al. 2016

2016 IEEE Applied Power Electronics Conference and Exposition (APEC)

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Multiphase PMSMs are widely used in faulttolerant control applications. And one of the important faulttolerant control problems is fault diagnosis. In most existing references, the fault diagnosis always focuses on three-phase PMSM. In this paper, a novel model and a fault diagnosis method of inter-turn short circuit (ITSC) fault for five-phase PMSM based on particle swarm optimization (PSO) are presented. There are two contributions in this paper: 1) By analyzing the physical parameters of the PMSM, such as resistance, inductance and flux, the mathematic model of ITSC fault for five-phase PMSM is established. 2) A parameter estimation method based on PSO algorithm, which introduces a fitness function related to the ITSC ratio and reformulates the fault parameters identification problem as the extreme seeking problem, is proposed to track the actual ITSC ratio and detect the fault level for five-phase PMSM. Simulation results provide preliminary verification of the proposed model and fault diagnosis method.

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Section: B Fault Parameter Identificationmentioning

confidence: 99%

“…Compared with the traditional three-phase PMSM, since the phase number is increased, the fault tolerance of the multiphase PMSM is improved, thus the reliability of the multiphase PMSM is higher. Based on these advantages, the multiphase PMSMs are applied in fault-tolerant control systems widely [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Modeling and fault diagnosis of inter-turn short circuit for five-phase PMSM based on Particle Swarm Optimization

Yang

Dou

Dai

et al. 2016

2016 IEEE Applied Power Electronics Conference and Exposition (APEC)

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show abstract

“…Significant studies on multiphase PMSM are being conducted based on the detection of different type of faults [75] as well as fault-tolerant control strategies for optimizing the machine performance after a fault arises [74].…”

Section: Research On Multiphase Permanent Magnet Synchronous Machinementioning

confidence: 99%

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Riera-Guasp

Antonino-Daviu

Capolino

2015

IEEE Trans. Ind. Electron.

467

234

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“…Network structure can be optimized. For a five-phase FTPM motor, the stator inter-turn short circuit fault is one of the most common faults [25]. Less turns short circuit fault leads to more influence on the motor running process.…”

Section: A Fault Diagnosis Systemmentioning

confidence: 99%

Fault diagnosis of five-phase fault-tolerant permanent-magnet motor based on principal component neural network

Zhou

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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In this paper, a new fault diagnosis method for a five-phase fault-tolerant permanent-magnet (FTPM) motor by using a compact method is proposed. The key is to create a neural network based on principle component analysis (PCA). For a current signal of a five-phase FTPM motor system, PCA theory is used to extract the main element from the fault sample data. It realizes optimum compressed of fault sample data and simplifies structure of neural network in fault diagnosis. Speed and precision of the fault classification are enhanced. The obtained results verify the effectiveness of the proposed method.

show abstract

Inter-turn fault detection in five-phase pmsms. Effects of the fault severity

Cited by 22 publications

References 11 publications

Modeling and fault diagnosis of inter-turn short circuit for five-phase PMSM based on Particle Swarm Optimization

Modeling and fault diagnosis of inter-turn short circuit for five-phase PMSM based on Particle Swarm Optimization

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Fault diagnosis of five-phase fault-tolerant permanent-magnet motor based on principal component neural network

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