Detection of inter-turn faults in multi-phase ferrite-PM assisted synchronous reluctance machine

Inter-turn winding faults in five-phase ferrite-permanent magnet-assisted synchronous reluctance motors (fPMa-SynRMs) can lead to catastrophic consequences if not detected in a timely manner, since they can quickly progress into more severe short-circuit faults, such as coil-to-coil, phase-to-ground or phase-to-phase faults. This paper analyzes the feasibility of detecting such harmful faults in their early stage, with only one short-circuited turn, since there is a lack of works related to this topic in multi-phase fPMa-SynRMs. Two methods are tested for this purpose, the analysis of the spectral content of the zero-sequence voltage component (ZSVC) and the analysis of the stator current spectra, also known as motor current signature analysis (MCSA), which is a well-known fault diagnosis method. This paper compares the performance and sensitivity of both methods under different operating conditions. It is proven that inter-turn faults can be detected in the early stage, with the ZSVC providing more sensitivity than the MCSA method. It is also proven that the working conditions have little effect on the sensitivity of both methods. To conclude, this paper proposes two inter-turn fault indicators and the threshold values to detect such faults in the early stage, which are calculated from the spectral information of the ZSVC and the line currentsPeer ReviewedPostprint (published version

A High-Fidelity Computationally Efficient Transient Model of Interior Permanent-Magnet Machine With Stator Turn Fault

An accurate transient model of interior permanent-magnet (IPM) machine with stator turn fault with due account of magnetic saturation is essential to develop robust and sensitive interturn fault detection algorithms and to evaluate drive controller performance and stability under fault conditions. This paper proposes a general method of modeling stator turn fault using flux linkage map of IPM machine under fault extracted from finite-element (FE) analysis. Simulation results from the proposed fault model are compared against FE and experimental results. The results show that the proposed model matches well with experimental data

Neural network-based diagnostic tool for detecting stator inter-turn faults in line start permanent magnet synchronous motors

Three-phase line-start permanent magnet synchronous motors are considered among the most promising types of motors in industrial applications. However, these motors experience several faults, which may cause significant financial losses. This paper proposed a feed-forward neural network-based diagnostic tool for accurate and fast detection of the location and severity of stator inter-turn faults. The input to the neural network is a group of representative statistical and frequency-based features extracted from the steady-state three-phase stator current signals. The current signals with different numbers of shorted turns and loading conditions are captured using the developed finite element JMAG ™ model for interior mount LSPMSM. In addition, an experimental set-up was built to validate the finite element model and the proposed diagnostics tool. The simulation and experimental test results showed an overall accuracy of 93.125% in detecting the location and the size of inter-turn, whereas, the accuracy in detecting the location of the fault is 100%

Real-Time Hardware-in-the-Loop Simulation of Permanent Magnet Synchronous Motor Drives under Stator Faults

Hardware-in-the-loop (HIL) testing methods can facilitate the development of control strategies in a safe and inexpensive environment particularly when extreme operating conditions such as faults are considered. HIL methods rely on accurate real-time emulation of the equipment under investigation. However, no validated tools for real-time emulation of electrical drives under fault conditions are available. This paper describes the implementation of a high-fidelity real-time emulator of a Permanent Magnet Synchronous Motor (PMSM) drive in a platform suitable for HIL tests. The emulator is capable of representing the drive operation under both healthy conditions and during inter-turn stator winding faults. Nonlinearities due to saturation, higher order harmonics, slotting effects, etc. are accounted for using fourdimensional look-up tables obtained by finite element analysis (FEA). The proposed model is computationally efficient and capable of running in real-time in a FPGA platform and is validated against simulations and experimental results in a wide range of operating conditions. Potential applications of the proposed emulation environment to the development of drive control, fault detection and diagnostic algorithms are proposed

Investigation on Multi-Physics Modelling of Fault Tolerant Stator Mounted Permanent Magnet Machines

This thesis investigates the stator mounted permanent magnet machines from the point of view of fault tolerant capability. The topologies studied are switched flux (and its derivatives C-Core, E-Core and modular), doubly salient and flux reversal permanent magnet machines. The study focuses on fault mode operation of these machines looking at severe conditions like short-circuit and irreversible demagnetization. The temperature dependence of the permanent magnet properties is taken into account. A complex multi-physics model is developed in order to assess the thermal state evolution of the switched flux machine during both healthy and faulty operation modes. This model couples the electro-mechanical domain with the thermal one, thus being able to consider a large range of operating conditions. It also solves issues such as large computational time and resources while still maintaining the accuracy. Experimental results are also provided for each chapter. A hierarchy in terms of fault tolerant capability is established. A good compromise can be reached between performance and fault tolerant capability. The mechanism of the magnet irreversible demagnetization process is explained based on magnetic circuit configuration. It is also found that the studied topology are extremely resilient against the demagnetizing influence of the short-circuit current and the magnet demagnetization is almost only affected by temperature

Non-linear circuit based model of PMSM under inter-turn fault: a simple approach based on healthy machine data

The paper proposes a fast dynamic mathematical model to evaluate the performances of saturated permanent magnet synchronous machines (PMSM) under stator winding’s inter turn fault. The parameters of the model can be determined using only manufacturer’s data of the healthy machine. Two surface mounted PMSM have been considered to investigate the validity of the proposed approach; with distributed and concentrated winding. It has been shown that the proposed model predicts the fault current with a reasonable accuracy compared to the non-linear Finite Elements analyses and to the experimental results. This model can be incorporated in a global simulation environment of power electronic of electrical device since the computation time is very short

Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations

Paper VI is excluded from the dissertation until the article will be published.Permanent magnet synchronous motors (PMSMs) have played a key role in commercial and industrial applications, i.e. electric vehicles and wind turbines. They are popular due to their high efficiency, control simplification and large torque-to-size ratio although they are expensive. A fault will eventually occur in an operating PMSM, either by improper maintenance or wear from thermal and mechanical stresses. The most frequent PMSM faults are bearing faults, short-circuit and eccentricity. PMSM may also suffer from demagnetisation, which is unique in permanent magnet machines. Condition monitoring or fault diagnosis schemes are necessary for detecting and identifying these faults early in their incipient state, e.g. partial demagnetisation and inter-turn short circuit. Successful fault classification will ensure safe operations, speed up the maintenance process and decrease unexpected downtime and cost. The research in recent years is drawn towards fault analysis under dynamic operating conditions, i.e. variable load and speed. Most of these techniques have focused on the use of voltage, current and torque, while magnetic flux density in the air-gap or the proximity of the motor has not yet been fully capitalised. This dissertation focuses on two main research topics in modelling and diagnosis of faulty PMSM in dynamic operations. The first problem is to decrease the computational burden of modelling and analysis techniques. The first contributions are new and faster methods for computing the permeance network model and quadratic time-frequency distributions. Reducing their computational burden makes them more attractive in analysis or fault diagnosis. The second contribution is to expand the model description of a simpler model. This can be achieved through a field reconstruction model with a magnet library and a description of both magnet defects and inter-turn short circuits. The second research topic is to simplify the installation and complexity of fault diagnosis schemes in PMSM. The aim is to reduce required sensors of fault diagnosis schemes, regardless of operation profiles. Conventional methods often rely on either steady-state or predefined operation profiles, e.g. start-up. A fault diagnosis scheme robust to any speed changes is desirable since a fault can be detected regardless of operations. The final contribution is the implementation of reinforcement learning in an active learning scheme to address the imbalance dataset problem. Samples from a faulty PMSM are often initially unavailable and expensive to acquire. Reinforcement learning with a weighted reward function might balance the dataset to enhance the trained fault classifier’s performance.publishedVersio

Modelling and detection of faults in axial-flux permanent magnet machines

The development of various topologies and configurations of axial-flux permanent magnet machine has spurred its use for electromechanical energy conversion in several applications. As it becomes increasingly deployed, effective condition monitoring built on reliable and accurate fault detection techniques is needed to ensure its engineering integrity. Unlike induction machine which has been rigorously investigated for faults, axial-flux permanent magnet machine has not. Thus in this thesis, axial-flux permanent magnet machine is investigated under faulty conditions. Common faults associated with it namely; static eccentricity and interturn short circuit are modelled, and detection techniques are established. The modelling forms a basis for; developing a platform for precise fault replication on a developed experimental test-rig, predicting and analysing fault signatures using both finite element analysis and experimental analysis. In the detection, the motor current signature analysis, vibration analysis and electrical impedance spectroscopy are applied. Attention is paid to fault-feature extraction and fault discrimination. Using both frequency and time-frequency techniques, features are tracked in the line current under steady-state and transient conditions respectively. Results obtained provide rich information on the pattern of fault harmonics. Parametric spectral estimation is also explored as an alternative to the Fourier transform in the steady-state analysis of faulty conditions. It is found to be as effective as the Fourier transform and more amenable to short signal-measurement duration. Vibration analysis is applied in the detection of eccentricities; its efficacy in fault detection is hinged on proper determination of vibratory frequencies and quantification of corresponding tones. This is achieved using analytical formulations and signal processing techniques. Furthermore, the developed fault model is used to assess the influence of cogging torque minimization techniques and rotor topologies in axial-flux permanent magnet machine on current signal in the presence of static eccentricity. The double-sided topology is found to be tolerant to the presence of static eccentricity unlike the single-sided topology due to the opposing effect of the resulting asymmetrical properties of the airgap. The cogging torque minimization techniques do not impair on the established fault detection technique in the single-sided topology. By applying electrical broadband impedance spectroscopy, interturn faults are diagnosed; a high frequency winding model is developed to analyse the impedance-frequency response obtained

Stator turn fault detection by 2nd harmonic in instantaneous power for a triple redundant fault-tolerant PM drive

Fast and reliable detection of stator faults is of key importance for fail-safe and fault tolerant machine drives in order to immediately trigger appropriate fault mitigation actions. The paper presents a detailed analytical and experimental analysis of the behavior of a closed loop controlled permanent magnet machine drive under inter-turn fault conditions. It is shown that significant 2nd harmonic components in the dq voltages, currents, instantaneous active power (IAP) and reactive power (IRP) are generated during turn fault conditions. The analyses further show that the increase of the 2nd harmonic in IAP and IRP during fault conditions is comparatively higher than that of voltage and current, making them ideal candidates as turn fault indicators. A turn fault detection technique based on 2nd harmonic in IAP and IRP is implemented and demonstrated for a triple redundant, fault tolerant permanent magnet assisted synchronous reluctance machine (PMA SynRM) drive. The effectiveness of the proposed detection technique over the whole operation region is assessed, demonstrating fast and reliable detection over most of the operating region under both motoring and generating mode