Detection of Inter-Turn Faults in Multi-Phase Ferrite-PM Assisted Synchronous Reluctance Machines

Abstract

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 currents.Energies 2019, 12, 2733 2 of 15 ripple in the output, although torque pulsations can be minimized by means of a suitable design, including rotor skewing, asymmetric flux barriers or selected rotor steps [5].Faults in electrical machines could produce loss of system reliability, unscheduled shutdowns [9], important economic losses or even harmful effects to humans. Therefore, there is a growing demand for improved fault diagnosis approaches in electrical machines, in particular for high-performance applications [10]. This strategy ensures the safe and reliable operation of the plant, while greatly reducing unexpected and unscheduled fault occurrences, thus improving system availability and minimizing economic losses and the likelihood of accidents [11].Rotating electric machines are designed with mechanical and electrical symmetry to maximize performance and efficiency and to minimize vibrations. When operating under faulty conditions this symmetry is lost, thus changing the magnitude or the shape of different machine signals, such as the electromotive force, line currents, vibrations profile or the temperature, among others [12]. Most of these faults generate specific patterns of such signals, so these changes can be used for fault diagnosis purposes.Different fault diagnosis techniques have been analyzed in the technical literature, based on on-line or off-line approaches. Whereas off-line methods require the disconnection of the machine and sometimes the removal of some components, on-line diagnosis methods require the addition of specific sensors to acquire data from the machine during normal operation, although in some cases no extra sensors are required. Due to the ex...

Detection of Partial Demagnetization Faults in Five-Phase Permanent Magnet Assisted Synchronous Reluctance Machines

“…Whereas the former methods are based on disconnecting the machine from the mains, the latter approach is most straightforward and simple to apply [12], since it does not require to disconnect the machine, although in some cases, specific sensors must be added to detect the faults. Faults can be detected on-line by monitoring and analyzing the voltages and currents at the machine terminals, although other magnitudes can be analyzed, including changes in the magnetic field, the input impedance, the vibrations pattern [13] or in the speed [14].…”

Fault Diagnosis in Multi Phase Induction Machine using Mind Evolution Computation Algorithm Optimized Neural Network

“…where, V m is the fundamental component's maximum magnitude, is the noise found in the voltage waveform, t is the sampling period, is the fundamental component's phase, and is the voltage's angular frequency. The complex form of signal arrived from the motor voltage is got by αβ transformation [11] as stated as follows, (5) The complex voltage can be gotten as, (6) The voltage V t can be modelled as, (7) In which, A is the amplitude of this signal V t , and is voltage's noise component and . The voltage could be modelled as, (8) This version is also used in the proposed attribute estimation and the scheme that describes the extraction method is depicted in Fig.…”

“…The procedure and the basic theory of fault identification of multiphase induction motor are all discussed [5,6]. To the grounds of this study, the motor voltage and its slope are used as the features to diagnosis the fault in multiphase induction motor; and a identification system based on neural network has been now exhibited.…”

Neural Network Based Fault Diagnostics in Multi Phase Induction Machine

“…Investigation and identification of malfunctions is still a significant problem at present, since (a) the relationship between the cause of the malfunction and its symptom is quite complex; (b) the convenience of the fault identification procedure for a multiphase induction motor is rather limited; (c) the procedure for identifying artificial intelligence based on a fundamental discourse, you can find many questions, such as expression and gaining understanding, a fundamental lawsuit, etc. The procedure and the basic theory of fault identification of a multiphase induction motor are all discussed [5,6]. Based on this study, the voltage of the motor and its slope are used as functions for diagnosing a malfunction in a multiphase induction motor; and an identification system based on a neural network is now exposed.…”