Robust Condition Monitoring and Fault Diagnosis of Variable Speed Induction Motor Drives

The main types of faults studied in the literature are commonly categorized as electrical faults and mechanical faults. In addition to well known faults, the performance of a diagnostic algorithm and its operational reliability in harsh environments has been another concern. In this work, the reliability of an electric motor diagnosis signal processing algorithm itself is studied in detail under harsh industrial conditions. Reliability and robustness of the diagnosis has especially been investigated under 1) potential motor feedback error; 2) noise interference to a diagnosis-relevant system; 3) ease of implementation; and 4) universal application of diagnostic scheme in industry. Low cost and flexible implementation strategies are also presented. 1) Signature-based diagnosis has been performed utilizing the speed feedback information which is used to determine fault characteristic frequency. Therefore, feedback information is required to maintain high accuracy for precise diagnosis which, in fact, is not the case in a practical industrial environment due to industrial noise interferences. In this dissertation, the performance under feedback error is analyzed in detail and error compensation algorithms are proposed. 2) Fault signatures are commonly small where the amplitude is continuously being interfered with motor noise. Even though a decision is based on the signature, the detection error will not be negligible if the signature amplitude is within or close to the noise floor because the boundary noise level non-linearly varies and, hence, is quite ambiguous. In this dissertation, the effect of noise interference is analyzed in detail and a threshold design strategy is presented to discriminate potential noise content in diagnosis. 3) The compensating procedure of speed feedback errors and electrical machine current noise, characteristics which are basically non-stationary random variables, requires an exhaustive tracking effort. In this dissertation, the effective diagnosis implementation strategy is precisely presented for digital signal processor (DSP) system application. 4) Most of the diagnosis algorithms in the literature are developed assuming specific detection conditions which makes application difficult for universal diagnosis purposes. In this dissertation, by assuming a sinusoidal fault signal and its Gaussian noise contents, a general diagnosis algorithm is derived which can be applied to any diagnostic scheme as a basic tool

Degradation-Sensitive Control Algorithm Based on Phase Optimization for Interleaved DC–DC Converters

As the use of interleaved DC–DC converters in electric vehicles (EVs) increases, research on reliability improvement is required. In the case of interleaved DC–DC converters, degradation occurs between transistors and capacitors. In particular, transistor degradation imbalances cause an increase in output capacitor RMS current, which increases power loss and accelerates capacitor degradation. This degradation affects system reliability by increasing thermal stress. In this paper, based on a degraded 2-leg interleaved DC–DC boost converter, research to reduce the converter’s output capacitor RMS current was conducted. The output capacitor RMS current according to the transistor degradation imbalance was analyzed. As a result, it was confirmed that the transistor degradation imbalance causes an increase in the capacitor RMS current. To address this issue, a phase optimization algorithm for reducing increased capacitor RMS current is presented in this paper. Next, the phase optimization algorithm is mathematically analyzed. Finally, its efficacy is proved through simulations and experiments

Impacts of SiC-MOSFET Gate Oxide Degradation on Three-Phase Voltage and Current Source Inverters

In this paper, the performance variations of SiC MOSFET-based voltage and current source inverters under gate oxide degradation are studied. It is confirmed that the turn-on and turn-off delays of SiC MOSFETs change significantly by high electric field stress, which accelerates the gate oxide degradation. Variations in the turn-on and turn-off delays of switching devices extend or reduce the duty error of voltage source inverters and current source inverters. The performance variations of the voltage and current source inverter due to the duty error changes caused by the gate oxide degradation are analyzed through simulations. As a result, the gate oxide degradation worsens the performance of the voltage source inverter. Furthermore, the negative gate oxide degradation, which lowers the threshold voltage, decreases the performance of the current source inverter

Impacts of SiC-MOSFET Gate Oxide Degradation on Three-Phase Voltage and Current Source Inverters

In this paper, the performance variations of SiC MOSFET-based voltage and current source inverters under gate oxide degradation are studied. It is confirmed that the turn-on and turn-off delays of SiC MOSFETs change significantly by high electric field stress, which accelerates the gate oxide degradation. Variations in the turn-on and turn-off delays of switching devices extend or reduce the duty error of voltage source inverters and current source inverters. The performance variations of the voltage and current source inverter due to the duty error changes caused by the gate oxide degradation are analyzed through simulations. As a result, the gate oxide degradation worsens the performance of the voltage source inverter. Furthermore, the negative gate oxide degradation, which lowers the threshold voltage, decreases the performance of the current source inverter

Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors

High-Efficient Direct Power Control Scheme Using Predictive Virtual Flux for Three-Phase Active Rectifiers

In recent years, the pulse-width-modulation (PWM) converter has been found to have extensive applications in renewable energy, industrial fields, and others. The high efficiency requirement is crucial to operating a PWM rectifier in various applications, in addition to the fundamental control objectives of sinusoidal grid currents and the correct DC bus voltage. Additionally, in practical application, another issue arises when the grid voltage frequently experiences distortion, leading to a distorted grid current and a significant rise in total harmonic distortion (THD). To resolve these problems, a model predictive virtual flux-based direct power control (MPVFDPC) with improved power loss performance is proposed based on an integrated switching state predetermination strategy. The proposed MPVFDPC for PWM rectifier inherits the merits of both virtual flux control and direct power control, which have fast dynamic performance and the grid current THD is considerably decreased under distorted grid voltage states. The proposed technique aims to minimize switching loss under ideal and distorted grid voltage states by exploiting the discontinuous modulation concept by using a switching state predetermination strategy. The MPVFDPC with switching state predetermination strategy is proven by employing it in experiments as well as simulations in comparison with previous models: predictive direct power control (Conv. MPDPC) and conventional MPVFDPC (Conv. MPVFDPC). The acquired waveforms and quantitative data are employed to prove the effectiveness of the developed algorithm

Comparisons of Loss Reduction Techniques Based on Pulsewidth Modulation and Model Predictive Control for Three-Phase Voltage Source Inverters

Due to the lack of comparative studies between discontinuous pulse-width modulation and model predictive control methods for reducing switching losses in two-level three-phase voltage source inverter, a comparative analysis of a generalized discontinuous pulse-width modulation and two model predictive control approaches for reducing switching losses is studied in this paper. Both generalized discontinuous pulse-width modulation and two model predictive control approaches are described and conducted in the simulation and experiment. The output performance is obtained by these methods after conducting in various conditions, including switching frequency, output power, and load conditions. It is validated that the generalized discontinuous pulse-width modulation control scheme achieves a better control performance at steady-state, while two model predictive control schemes have better transient-state performance with a superior dynamic. Additionally, the generalized discontinuous pulse-width modulation approach achieves better reducing switching losses performance and has slightly higher efficiency than that of two model predictive control approaches

DC-Link Electrolytic Capacitors Monitoring Techniques Based on Advanced Learning Intelligence Techniques for Three-Phase Inverters

The reliability of the electronic converter is a vital concern in an industrialized area. Capacitors are critical in electronic converters and are more likely to fail than other electronic gears. Due to aging, the capacitor progressively loses its original quality and capacitance, and the equivalent series resistance escalates. Hence, condition monitoring is a fundamental procedure for evaluating capacitor health that affords prognostic repairs to guarantee stability in power networks. The ESR and capacitance of the capacitor are commonly employed to estimate the condition grade. This study proposes an estimation scheme that utilizes the source current to assess the health condition of an aluminum capacitor. Several advanced intelligence techniques are adopted to estimate the parameters of an AEC in a three-phase inverter system. First, different signals used as inputs, such as input power, capacitor current, voltage, and power, output current, voltage, and power, are analyzed using fast Fourier transform and discrete wavelet transform analysis. Then, various indexes of the analyzed signals, such as RMS, average, median, and variance, are used as the inputs in learning models to monitor the AEC’s parameters. In addition, various input signals are combined to obtain the best combinations for capacitor monitoring. The estimated results prove that utilizing the source current combined with selected indexes improves the monitoring accuracy of the AEC’s health status