Alberto Berzoy

This study presents an in-service non-invasive non-destructive fault diagnosis (FD) routine to detect stator's incipient faults in direct torque control (DTC) driven induction motors (IM). The study focuses on the IM stator's inter-turn failure (ITF). The developed FD algorithm is a time-domain based algorithm based on the application of the mathematical morphological gradient (MMG) technique on the stator currents. It is less computational burdensome than the frequency-domain based FD techniques. To detect the faulty phase, we introduced and implemented the differential MMG (MMG) algorithm concept. A comprehensive investigation of harmonics, inter-harmonics, and sub-harmonics contents due to the ITF, the DTC controller's reaction to it, the inverter's switching activity, and the ITF effect on the air-gap flux density were deeply investigated. A performance index and design guideline of selecting the structuring element (SE) length for in the FD routine were introduced and implemented. A physics-based co-simulation platform was built to investigate the feasibility of the proposed FD algorithm. Simulation reinforced by experimental results depicted the robustness of the FD method. The comparative analysis is presented between the developed MMG and the conventional motor current signature analysis (MCSA). The experimental results proved that developed FD is faster, more accurate, and more robust than the MCSA in detecting the ITFs at their embryonic stages.

Islanded nanogrids (NGs) are autonomous systems consisting of small-scale generation units including renewable energy sources and traditional fuel generators and energy storage systems (ESS) that typically serve few buildings or loads. This work aims at developing and validating a new optimal energy management (EM) algorithm for an islanded NG. To minimize the generator's operating cost and maximize battery availability at each operating cycle, dynamic programming (DP) framework is employed to solve the underlying optimization problem. The goal of the proposed approach is to ensure the use of maximum available solar power and to achieve optimal battery state of charge (SOC). To meet that goal, the management of the ESS is formulated as a stochastic optimal control problem, where nonlinearities in the battery discharging process are considered. A Markov model is constructed for predicting the probability distribution of the solar production used in the stochastic DP formulation. Simulation results are given to illustrate the efficacy of the proposed DP-based approach compared to a rule-based algorithm. Finally, a hardware-in-the-loop system is used to evaluate the real-time operation of the proposed EM algorithm.

Stator's turn fault is one of the major causes of induction motor's (IM) failures. IMs are typically driven by direct torque control (DTC) on industry applications and more recently on electric vehicles (EV). Thus, in this study, an in-service non-intrusive stator turn fault detection (FD) technique for a DTC inverter-fed IM is proposed. The presented FD strategy is based on the on-line estimation of the off-diagonal term in the IM sequence components (SCs) impedance matrix. The FD doesn't require the prior knowledge of IM's parameters, and it is robust and immune against sensor's inherent errors. Furthermore, the proposed FD routine is highly sensitive as it is able to detect incipient failure of approximately 1% fault severity factor. Two practical methodologies are applied for the computation of the current and voltage SC: 1. symmetrical components and 2. Savitzky-Golay filter. Simulation reinforced by experimental results demonstrated the success of the FD in a closed-loop controlled IM.

The objective of this PhD research is to develop robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters. The flexible energy forms synthesized by these connected power electronic converters greatly enhance the performance and expand the operating region of induction motors. They also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. The current state of the art in condition monitoring of power-converter-fed electric machines is underdeveloped as compared to the maturing condition monitoring techniques for grid-connected electric machines. This dissertation first investigates the stator turn-to-turn fault modelling for induction motors (IM) fed by a grid directly. A novel and more meaningful model of the motor itself was developed and a comprehensive study of the closed-loop inverter drives was conducted. A direct torque control (DTC) method was selected for controlling IM’s electromagnetic torque and stator flux-linkage amplitude in industrial applications. Additionally, a new driver based on DTC rules, predictive control theory and fuzzy logic inference system for the IM was developed. This novel controller improves the performance of the torque control on the IM as it reduces most of the disadvantages of the classical and predictive DTC drivers. An analytical investigation of the impacts of the stator inter-turn short-circuit of the machine in the controller and its reaction was performed. This research sets a based knowledge and clear foundations of the events happening inside the IM and internally in the DTC when the machine is damaged by a turn fault in the stator. This dissertation also develops a technique for the health monitoring of the induction machine under stator turn failure. The developed technique was based on the monitoring of the off-diagonal term of the sequence component impedance matrix. Its advantages are that it is independent of the IM parameters, it is immune to the sensors’ errors, it requires a small learning stage, compared with NN, and it is not intrusive, robust and online. The research developed in this dissertation represents a significant advance that can be utilized in fault detection and condition monitoring in industrial applications, transportation electrification as well as the utilization of renewable energy microgrids. To conclude, this PhD research focuses on the development of condition monitoring techniques, modelling, and insightful analyses of a specific type of electric machine system. The fundamental ideas behind the proposed condition monitoring technique, model and analysis are quite universal and

This paper investigates the online fault detection (FD) of the inter-turn short-circuit (ITSC) fault in direct torque control (DTC)-driven induction motors (IMs). We conducted the investigations of harmonic, inter-harmonic, and sub-harmonic contents for FD purposes. The harmonics in the flux leakage spectrum appear as a result of diverse causes including stator fault, DTC reaction to the fault, switching activities, and core saturation effect. Physics-based co-simulation was held through the coupling between a finite-element model (FEM) of the faulty IM and the circuit simulation of DTC. Its advantages are: 1) the IM harmonic contents including stator slots, rotor slots, saturation, switching activity, and fault are simulated and 2) dangerous experimentations of a faulty IM can be avoided. Therefore, we can safely study all the characteristics on a faulty IM. We exploited FEM for the verification of the experimental harmonic signatures of the scattered magnetic field. We verified the online FD technique based on the radiated magnetic field's spectrum through the co-simulation and experimental results. This indicated that the method is able to correctly detect the ITSC faults with 1% severity factor.

This paper presents an investigation of a three-phase squirrel-cage induction machine (IM) with inter-turn short-circuit (ITSC) fault driven by Direct Torque Control (DTC). DTC is the most typical driver for controlling IM's torque and flux in industrial applications. Also, DTC has been classified as a non-linear hysteresis-based predictive controller. Two state-space (SS) models for the asymmetric IM are presented. The first model uses flux-linkages and the second one uses currents as SS variables. The advent of these models is to provide flux and currents vectors information to calculate the angle difference between the trajectories of constant torque and constant stator flux amplitude, expressed as a function of the inverter voltage components. Additionally, errors in the estimated torque and flux are found throughout mathematical models that explains the DTC reaction to the ITSC effects. Simulation and experimental results are conducted for the asymmetric IM driven by DTC to observe the limits of the controller and to corroborate their theoretical impacts which in future research could be utilized for ITSC fault detection.

In this paper an in-service non-intrusive stator winding inter-turn fault detector (FD) for an inverter-fed induction machine (IM) is proposed. The FD is based on the computation of the off-diagonal term of the sequence components (SCs) impedance matrix. This technique was proven efficaciously for direct on-line (DOL) IM, as it deciphers its abnormal impedance behavior with neither additional hardware nor IM parameter's knowledge. Additionally, the FD technique is immune to sensor errors. The hypothesis of this article is that the offdiagonal sequence impedance is not affected by the Direct Torque Control (DTC) and therefore, the contribution of this article is the adaptation of the FD to an inverter-fed IM under DTC. Simulation reinforced by experimental results demonstrated the correctness of the hypothesis, as well as the robustness of the FD method for closed-loop controlled IM, against other detectors used in DOL IM, where their signatures are hidden by the controller.

Electric vehicle (EV) can act as an energy consuming device or a distributed energy resource in the concepts known as vehicle-to-grid (V2G) and grid-to-vehicle (G2V). The bidirectional wireless power interface is a successful choice for this application, since it provides automatic, reliable, and safe operation. Therefore, this paper presents modeling and assessment analysis for the steady-state performance of bidirectional inductive wireless power transfer system (BIWPTS) in EV implementations. The analysis is presented for the three main compensation configurations: LC-series, LC-parallel, and LCL-topology. Moreover, the steady-state equivalent circuit-based mathematical models for all topologies are developed. These models were used to precisely determine the system response during V2G and G2V operations based on Fourier series. The study presents evident criteria to pick among the different BIWPTS structures in the diverse applications. The proposed analyses were verified both in simulation and experiment. The results demonstrate the ability of the proposed models to provide accurate estimation for BIWPTS performance under various operating and control conditions. Also, the evaluation analysis shows that LCL-topology is more appropriate for the bidirectional operation due to the simple design and control requirements, and being less sensitive to the misalignment.

This paper proposes an enhanced strategy for direct torque control (DTC) combining artificial intelligent (AI) and predictive algorithms. The advantages of this merge are in the solution of closed-loop controlled induction machine (IM) problems. Predictive DTC (P-DTC) methods reduce the high torque ripple and improve the performance at both starting condition and low mechanical speed operation. However, P-DTC depends on the IM parameter's knowledge. The approach here is the introduction of fuzzy logic control with dynamic rules based on the P-DTC law's to reduce the parameter dependency and improve the performance of P-DPC. Additional comparative performance study of eight modulation strategies under the proposed fuzzy-predictive DTC (FP-DTC) is conducted. It results that the space-vector modulation (SVM) is the most suitable scheme with the best combination of criteria such stator current total harmonic distortion, switching losses and dynamic behavior. The parameter dependency of the FP-DTC is tested by a sensitivity analysis which corroborates the robustness of the proposed control. For verification purposes, simulations of the DTC, P-DTC, and FP-DTC were conducted and compared. Experimental results for the three controllers and two modulations (pulse width modulation and SVM) confirm the expected performance of the proposed control algorithm and modulation assessment study.

This paper studies the effect of the location of stator inter-turn short-circuit (ITSC) faults on the squirrel-cage induction machine (IM) model parameter’s. The ITSC location can be affected in three different ways, however we studied two of them: the physical placement for a fixed number of turns fault and the variation of the number of turns short-circuited. Both different cases will influence the rotating magnetic field shape in the airgap and thus the stator and rotor parameters. Two investigations are conducted, the former one is a finite element analysis (FEA) of four location cases of equal severity factor ITSC in the IM phase “a”. The latter is a theoretical and mathematical analysis where the ITSC is modeled by a step-down auto-transformer in the faulty circuit. The results of the FEA corroborate that different locations impacts the IM parameters, however, the effect is not substantial and the theoretical model indicates how the parameters vary with the fault. Moreover, a comparative study of two IM damaged models that present different approach to the stator leakage inductance is performed. The asymmetric mathematical model is presented, simulated and experimentally compared.