Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

EFFICIENCY AND RELIABILITY ENHANCEMENT OF MULTIPHASE SYNCHRONOUS MOTOR DRIVES

Multiphase electric machines are attractive in comparison with three-phase ones due to advantages such as fault-tolerant nature, smaller rating per phase and lower torque ripple. More specifically, the machines with multiple three-phase windings are particularly convenient, because they are suitable for standard off-the-shelf three-phase dc/ac converter modules. For instance, they are becoming a serious option for applications such as electric vehicles and wind turbines. On the other hand, in these applications, operation at low power is often required for long time intervals; hence, improving the efficiency under such conditions is highly desired and could save a significant amount of energy in the long term. This dissertation proposes a method to enhance the efficiency of electric drives based on multiple three-phase windings at light load. The number of active legs is selected depending on the required torque at each instant. To ensure that the overall efficiency is effectively optimized, not only the converter losses, but also the stator copper losses, are taken into account. Experimental results verify the theoretical outcomes. Surface-mounted permanent-magnet synchronous motors (SPMSMs) require a position measurement to ensure a high-performance control. To avoid the cost and maintenance associated to position sensors, sensorless methods are often preferred. The approaches based on high-frequency signal injection are currently a well-established solution to obtain an accurate position estimation in SPMSMs. These techniques can be roughly divided into two groups: those based on sinusoidal or on square-wave high-frequency signals. The main drawback of the former is the limitation on the response speed, due to the presence of several low-pass filters (LPFs). On the other hand, the latter methods are sensitive to deadtime effects, and high-frequency closed-loop current control is required to overcome it. This dissertation proposes to improve the sensorless strategies based on sinusoidal high-frequency injection by simplifying the scheme employed to extract the information about the position error. Namely, two LPFs and several multiplications are removed. Such simplification does not only reduce the computational complexity, but also permits to obtain a faster response to the changes in the angle/speed, and hence, a faster closed-loop control. Experimental results based on a SPMSM prove the enhanced functionality of the proposed method with respect to the previous ones based on high-frequency sinusoidal signal injection

Modelling and indirect field‐oriented control for pole phase modulation induction motor drives

In the recent days, for the traction and electric vehicle (EV) applications, multiphase machines with pole phase modulation (PPM) technique have been proposed. The smoother operation during pole changeovers as well as steady-state operations is a significant constraint while adopting the PPM-based multiphase induction motor (PPMIM) drives for EV and traction applications. So, in this paper, the PPMIM dynamic model and associated vector control are proposed for attaining a smoother operation of the machine. The machine modelling equations and transformation matrices are implemented in an arbitrary reference frame by considering the different pole phase combinations. Based on the modelling equations, the indirect field-oriented control (IFOC) is proposed for PPMIM drives by reflecting the associated changes in parameters for different pole phase modes. In the IFOC, for regulating the d-axis and q-axis current components, single PI control loops have been implemented for all pole-phase combinations. The proposed IFOC scheme is robust and applicable for adopting any type of pulse width modulation. The experimental, as well as simulation results, are given to illustrate the potentiality of the proposed dynamic model and IFOC. The PPMIM machine performance during the steady state as well as pole changeovers in different pole phase modes are analyzed and associated. Simulation and experimental results are presented

Modeling and Simulation of Five-Phase Induction Motor Fed With Pulse Width Modulated Five-Phase Multilevel Voltage Source Inverter Topologies

This paper presents modelling and simulation of five-phase induction motor fed with pulse width modulated five-phase multilevel voltage source inverter. The conventional and diode clamped multilevel five-phase inverter configurations are reviewed with pulse width modulation (PWM) techniques. A hybrid three-level inverter topology with less number of components count is proposed for five-phase induction motor drive. The dynamic analysis of five-phase voltage equations in d-q axis of the induction motor are stated and modelled usingMatlab/Simulink/Simscape blocks. The simulation results based on conventional and threelevel five-phase inverters are displayed while the hybrid inverter topology showed some better performance based on the following: : at 0.0127secs maximum torque of 34.54Nm occurred, maximum stator current occurred for 0.18secs with a value of 10A, 9.99% total harmonic distortion was obtained and 15KW power rating was obtained

Analysis of the Harmonic Performance of Power Converters and Electrical Drives

Power converters have progressively become the most efficient and attractive solution in recent decades in many industrial sectors, ranging from electric mobility, aerospace applications to attain better electric aircraft concepts, vast renewable energy resource integration in the transmission and distribution grid, the design of smart and efficient energy management systems, the usage of energy storage systems, and the achievement of smart grid paradigm development, among others.In order to achieve efficient solutions in this wide energy scenario, over the past few decades, considerable attention has been paid by the academia and industry in order to develop new methods to achieve power systems with maximum harmonic performance aiming for two main targets. On the one hand, the high-performance harmonic performance of power systems would lead to improvements in their power density, size and weight. This becomes critical in applications such as aerospace or electric mobility, where the power converters are on-board systems. On the other hand, current standards are becoming more and more strict in order to reduce the EMI and EMC noise, as well as meeting minimum power quality requirements (i.e., grid code standards for grid-tied power systems)

Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

Improved field oriented control for stand alone dual star induction generator used in wind energy conversion

This paper presents a novel direct rotor flux oriented control with online estimation of magnetizing current and magnetizing inductance applied to self-excited dual star induction generator equipping a wind turbine in remote sites. The induction generator is connected to nonlinear load through two PWM rectifiers. The fuzzy logic controller is used to ensure the DC bus voltage a constant value when changes in speed and load conditions. In this study, a performance comparison between the conventional approach and the novel approach is made. The proposed control strategy is validated by simulation in Matlab/Simulink

Predictive current control in electrical drives: an illustrated review with case examples using a five-phase induction motor drive with distributed windings

The industrial application of electric machines in variable-speed drives has grown in the last decades thanks to the development of microprocessors and power converters. Although three-phase machines constitute the most common case, the interest of the research community has been recently focused on machines with more than three phases, known as multiphase machines. The principal reason lies in the exploitation of their advantages like reliability, better current distribution among phases or lower current harmonic production in the power converter than conventional three-phase ones, to name a few. Nevertheless, multiphase drives applications require the development of complex controllers to regulate the torque (or speed) and flux of the machine. In this regard, predictive current controllers have recently appeared as a viable alternative due to an easy formulation and a high flexibility to incorporate different control objectives. It is found however that these controllers face some peculiarities and limitations in their use that require attention. This work attempts to tackle the predictive current control technique as a viable alternative for the regulation of multiphase drives, paying special attention to the development of the control technique and the discussion of the benefits and limitations. Case examples with experimental results in a symmetrical five-phase induction machine with distributed windings in motoring mode of operation are used to this end

EVOLUTION AND DISSEMINATION OF SPECIALIZED STRATEGIES, METHODS, AND TECHNIQUES OF SYNCHRONOUS PULSEWUDTH MODULATION FOR CONTROL OF VOLT-AGE SOURCE INVERTERS AND INVERTER-BASED SYSTEMS

This publication provides a brief historical overview of the development of methods and techniques of pulsewidth modulation (PWM) for voltage source inverters, published mainly in Ukrainian publishing houses and in Ukrainian periodicals. Accent has been done to review of results of investigation of alternative methods and techniques of synchronous space-vector-based multi-zone PWM for inverters with low switching frequency. In particular, in the mentioned publications, the basic strategies, schemes, and algorithms of synchronous multi-zone modulation have been further developed, modernized, modified, and disseminated in relation to new promising topologies of power conversion systems, including: two-inverter-based electric drives with open-end winding of electrical motor; dual three-phase electric drives of symmetrical and asymmetric types; powerful six-phase systems based on four inverters, and two-inverter-based and three-inverter-based photovoltaic installations with multi-winding transformer. It is shown that the developed schemes and algorithms of synchronous space-vector PWM, applied for control of inverter-based systems, provide continuous synchronization and symmetry of basic voltage waveforms of systems during the whole control range including zone of overmodulation of inverters. It provides minimization of even harmonics and undesirable subharmonics (of the fundamental frequency) in spectra of the basic voltages of systems, leading to reducing of losses in systems and to increasing of its efficiency. Based on a comparative analysis of the integral spectral characteristics of the phase and line voltages of systems, recommendations are formulated for the rational choice of schemes and algorithms of synchronous modulation for the relevant installations, depending on the modes of their operation. References 30, tables 2, figures 25