On the reliability of electrical drives for safety-critical applications

The aim of this work is to present some issues related to fault tolerant electric drives,which are able to overcome different types of faults occurring in the sensors, in thepower converter and in the electrical machine, without compromising the overallfunctionality of the system. These features are of utmost importance in safety-criticalapplications. In this paper, the reliability of both commercial and innovative driveconfigurations, which use redundant hardware and suitable control algorithms, will beinvestigated for the most common types of fault: besides standard three phase motordrives, also multiphase topologies, open-end winding solutions, multi-machineconfigurations will be analyzed, applied to various electric motor technologies. Thecomplexity of hardware and control strategies will also be compared in this paper, sincethis has a tremendous impact on the investment costs

Power Quality Improvement of Distributed Generation Integrated Network with Unified Power Quality Conditioner.

With the increased penetration of small scale renewable energy sources in the electrical distribution network, maintenance or improvement of power quality has become more critical than ever where the level of voltage and current harmonics or disturbances can vary widely. For this reason, Custom Power Devices (CPDs) such as the Unified Power Quality Conditioner (UPQC) can be the most appropriate solution for enhancing the dynamic performance of the distribution network, where accurate prior knowledge may not be available. Therefore, the main objective of the present research is to investigate the (i) placement (ii) integration (iii) capacity enhancement and (iv) real time control of the Unified Power Quality Conditioner (UPQC) to improve the power quality (PQ) of a distributed generation (DG) network connected to the grid or microgrid

A Multifunctional SiC DC-DC Converter Topology with Normalized Fault Detection Strategy for Electric Vehicle Applications

The automotive industry is experiencing a monumental shift in technology and propulsion strategies. More than ever before, car manufacturers and suppliers are shifting development and funding away from combustion engines in favor of electrified powertrains. One of the main obstacles contributing to customers reluctance to buy EVs is the lack of infrastructure for charging. Traditional 110/220VAC outlets equipped at residential buildings are relatively low power compared to the batteries used in EVs today. These AC chargers, classified as level 1 and level 2, will take approximately 12-24 hours to completely charge a battery, depending on battery size and state-of-charge. Additionally, because this method of charging uses alternating current, vehicles must have chargers on-board to convert the energy from AC to DC to recharge the battery because EV batteries are direct current energy sources. Millions of dollars from the government and private companies are being invested to create an adequate DC fast charging infrastructure. The advantages of DC charging are two-fold, much quicker charging times and the elimination of onboard chargers. However, there is one blatant problem with current investments into a DC charging infrastructure – technological advancement. Most electric vehicles in production have battery pack voltages between 300V and 400V and current DC fast chargers are being developed for the current technology. This will likely change rather quickly; the development of wide-bandgap devices will allow for higher voltage devices. Furthermore, the energy densities of batteries will also likely improve, allowing for higher bus voltages. Higher bus voltages will offer several advantages over current architectures – more power, smaller devices, improved efficiencies, and more. The problem is, once higher bus voltages are achieved and popularized, the current fast charging infrastructure will be deemed obsolete. An intermediate solution needs to be developed to allow higher bus voltage vehicles to continue to utilize the current fast chargers being deployed nation-wide. The proposed DC-DC converter is a practical design that offers multiple purposes when implemented in electric vehicles that utilize permanent magnet synchronous machines (PMSM) and bus voltages of ~800V. It consists of a bi-directional interleaved DC-DC cascaded with an isolated full bridge converter. This configuration provides a 12V source with galvanic isolation during normal propulsion. The interleaved converter can boost in reverse to allow for charging of the 800V bus with current generation DC fast chargers operating at ~400V. Finally, an inverter fault detection methodology has been realized to take advantage of the interleaved DC-DC structure. If an open switch fault is detected on any of the 3-phases driving the PMSM, the appropriate phase-leg is isolated, and a phase-leg from the interleaved DC-DC is used to maintain propulsion. This is realized by monitoring the phase currents of the AC motor and analyzing the difference in value between all three. A threshold value is implemented in C-code, not contingent on the system parameters. A difference of phase currents greater than the threshold value is a clear indication that an open switch fault has occurred. The proposed power conversion structure and the motor inverter fault detection, isolation, and compensation approaches are verified by a PSIM simulation. The simulation results successfully validate the feasibility of proposed electric powertrain structure and inverter switch fault detection and compensation methods.Master of Science in EngineeringEnergy Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/156398/1/Brandon Pieniozek Final Thesis.pdfDescription of Brandon Pieniozek Final Thesis.pdf : Thesi

A Novel Control Method For Grid Side Inverters Under Generalized Unbalanced Operating Conditions

This thesis provides a summary on renewable energy sources integration into the grid, using an inverter, along with a comprehensive literature research on variety of available control methods. A new generalized method for grid side inverter control under unbalanced operating conditions is also proposed. The presented control method provides complete harmonic elimination in line currents and DC link voltage with adjustable power factor. The method is general, and can be used for all levels of imbalance in grid voltages and line impedances. The control algorithm proposed in this work has been implemented by using MATLAB Simulink and dSPACE RT1104 control system. Simulation and experimental results presented in this thesis are in excellent agreement

Modern Diagnostics Techniques for Electrical Machines, Power Electronics, and Drives

© 2015 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] For the last ten years, at least three different special sections dealing with diagnostics in power electrical engineering have been published in the IEEE transactions on industrial electronics [1]-[5]. All of them had their specificities, but the last ones, starting in 2011, were more connected to relevant events organized on the topic. In fact, these events have been clearly the only international forums fully dedicated to diagnostics techniques in power electrical engineering. For this particular issue, it has been decided to separate the different submissions into six parts: state of the art; general methods; induction machines (IMs); synchronous machines (SMs); . electrical drives; power components and power converters. The second section includes only one state-of-the-art paper, which is dedicated to actual techniques implemented in both industry and research laboratories. The third section includes three papers on diagnostic techniques not specifically aimed at a particular type of machine. The fourth section includes three papers devoted to diagnostics of rotor faults, two dedicated to stator insulation issues, and four papers dealing with mechanical faults diagnosis in IMs. The fifth section includes papers focusing on different types of SMs. The first two papers deal with wound-rotor SMs, the following three papers are dedicated to permanent-magnet radial flux machines, and the last one deals with permanent-magnet axial flux machines. Regarding the types of faults analyzed, there are three papers devoted to the diagnosis of interturn short circuits in the stator windings, i.e., one dedicated to the detection and location of field-winding-to-ground faults and a paper devoted to the diagnosis of static eccentricities. In the sixth section, two papers investigate issues related to faults in drive sensors, and one is devoted to fault detections in the coupling inductors. The last section includes two papers devoted to diagnosis of faults and losses analysis in switching components of power converters.Capolino, G.; Antonino-Daviu, J.; Riera-Guasp, M. (2015). Modern Diagnostics Techniques for Electrical Machines, Power Electronics, and Drives. IEEE Transactions on Industrial Electronics. 62(3):1738-1745. doi:10.1109/TIE.2015.2391186S1738174562

On Deep Machine Learning Based Techniques for Electric Power Systems

This thesis provides deep machine learning-based solutions to real-time mitigation of power quality disturbances such as flicker, voltage dips, frequency deviations, harmonics, and interharmonics using active power filters (APF). In an APF the processing delays reduce the performance when the disturbance to be mitigated is tima varying. The the delays originate from software (response time delay) and hardware (reaction time delay). To reduce the response time delays of APFs, this thesis propose and investigate several different techniques. First a technique based on multiple synchronous reference frame (MSRF) and order-optimized exponential smoothing (ES) to decrease the settling time delay of lowpass filtering steps. To reduce the computational time, this method is implemented in a parallel processing using a graphics processing unit (GPU) to estimate the time-varying harmonics and interharmonics of currents. Furthermore, the MSRF and three machine learning-based solutions are developed to predict future values of voltage and current in electric power systems which can mitigate the effects of the response and reaction time delays of the APFs. In the first and second solutions, a Butterworth filter is used to lowpass filter the\ua0 dq\ua0 components, and linear prediction and long short-term memory (LSTM) are used to predict the filtered\ua0 dq\ua0 components. The third solution is an end-to-end ML-based method developed based on a combination of convolutional neural networks (CNN) and LSTM. The Simulink implementation of the proposed ML-based APF is carried out to compensate for the current waveform harmonics, voltage dips, and flicker in Simulink environment embedded AI computing system Jetson TX2.\ua0In another study, we propose Deep Deterministic Policy Gradient (DDPG), a reinforcement learning (RL) method to replace the controller loops and estimation blocks such as PID, MSRF, and lowpass filters in grid-forming inverters. In a conventional approach it is well recognized that the controller tuning in the differen loops are difficult as the tuning of one loop influence the performance in other parts due to interdependencies.In DDPG the control policy is derived by optimizing a reward function which measure the performance in a data-driven fashion based on extensive experiments of the inverter in a simulation environment.\ua0Compared to a PID-based control architecture, the DDPG derived control policy leads to a solution where the response and reaction time delays are decreased by a factor of five in the investigated example.\ua0Classification of voltage dips originating from cable faults is another topic addressed in this thesis work. The Root Mean Square (RMS) of the voltage dips is proposed as preprocessing step to ease the feature learning for the developed\ua0 LSTM based classifier. Once a cable faults occur, it need to be located and repaired/replaced in order to restore the grid operation. Due to the high importance of stability in the power generation of renewable energy sources, we aim to locate high impedance cable faults in DC microgrid clusters which is a challenging case among different types of faults. The developed Support Vector Machine (SVM) algorithm process the maximum amplitude and\ua0 di/dt\ua0 of the current waveform of the fault as features, and the localization task is carried out with\ua0 95 %\ua0 accuracy.\ua0Two ML-based solutions together with a two-step feature engineering method are proposed to classify Partial Discharges (PD) originating from pulse width modulation (PWM) excitation in high voltage power electronic devices. As a first step, maximum amplitude, time of occurrence, area under PD curve, and time distance of each PD are extracted as features of interest. The extracted features are concatenated to form patterns for the ML algorithms as a second step. The suggested feature classification using the proposed ML algorithms resulted in\ua0 95.5 %\ua0 and\ua0 98.3 %\ua0\ua0 accuracy on a test data set using ensemble bagged decision trees and LSTM networks

Investigations of New Fault-Tolerant Methods for Multilevel Inverters

The demands of power electronics with high power capability have increased in the last decades. These needs have driven the expansion of existing power electronics topologies and developing new power electronics generations. Multilevel inverters (MLI) are one of the most promising power electronics circuits that have been implemented and commercialized in high-voltage direct current (HVDC), motor drives, and battery energy storage systems (BESS). The expanding uses of the MLI have lead to creation of new topologies for different applications. However, one of the disadvantages of using MLIs is their complexity. MLIs consist of a large number of switching devices, which can result in a reduction of system reliability. There are significant challenges to the design of a reliable system that has the MLI’s capability with integrated fault-tolerance. In other words, design a system that can handle the fault, totally or partially, while maintaining high power capabilities and efficiency. This aim of this dissertation is to investigate the fault-tolerance of MLIs from two different points of view: 1- Develop new solutions for existing MLI topologies. In other words, add some features to existing MLIs to improve their reliability when a fault occurs. 2- Design new MLIs that have a fault-tolerant capability. A new open-circuit fault detection is proposed in this dissertation. The new fault detection method is based on monitoring the output voltage of each cell and leg voltage polarity along with each switch state. By monitoring each cell output voltage and leg voltage, the faulty cell can be detected and isolated. A novel circuit to maintain system operation under the condition of one (or more) components suffering from a faulted condition is also proposed in this dissertation. This results in a topology that continues to operate at full capability. Additionally, a new topology is proposed that offers reducing the number of batteries by 50%. Also, it has the ability to operate under non-unity power factor, which enables it to be suitable for battery energy storage systems, and static compensator (STATCOM) applications. Another novel hybrid cascaded H-bridge (CHB), known as the X-CHB, for a fault-tolerant operation is proposed in this dissertation. It ensures seamless operation of the system under an open/short circuit switching fault or dc supply fault

Design and Control of Electrical Motor Drives

Dear Colleagues, I am very happy to have this Special Issue of the journal Energies on the topic of Design and Control of Electrical Motor Drives published. Electrical motor drives are widely used in the industry, automation, transportation, and home appliances. Indeed, rolling mills, machine tools, high-speed trains, subway systems, elevators, electric vehicles, air conditioners, all depend on electrical motor drives.However, the production of effective and practical motors and drives requires flexibility in the regulation of current, torque, flux, acceleration, position, and speed. Without proper modeling, drive, and control, these motor drive systems cannot function effectively.To address these issues, we need to focus on the design, modeling, drive, and control of different types of motors, such as induction motors, permanent magnet synchronous motors, brushless DC motors, DC motors, synchronous reluctance motors, switched reluctance motors, flux-switching motors, linear motors, and step motors.Therefore, relevant research topics in this field of study include modeling electrical motor drives, both in transient and in steady-state, and designing control methods based on novel control strategies (e.g., PI controllers, fuzzy logic controllers, neural network controllers, predictive controllers, adaptive controllers, nonlinear controllers, etc.), with particular attention to transient responses, load disturbances, fault tolerance, and multi-motor drive techniques. This Special Issue include original contributions regarding recent developments and ideas in motor design, motor drive, and motor control. The topics include motor design, field-oriented control, torque control, reliability improvement, advanced controllers for motor drive systems, DSP-based sensorless motor drive systems, high-performance motor drive systems, high-efficiency motor drive systems, and practical applications of motor drive systems. I want to sincerely thank authors, reviewers, and staff members for their time and efforts. Prof. Dr. Tian-Hua Liu Guest Edito