Evolution of Controllers for the Speed Control in Thyristor Fed Induction Motor Drive

Induction Motors (IMs) are now becoming the pillar of almost all the motoring applications related to the industry and household. The practical applications of IMs usually require constant motoring speed. As a result, different types of control systems for IM's speed controlling have been shaped. One of the important techniques is the utilization of thyristor fed drive. Although, the thyristor fed induction motor drive (TFIMD) offers stable speed performance, the practical speed control demand is much more precise. Hence, this drive system utilizes additional controllers to attain precise speed for practical applications. This paper offers a detailed review of the controllers utilized with the thyristor fed IM drive in the past few decades to achieve good speed control performance. The clear intent of the paper is to provide a comprehensible frame of the pros and cons of the existing controllers developed for the TFIMD speed control requirements. Keywords: Thyristor Fed Drives, Induction Motors, Speed Controller, Conventional Controllers, and Soft Computing Techniques

Analysis of artificial intelligence in industrial drives and development of fault deterrent novel machine learning prediction algorithm

Industrial sectors rely on electrical inverter drives to power their various load segments. Because the majority of their load is nonlinear, their drive system behaviour is unpredictable. Manufacturers continue to invest much in research and development to ensure that the device can resist any disturbances caused by the power system or load-side changes. The literature in this field of study depicts numerous effects caused by harmonics, a sudden inrush of currents, power interruption in all phases, leakage current effects and torque control of the system, among others. These and numerous other effects have been discovered as a result of research, and the inverter drive has been enhanced to a more advanced device than its earlier version. Despite these measures, inverter drives continue to operate poorly and frequently fail throughout the warranty term. This failure analysis is used as the basis for this research work, which presents a method for forecasting faulty sections using power system parameters. The said parameters were obtained by field-test dataset analysis in industrial premises. The prediction parameter is established by the examination of field research test data. The same data are used to train the machine learning system for future pre-emptive action. When exposed to live data feeds, the algorithm may forecast the future and suggest the same. Thus, when comparing the current status of the device to the planned study effort, the latter provides an advantage in terms of safeguarding the device and avoiding a brief period of total shutdown. As a result, the machine learning model was trained using the tested dataset and employed for prediction purposes; as a result, it provides a more accurate prediction, which benefits end consumers rather than improving the power system\u27s grid-side difficulties

Development and Implementation of Novel Intelligent Motor Control for Performance Enhancement of PMSM Drive in Electrified Vehicle Application

The demand for electrified vehicles has grown significantly over the last decade causing a shift in the automotive industry from traditional gasoline vehicles to electric vehicles (EVs). With the growing evolution of EVs, high power density, and high efficiency of electric powertrains (e–drive) are of the utmost need to achieve an extended driving range. However, achieving an extended driving range with enhanced e-drive performance is still a bottleneck. The control algorithm of e–drive plays a vital role in its performance and reliability over time. Artificial intelligence (AI) and machine learning (ML) based intelligent control methods have proven their continued success in fault determination and analysis of motor–drive systems. Considering the potential of intelligent control, this thesis investigates the legacy space vector modulation (SVM) strategy for wide–bandgap (WBG) inverter and conventional current PI controller for permanent magnet synchronous motor (PMSM) control to reduce the switching loss, computation time and enhance transient performance in the available state–of–the-art e–drive systems. The thesis converges on AI– and ML–based control for e–drives to enhance the performance by focusing in reducing switching loss using ANN–based modulation technique for GaN–based inverter and improving transient performance of PMSM by incorporating ML–based parameter independent controller

Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic features

A three-phase pulse-width modulation (PWM) rectifier can usually maintain operation when open-circuit faults occur in insulated-gate bipolar transistors (IGBTs), which will lead the system to be unstable and unsafe. Aiming at this problem, based on random forests with transient synthetic features, a data-driven online fault diagnosis method is proposed to locate the open-circuit faults of IGBTs timely and effectively in this study. Firstly, by analysing the open-circuit fault features of IGBTs in the three-phase PWM rectifier, it is found that the occurrence of the fault features is related to the fault location and time, and the fault features do not always appear immediately with the occurrence of the fault. Secondly, different data-driven fault diagnosis methods are compared and evaluated, the performance of random forests algorithm is better than that of support vector machine or artificial neural networks. Meanwhile, the accuracy of fault diagnosis classifier trained by transient synthetic features is higher than that trained by original features. Also, the random forests fault diagnosis classifier trained by multiplicative features is the best with fault diagnosis accuracy can reach 98.32%. Finally, the online fault diagnosis experiments are carried out and the results demonstrate the effectiveness of the proposed method, which can accurately locate the open-circuit faults in IGBTs while ensuring system safety.Comment: IET Power Electronic

Advances in Rotating Electric Machines

It is difficult to imagine a modern society without rotating electric machines. Their use has been increasing not only in the traditional fields of application but also in more contemporary fields, including renewable energy conversion systems, electric aircraft, aerospace, electric vehicles, unmanned propulsion systems, robotics, etc. This has contributed to advances in the materials, design methodologies, modeling tools, and manufacturing processes of current electric machines, which are characterized by high compactness, low weight, high power density, high torque density, and high reliability. On the other hand, the growing use of electric machines and drives in more critical applications has pushed forward the research in the area of condition monitoring and fault tolerance, leading to the development of more reliable diagnostic techniques and more fault-tolerant machines. This book presents and disseminates the most recent advances related to the theory, design, modeling, application, control, and condition monitoring of all types of rotating electric machines

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

Internal Fault Diagnosis of MMC-HVDC Based on Classification Algorithms in Machine Learning

With the development of the HVDC system, MMC-HVDC is now the most advanced technology that has been put into use. In power systems, faults happen during the operation due to natural reasons or devices physical issues, which would cause serious economic losses and other implications. Thus, fault detection and analysis are extremely important, especially in the HVDC system. Existing works in literature mainly focus on the faults detection and analysis on the system side such as short circuit of the AC side, and open circuit of the DC side. However, little attention has been paid to the fault detection and analysis inside the converters. With the technology development of converter devices, replacing the whole converter becomes more expensive. Thus, my research mainly focuses on the detection and classification of the faults within the internal of the MMC module. In this research, an SPS model of MMC-HVDC is built as the example. Faults including short circuit and open circuit located inside the MMC module are simulated. Machine learning algorithms are chosen as the tool to achieve the goal of detecting faults and locating the fault position inside the MMC module precisely. After comparing the basic characteristics and properly application situations of various methods of machine learning, Coarse KNN, Complex Tree and Bagged Tree (Random Forest) are deployed to solve the problem. The performance of the methods are analyzed and compared, to get the most proper method in solving the problem

Internal Fault Diagnosis of MMC-HVDC Based on Classification Algorithms in Machine Learning

With the development of the HVDC system, MMC-HVDC is now the most advanced technology that has been put into use. In power systems, faults happen during the operation due to natural reasons or devices physical issues, which would cause serious economic losses and other implications. Thus, fault detection and analysis are extremely important, especially in the HVDC system. Existing works in literature mainly focus on the faults detection and analysis on the system side such as short circuit of the AC side, and open circuit of the DC side. However, little attention has been paid to the fault detection and analysis inside the converters. With the technology development of converter devices, replacing the whole converter becomes more expensive. Thus, my research mainly focuses on the detection and classification of the faults within the internal of the MMC module. In this research, an SPS model of MMC-HVDC is built as the example. Faults including short circuit and open circuit located inside the MMC module are simulated. Machine learning algorithms are chosen as the tool to achieve the goal of detecting faults and locating the fault position inside the MMC module precisely. After comparing the basic characteristics and properly application situations of various methods of machine learning, Coarse KNN, Complex Tree and Bagged Tree (Random Forest) are deployed to solve the problem. The performance of the methods are analyzed and compared, to get the most proper method in solving the problem

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications