Modeling and Real-Time Scheduling of DC Platform Supply Vessel for Fuel Efficient Operation

DC marine architecture integrated with variable speed diesel generators (DGs) has garnered the attention of the researchers primarily because of its ability to deliver fuel efficient operation. This paper aims in modeling and to autonomously perform real-time load scheduling of dc platform supply vessel (PSV) with an objective to minimize specific fuel oil consumption (SFOC) for better fuel efficiency. Focus has been on the modeling of various components and control routines, which are envisaged to be an integral part of dc PSVs. Integration with photovoltaic-based energy storage system (ESS) has been considered as an option to cater for the short time load transients. In this context, this paper proposes a real-time transient simulation scheme, which comprises of optimized generation scheduling of generators and ESS using dc optimal power flow algorithm. This framework considers real dynamics of dc PSV during various marine operations with possible contingency scenarios, such as outage of generation systems, abrupt load changes, and unavailability of ESS. The proposed modeling and control routines with real-time transient simulation scheme have been validated utilizing the real-time marine simulation platform. The results indicate that the coordinated treatment of renewable based ESS with DGs operating with optimized speed yields better fuel savings. This has been observed in improved SFOC operating trajectory for critical marine missions. Furthermore, SFOC minimization at multiple suboptimal points with its treatment in the real-time marine system is also highlighted

On the identifiability, parameter identification and fault diagnosis of induction machines

PhD ThesisDue to their reliability and low cost, induction machines have been widely utilized in a large variety of industrial applications. Although these machines are rugged and reliable, they are subjected to various stresses that might result in some unavoidable parameter changes and modes of failures. A common practice in induction machine parameter identification and fault diagnosis techniques is to employ a machine model and use the external measurements of voltage, current, speed, and/or torque in model solution. With this approach, it might be possible to get an infinite number of mathematical solutions representing the machine parameters, depending on the employed machine model. It is therefore crucial to investigate such possibility of obtaining incorrect parameter sets, i.e. to test the identifiability of the model before being used for parameter identification and fault diagnosis purposes. This project focuses on the identifiability of induction machine models and their use in parameter identification and fault diagnosis. Two commonly used steady-states induction machine models namely T-model and inverse Γ- model have been considered in this thesis. The classical transfer function and bond graph identifiability analysis approaches, which have been previously employed for the T-model, are applied in this thesis to investigate the identifiability of the inverse Γ-model. A novel algorithm, the Alternating Conditional Expectation, is employed here for the first time to study the identifiability of both the T- and inverse Γ-models of the induction machine. The results obtained from the proposed algorithm show that the parameters of the commonly utilised Tmodel are non-identifiable while those of the inverse Γ-model are uniquely identifiable when using external measurements. The identifiability analysis results are experimentally verified by the particle swarm optimization and Levenberg-Marquardt model-based parameter identification approaches developed in this thesis. To overcome the non-identifiability problem of the T-model, a new technique for induction machine parameter estimation from external measurements based on a combination of the induction machine’s T- and inverse Γ-models is proposed. Results for both supply-fed and inverter-fed operations show the success of the technique in identifying the parameters of the machine using only readily available measurements of steady-state machine current, voltage and speed, without the need for extra hardware. ii A diagnosis scheme to detect stator winding faults in induction machines is also proposed in this thesis. The scheme uses time domain features derived from 3-phase stator currents in conjunction with particle swarm optimization algorithm to check characteristic parameters of the machine and detect the fault accordingly. The validity and effectiveness of the proposed technique has been evaluated for different common faults including interturn short-circuit, stator winding asymmetry (increased resistance in one or more stator phases) and combined faults, i.e. a mixture of stator winding asymmetry and interturn short-circuit. Results show the accuracy of the proposed technique and it is ability to detect the presence of the fault and provide information about its type and location. Extensive simulations using Matlab/SIMULINK and experimental tests have been carried out to verify the identifiability analysis and show the effectiveness of the proposed parameter identification and fault diagnoses schemes. The constructed test rig includes a 1.1 kW threephase test induction machine coupled to a dynamometer loading unit and driven by a variable frequency inverter that allows operation at different speeds. All the experiment analyses provided in the thesis are based on terminal voltages, stator currents and rotor speed that are usually measured and used in machine control.Libya, through the Engineering Faculty of Misurata- Misurata Universit

Development of new methods for nonintrusive induction motor energy efficiency estimation

Induction motors (IMs) are the most widely used motors in industries. They constitute about 70% of the total motors used in industries and are the largest energy consumers in industrial applications. As a result of the increasing need for energy savings and demand-side management, the development of methods for accurate energy efficiency estimation has become a crucial area of research. While several methods have been proposed for induction motor efficiency determination, majority of the methods cannot be easily applied in the field owing to the intrusive nature of the test procedures involved. This PhD work presents some novel methods for nonintrusive efficiency estimation of induction motors operating on-site using limited motor terminal measurements and nameplate data. The first method is developed for induction motors operating on sinusoidal supply source (line-fed). The method uses a modified inverse Г-model equivalent circuit with series core loss arrangement to mitigate the inherent problems of higher computational burden and parameter redundancy associated with the conventional equivalent circuit method. Furthermore, a new method is presented for estimating the friction and windage loss using the airgap torque and motor nameplate data. The proposed Nonintrusive Field Efficiency Estimation (NFEE) technique was validated experimentally on four different induction motors for both balanced and unbalanced voltage supply conditions. The results demonstrate the accuracy of the proposed NFEE method and confirm its advantage over the conventional equivalent circuit method. In addition to the problem of unbalanced voltage supply, the presence of harmonics significantly affects the operation of induction motors. The second novel approach for estimating efficiency proposed in this PhD work extends the NFEE method to cover for non-sinusoidal supply condition. The method considers the variation of core loss, rotor bar resistance and leakage inductance due to time harmonics and skin effects. Finally, the efficiency estimations are compared to the IEC/TS 60034-2-3 in the case of a balanced non-sinusoidal supply condition. This allows not only the efficiency comparison but also the loss segregation analysis on the various components of the motor losses. In the case of an unbalanced supply, the efficiency results are compared to measured values obtained based on the direct input-output method. In both the first and second methods, a robust Chicken Swarm Optimization (CSO) algorithm has been used for the first time in conjunction with a simplified inverse Г-model EC to correctly determine the induction motor parameters and hence its losses and efficiency while inservice. As Variable Frequency Drives (VFDs) continue to dominate industrial process control, there is a need for stakeholders to quantify the converter-fed motor losses over a wide range of operating frequency and loading conditions. Although there is an increase in legislative activities, particularly in Europe, towards the classification and improvement of energy efficiency in electric drive systems, the handful of available standards for quantifying the harmonic losses are still undergoing validation. One of such standards is the IEC/TS 60034-2-3, which has been lauded as a step in the right direction. However, its limitation to rated motor frequency has been identified as one of its main weaknesses. Therefore, the third method proposed in this research demonstrates how the IEC/TS 60034-2-3 loss segregation methodology at nominal frequency can be extended over the constant-torque region of an induction motor (IM). The methodology has been validated by testing two motors using a 2-level voltage source inverter (VSI) in an open-loop V/F control mode. The results provide good feedback to the relevant IEC standards committee as well as guidance to stakeholders

Permanent Magnet Vernier Machine: A Review

Permanent magnet vernier machines (PMVMs) gained a lot of interest over the past couple of decades. This is mainly due to their high torque density enabled by the magnetic gearing effect. This study will provide a thorough review of recent advances in PMVMs. This review will cover the principle of operation and nature of magnetic gearing in PMVMs, and a better understanding of novel PMVM topologies using different winding configuration as well as different modulation poles and rotor structures. Detailed discussions on the choice of gear ratio, slot-pole combinations, design optimisation and role of advanced materials in PMVMs will be presented. This will provide an update on the current state-of-the art as well as future areas of research. Furthermore, the power factor issue, fault tolerance as well as cost reduction will be discussed highlighting the gap between the current state-of-the art and what is needed in practical applications

LogBase: A Scalable Log-structured Database System in the Cloud

Numerous applications such as financial transactions (e.g., stock trading) are write-heavy in nature. The shift from reads to writes in web applications has also been accelerating in recent years. Write-ahead-logging is a common approach for providing recovery capability while improving performance in most storage systems. However, the separation of log and application data incurs write overheads observed in write-heavy environments and hence adversely affects the write throughput and recovery time in the system. In this paper, we introduce LogBase - a scalable log-structured database system that adopts log-only storage for removing the write bottleneck and supporting fast system recovery. LogBase is designed to be dynamically deployed on commodity clusters to take advantage of elastic scaling property of cloud environments. LogBase provides in-memory multiversion indexes for supporting efficient access to data maintained in the log. LogBase also supports transactions that bundle read and write operations spanning across multiple records. We implemented the proposed system and compared it with HBase and a disk-based log-structured record-oriented system modeled after RAMCloud. The experimental results show that LogBase is able to provide sustained write throughput, efficient data access out of the cache, and effective system recovery.Comment: VLDB201

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

Some aspects of high-torque, low-speed, brushless electric motors

Imperial Users onl

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks