Implementation of a Cascade Fault Tolerant Control and Fault Diagnosis Design for a Modular Power Supply

The main objective of this research work was to develop reliable and intelligent power sources for the future. To achieve this objective, a modular stand-alone solar energy-based direct current (DC) power supply was designed and implemented. The converter topology used is a two-stage interleaved boost converter, which is monitored in closed loop. The diagnosis method is based on analytic redundancy relations (ARRs) deduced from the bond graph (BG) model, which can be used to detect the failures of power switches, sensors, and discrete components such as the output capacitor. The proposed supervision scheme including a passive fault-tolerant cascade proportional integral sliding mode control (PI-SMC) for the two-stage boost converter connected to a solar panel is suitable for real applications. Most model-based diagnosis approaches for power converters typically deal with open circuit and short circuit faults, but the proposed method offers the advantage of detecting the failures of other vital components. Practical experiments on a newly designed and constructed prototype, along with simulations under PSIM software, confirm the efficiency of the control scheme and the successful recovery of a faulty stage by manual isolation. In future work, the automation of this reconfiguration task could be based on the successful simulation results of the diagnosis method.This research was funded by the Tunisian Ministry of Higher Education and Scientific Research

Multi-domain Modelling in DESTECS and Ptolemy - a Tool Comparison

Developing embedded systems with high performance and safety requirements is notoriously hard. It is not enough to have a thorough understanding of the control algorithms used, but a deep understanding of the monitored and controlled physical environment is required to ensure that performance and safety requirements are met. Various tools deal with modeling such multi-domain systems and provide evaluation through simulation. Two such tools — DESTECS and Ptolemy — are examined and compared in this paper, using a case study of an aircraft fuel system. Usability, quantitative, and qualitative comparison criteria are used to give a thorough analysis of the capabilities of the two tools. The contribution of this paper is a description of pros and cons of each tool, helping future users to choose the right tool that suits their needs

Modelling polymer electrolyte membrane fuel cells for dynamic reliability assessment

Tackling climate change is arguably the biggest challenge humanity faces in the 21st century. Rising average global temperatures threaten to destabilize the fragile ecosystem of the Earth and bring unprecedented changes to human lives if nothing is done to prevent it. This phenomenon is caused by the anthropogenic greenhouse effect due to the increasing atmospheric concentrations of carbon dioxide (CO2). One way to avert the disaster is to drastically reduce the consumption of fossil fuels in all spheres of human activities, including transportation. To do this, research and development of electric vehicles (EVs) to make them more efficient, reliable and accessible is essential. [Continues.

Research and development of diagnostic algorithms to support fault accommodating control for emerging shipboard power system architectures

The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS.The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS

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

Evaluation of reliability modeling tools for advanced fault tolerant systems

The Computer Aided Reliability Estimation (CARE III) and Automated Reliability Interactice Estimation System (ARIES 82) reliability tools for application to advanced fault tolerance aerospace systems were evaluated. To determine reliability modeling requirements, the evaluation focused on the Draper Laboratories' Advanced Information Processing System (AIPS) architecture as an example architecture for fault tolerance aerospace systems. Advantages and limitations were identified for each reliability evaluation tool. The CARE III program was designed primarily for analyzing ultrareliable flight control systems. The ARIES 82 program's primary use was to support university research and teaching. Both CARE III and ARIES 82 were not suited for determining the reliability of complex nodal networks of the type used to interconnect processing sites in the AIPS architecture. It was concluded that ARIES was not suitable for modeling advanced fault tolerant systems. It was further concluded that subject to some limitations (the difficulty in modeling systems with unpowered spare modules, systems where equipment maintenance must be considered, systems where failure depends on the sequence in which faults occurred, and systems where multiple faults greater than a double near coincident faults must be considered), CARE III is best suited for evaluating the reliability of advanced tolerant systems for air transport

Innovation in Energy Systems

It has been a little over a century since the inception of interconnected networks and little has changed in the way that they are operated. Demand-supply balance methods, protection schemes, business models for electric power companies, and future development considerations have remained the same until very recently. Distributed generators, storage devices, and electric vehicles have become widespread and disrupted century-old bulk generation - bulk transmission operation. Distribution networks are no longer passive networks and now contribute to power generation. Old billing and energy trading schemes cannot accommodate this change and need revision. Furthermore, bidirectional power flow is an unprecedented phenomenon in distribution networks and traditional protection schemes require a thorough fix for proper operation. This book aims to cover new technologies, methods, and approaches developed to meet the needs of this changing field

Battery SMART charge controller/combined co-gen grid connected inverter design and simulation

Electricity generation and distribution is undergoing significant change under the influences of energy security, climate change, technological development, and economics. Technologies that have introduced two-way power flow onto a distribution grid that was designed for one-way power flow are creating challenges and opportunities for innovation in the electricity distribution sector. These technologies include solar photovoltaics (PV), wind turbines, and battery energy storage systems (BESS). As the newest technology, BESS present opportunities to both the electricity distribution network service provider (DNSP) and the consumer. This dissertation focused primarily on the consumer side of the switchboard, modelling and analysing the economics and some of the technical issues for an economic-mediated battery controller as part of a grid-tied residential hybrid renewable energy system (HRES) that consists of a BESS, 1 kW wind turbine, and 10 kW PV array. The geographical context of this project is Nambour, Queensland; PV and wind power calculations were based on Nambour’s meteorological history. Residential energy consumption was modelled as a ‘typical’ Nambour residential customer. The technological context was such that costs and choices applied at mid-2016. The tariff context used was the recently introduced TOU tariff 12, which played a significant role in the timing and logic development of the battery charge controller algorithm. From a technical standpoint, the charge controller algorithm was a major achievement of the present work. In developing the algorithm, it was found that the use of data from individual system components could be used to formulate the optimum mix of power sourced from or sunk to both the grid and the BESS. The output of this formulation was then demonstrated as a data input used for the control of the switching patterns of the BESS power electronics, a two-quadrant DC-DC converter (chopper). The other major achievement of the current work was the finding that although BESS economics continue to improve, they generally still need to achieve further cost reductions in order to realise economic feasibility for the modelled context. It was also found that economic feasibility is more likely to be reached more quickly under conditions of high energy consumption, high inflation, high peak TOU tariff, and low discount rate

Control Strategies for Improving Reliability and Efficiency in Modular Power Converters

The significance of modular power converters has escalated drastically in various applications such as electrical energy distribution, industrial motor drives and More Electric Aircraft (MEA) owing to the benefits such as scalability, design flexibility, higher degree of fault tolerance and better maintenance. One of the main advantages of modular systems is the ability to replace the faulty converter cells during maintenance instead of the entire system. However, such maintenance cycles can result in a system of converter cells with different aging. A system with cells having different aging arises the threats of multiple maintenance, lower reliability and availability, and high maintenance costs. For controlling the thermal-stress based aging of modular power converters, power routing strategy was proposed. The thesis focuses on the different implementation strategies of power routing for modular converters. Power semiconductors are one of the most reliability critical components in power converters, and thermal-stress has been identified as the main cause of their failure. This thesis work concentrates on the power semiconductor reliability improvement algorithms. For improving system lifetime, virtual resistor based power routing algorithms for single stage and multi-stage modular architectures have been investigated through simulations and validated with experiment. A unified framework for routing the power in complex modular converter architectures is defined based on graph theory. Popular converter architectures for Smart Transformer (ST) and MEA applications are modeled as graphs to serve as the basis for developing power flow optimization. The effectiveness of graph theory for optimizing the power flow in modular systems is demonstrated with the help of proposed algorithms