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

Power efficient and power attacks resistant system design and analysis using aggressive scaling with timing speculation

Growing usage of smart and portable electronic devices demands embedded system designers to provide solutions with better performance and reduced power consumption. Due to the new development of IoT and embedded systems usage, not only power and performance of these devices but also security of them is becoming an important design constraint. In this work, a novel aggressive scaling based on timing speculation is proposed to overcome the drawbacks of traditional DVFS and provide security from power analysis attacks at the same time. Dynamic voltage and frequency scaling (DVFS) is proven to be the most suitable technique for power efficiency in processor designs. Due to its promising benefits, the technique is still getting researchers attention to trade off power and performance of modern processor designs. The issues of traditional DVFS are: 1) Due to its pre-calculated operating points, the system is not able to suit to modern process variations. 2) Since Process Voltage and Temperature (PVT) variations are not considered, large timing margins are added to guarantee a safe operation in the presence of variations. The research work presented here addresses these issues by employing aggressive scaling mechanisms to achieve more power savings with increased performance. This approach uses in-situ timing error monitoring and recovering mechanisms to reduce extra timing margins and to account for process variations. A novel timing error detection and correction mechanism, to achieve more power savings or high performance, is presented. This novel technique has also been shown to improve security of processors against differential power analysis attacks technique. Differential power analysis attacks can extract secret information from embedded systems without knowing much details about the internal architecture of the device. Simulated and experimental data show that the novel technique can provide a performance improvement of 24% or power savings of 44% while occupying less area and power overhead. Overall, the proposed aggressive scaling technique provides an improvement in power consumption and performance while increasing the security of processors from power analysis attacks.N/

ASSESSING AND IMPROVING THE RELIABILITY AND SECURITY OF CIRCUITS AFFECTED BY NATURAL AND INTENTIONAL FAULTS

The reliability and security vulnerability of modern electronic systems have emerged as concerns due to the increasing natural and intentional interferences. Radiation of high-energy charged particles generated from space environment or packaging materials on the substrate of integrated circuits results in natural faults. As the technology scales down, factors such as critical charge, voltage supply, and frequency change tremendously that increase the sensitivity of integrated circuits to natural faults even for systems operating at sea level. An attacker is able to simulate the impact of natural faults and compromise the circuit or cause denial of service. Therefore, instead of utilizing different approaches to counteract the effect of natural and intentional faults, a unified countermeasure is introduced. The unified countermeasure thwarts the impact of both reliability and security threats without paying the price of more area overhead, power consumption, and required time. This thesis first proposes a systematic analysis method to assess the probability of natural faults propagating the circuit and eventually being latched. The second part of this work focuses on the methods to thwart the impact of intentional faults in cryptosystems. We exploit a power-based side-channel analysis method to analyze the effect of the existing fault detection methods for natural faults on fault attack. Countermeasures for different security threats on cryptosystems are investigated separately. Furthermore, a new micro-architecture is proposed to thwart the combination of fault attacks and side-channel attacks, reducing the fault bypass rate and slowing down the key retrieval speed. The third contribution of this thesis is a unified countermeasure to thwart the impact of both natural faults and attacks. The unified countermeasure utilizes dynamically alternated multiple generator polynomials for the cyclic redundancy check (CRC) codec to resist the reverse engineering attack

Data-driven methods for real-time voltage stability assessment

Voltage instability is a phenomenon that limits the operation and the transmission capacity of a power system. An operation state close to the security limits enables a cost-effective utilization of the system but it could also make the system more vulnerable to disturbances. The transition towards a more sustainable energy system, with a growing share of renewable generation, will increase the complexity in voltage stability assessment and cause significant planning and operational challenges for transmission system operators.The overall aim of this thesis is to develop a real-time voltage stability assessment tool which can be used to assist transmission system operators in monitoring voltage security limits and to provide early warnings of possible voltage instability. The thesis first analyzes the difference between static and dynamic voltage security margins, both theoretically and numerically. The results of the analysis show that power systems with a high share of loads with fast restoration dynamics, such as induction motors or power electronic controlled loads, may cause conventional static methods to assess the voltage security margins to become unreliable. Methods relying on a dynamic assessment of the security margin are in these circumstances more reliable. However, dynamic assessment of voltage security margins is computationally challenging and can in most cases not be estimated in the time frame required by system operators in critical situations. To overcome this challenge, a machine learning-based method for fast and robust computing of the dynamic voltage security margin is proposed and tested in this thesis. The method, based on artificial neural networks, can provide real-time estimations of voltage security margins, which are then validated using a search algorithm and actual time-domain simulations. The two-step approach is proposed to mitigate any inconsistency issues associated with neural networks under new or unseen operating conditions. Finally, a new method for voltage instability prediction is developed. The method is proposed to be used as an online tool for system operators to predict the system’s near-future stability condition given the current operating state. The method uses a more advanced neural network based on long-short term memory. The results from case studies using the Nordic 32 test system show good performance and the network can accurately, within only a few seconds, predict voltage instability events in almost all test cases

Toward Green Vehicles Digitalization for the Next Generation of Connected and Electrified Transport Systems

This survey paper reviews recent trends in green vehicle electrification and digitalization, as part of a special section on “Energy Storage Systems and Power Conversion Electronics for E-Transportation and Smart Grid”, led by the authors. First, the energy demand and emissions of electric vehicles (EVs) are reviewed, including the analysis of the trends of battery technology and of the recharging issues considering the characteristics of the power grid. Solutions to integrate EV electricity demand in power grids are also proposed. Integrated electric/electronic (E/E) architectures for hybrid EVs (HEVs) and full EVs are discussed, detailing innovations emerging for all components (power converters, electric machines, batteries, and battery-management-systems). 48 V HEVs are emerging as the most promising solution for the short-term electrification of current vehicles based on internal combustion engines. The increased digitalization and connectivity of electrified cars is posing cyber-security issues that are discussed in detail, together with some countermeasures to mitigate them, thus tracing the path for future on-board computing and control platforms.publishedVersio

Side Channel Attack on Low Power FPGA Platform

In today's advanced electronic age, people have become accustomed to using electronic devices to store and process their information. There is a general belief that the information is safe, due to the use of mathematically proven cryptographic systems in critical devices. However, in recent years, various side channel attacks have been used to break the security of systems that were thought to be completely safe. Side channel attacks are based on information gained through the physical implementation of a cryptosystem, rather than its mathematical construction. In this thesis work, an investigation is carried out to examine the susceptibility of the Hash-based Message Authentication Code standard based on the Secure Hash Algorithm (HMAC-SHA256) cryptosystem to a known correlation power analysis attack. For the purpose of this investigation, the cryptosystem was implemented on a low power Xilinx Field-Programmable Gate Array (FPGA) on the Side Channel Attack Standard Evaluation Board (SASEBO) platform. A secondary objective of the research work was to explore whether the SASEBO platform used may be easily modified to run side channel attacks on different cryptosystems. Four different side channel attacks were carried out on the HMAC-SHA256 implementation on the Xilinx Virtex-5 FPGA; two were based on power consumption measurements and two on electromagnetic (EM) emanation above the FPGA chip. This thesis has shown that SAESBO platform can be used as a testbed for examining the power side channel analysis of different cryptosystems with a small percentage of FPGA overhead. Although the EM emanations from SAESBO are not viable for side channel analysis, power from the on-chip core can be utilized. In addition the previously researched carry-propagate and pre-averaging techniques have been verified and found to be useful on this low power FPGA chip, requiring approximately 43776 traces for the guess of the correct secret intermediate values to reach among the top 5 ranked guesses

Microgrid Reliability Evaluation Based on Condition-Dependent Failure Models of Power Electronic Devices

Microgrid, a distributed energy system consisting of distributed energy and loads, aims to ensure reliable and affordable energy security in urban and rural communities. With the growing global energy need and the emerging threat of climate change, green renewable energy is becoming a new favorite in the field of power generation. Microgrids have received wide spread attention and application for their minimizing carbon dioxide and greenhouse gas emissions. Microgrids do so by maximizing clean local energy generation as well as reducing the stress of the transmission and distribution system. With the use of renewable energy, the reliability performance of microgrids becomes an issue because many renewable energy sources are intermittent. As power electronics increasingly serve as interfaces for renewable energy integration in microgrids, the reliable performance of power electronics plays an important role in microgrid reliability. In recent years, although the reliability of microgrid and power electronics has been studied, most of the research was limited to the reliability evaluation on the long-term planning timescale. However, the operational reliability evaluation considering power electronic influence was rarely studied. Power electronic devices such as converters are important parts of the microgrid system. The constant failure rate of converters has been widely used in power system planning. It has proved to be useful for calculating long-term or medium-term average reliability indices. However, under different operating conditions, the average failure rate cannot fully represent the failure rate of the component. In this thesis, a converter real-time failure model in different micro-sources was built and tested. These models were applied to an 11-node microgrid test system to calculate the operating reliability indices under different situations. Then, the sensitivity analysis was carried out, and the influence of various factors was evaluated. The converter real-time failure model is built based on the power losses of power electronics caused by the variation of the weather data (wind speed, ambient temperature, and illumination). To calculate the availability for test systems, systems were simplified to several sub-systems according to the power flow. By using the reliability block diagram (RBD) method and combining with the real-time availability of the converter, each sub-system’s hourly availability was calculated. In the simulation, the system availability considering the influence of power electronics was calculated and a comparison was made with the system availability without considering the influence of power electronics. To calculate operational reliability indices, a short-term outage model was applied and varied cases were used to test the influencing factor. The sensitivity analyses demonstrated the influence of seasons, wind turbine parameters and meteorological conditions. According to the simulation results, the reliability performance of the system can be more accurately reflected by these condition-dependent models. The ambient temperature is the main affecting factor for wind turbines and the illumination is the most important factor for photovoltaic arrays. The availability of subsystems varies significantly due to the different operating environments. The studies also indicated that the number and type of micro-sources in microgrid have great influence on the reliability indices of the overall system. The results of one year\u27s simulation illustrate that the reliability of the system has a certain seasonality. And it also shows the dependence of operational reliability on factors such as wind turbine parameters, system topology as well as local meteorological conditions