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(2020)ThesisElectrical insulation of high voltage (HV) power equipment plays essential roles in sound functioning and reliability of power systems. Examining the insulation condition through various diagnostic testings such as dielectric response (DR) and partial discharge (PD) measurements may be able to reveal the presence of defects and degradations in the insulation. Very low frequency (VLF- 1 Hz or lower) applied voltage has emerged as a promising diagnostic tool as it significantly reduces the required reactive power from the test supply. However, the existing interpretation knowledge at conventional power frequency (PF) 50 Hz cannot be directly applied to understand test results in the VLF range. This is the main motivation of the research which explores the dielectric behaviours and associated physical processes under VLF excitation. For dielectric response, experimental studies were carried out on short sections of medium voltage service-aged cross-linked polyethylene (XLPE) cables to diagnose the bulk insulation condition, such as the measurement of dissipation factor, polarisation and depolarisation current, frequency domain spectroscopy, activation energy etc. Experimental results show that dielectric behaviours of electric insulation are influenced by several factors including the excitation frequency, voltage amplitude, ambient temperature, dipolar processes (e.g. conduction and polarisation) etc. An empirical physical model describing the loss-factor measurement based on well-known dipolar theories is developed and verified by experimental results. Different partial discharge processes (e.g. cavity, surface and corona) are also investigated at both VLF (0.1 Hz) and 50 Hz applied voltage. Measurement results are presented with the phase-resolved image and show that discharge characteristics (inception voltage, magnitude and repetition rate) are strongly dependent on the applied frequency. Based on the finite element analysis (FEA) method, a dynamic model to simulate the discharge behaviours in the cavity or on the degraded surface is developed to examine the frequency dependence. The main contributions of this research include the measurement and modelling of both the dielectric response and partial discharge in electrical insulation. The research findings provide valuable information to understand the diagnostic characteristics at very low frequency excitation.
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(2020)ThesisWith the rapid growth of distributed energy resources (DERs), the power system structure has gradually become more decentralised. This shift has encouraged customers to participate in the distribution market transactions and to break the monopoly of suppliers - they can now choose between the services of conventional retailers or become an active participant in the distribution energy market (DEM). DERs can provide customers with the ability to reduce the impact of system outages caused by extreme weather events, vandalism, and faults in operational equipment. Also, the DEM needs to transition away from the provision of uniformed services to allow customers to choose energy providers flexibility. Given this context, this thesis focuses on developing an optimal trading mechanism (OTM) that incorporates a customised reliability service, in which customers can choose how they balance the electricity price they prepare to pay for electricity and their required reliability of supply. Customers are classified by a dynamic elasticity coefficient that considers the historical data of customer information and usages and incorporating real-time preferences as parameters. The OTM enables customers to choose bids based on the curtailed price and trading quantities considering their reliability requirements. A novel double-sided auction has been applied in order to obtain a floated average clearing price that reflects the different levels of reliability. Mathematical modelling and simulations have proven the OTM to satisfy the incentive compatibility. The forecasting models for load and generation output have been developed and utilised for the risk assessment model (RAM) as predicted real-time utility. The RAM II considers weather, state of equipment and outages caused by accidental events -such as vandalism, bird strikes, etc, - which may impact the distribution side and produces a set of advised interval prices from which customers can choose. During grid failures, the OTM can satisfy the reliability requirements of most consumers, whilst prosumers can obtain higher economic benefits than they would with other distribution market mechanisms. These benefits effectively motivate customers to participate in the distribution market, which in turn contributes to the deregulation of the electricity market and improves the resilience of the main grid.
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(2020)ThesisWidespread use of heterogeneous Internet of Things (IoT) devices has led to an escalation in sophisticated cyber-attacks, putting private data, personal safety, and critical infrastructures at heightened risk. In order to combat these escalating threats, standards bodies, governments, and industries are pushing IoT vendors to develop formal behavioral specifications of their IoT devices so that unintended usage can be detected and blocked. However, IoT behavioral models are still at an early stage of maturity, with researchers and practitioners still grappling with how to generate, verify, and enforce behavioral specifications in operational networks, as well as detecting anomalous behaviors indicative of cyber-attacks. This thesis develops systematic methods to generate and validate behavioral profiles, monitor and enforce them in the network using Software-Defined Networking (SDN) principles, and detect and thwart intrusions with high precision. This thesis is structured into four parts. First, we review IoT stakeholders’ perspectives on security, network threats, attack vectors, and critique existing countermeasures and their limitations. Based on this review, we argue that robust and verifiable IoT network security deserves a research study. Second, we develop tools to assist IoT manufacturers in generating and verifying behavioral profiles, highlighting insights and challenges encountered in the process. Using this tool, we generated behavioral profiles of 28 consumer IoT devices and released them publicly. We then help the users of IoT devices in tracking their network behavior using the behavioral profile, by translating behavioral policies into flow rules enforced using SDN principles. Third, we develop a method to monitor the behavior of each IoT device at run-time, compare it to the expected profile, and identify any anomalous behavior indicative of volumetric attacks using multi-level machine learning models with over 98% accuracy. Finally, we enhance our models to large-scale IoT systems by formally capturing the interactions of the IoT devices within a deployment environment such as a building, thereby providing comprehensive security monitoring of large infrastructure. We applied our technique to a subset of our university smart campus network, covering three types of IoT devices deployed in seven different buildings and demonstrate how our scheme detects attacks to achieve both static and dynamic cybersecurity.
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(2021)ThesisRural electrification demands the use of inexpensive technologies such as single wire earth return (SWER) networks. There is a steadily growing energy demand from remote consumers, and the capacity of existing lines may become inadequate soon. Besides, the existing SWER networks are very inefficient and experience poor voltage regulation. Furthermore, high-impedance arcing faults (HIF) from SWER lines can ignite bushfires such as the catastrophic 2009 Black Saturday bushfires in Victoria (Australia). Replacing SWER lines by cables as recommended by the Victorian Royal Commission comes at an astronomical cost and service providers are not able to comply with. As a solution, reliable remote electricity networks can be established through splitting the existing system into microgrids, and existing SWER lines can be utilised to interconnect those microgrids. The development of such reliable networks with better energy demand management will rely on having an integrated network-wide condition monitoring system. As the first contribution of this thesis, a distributed online monitoring platform is developed that incorporates power quality monitoring, real-time HIF identification and transient classification in SWER network. Characteristic features are extracted from the current and voltage signals, and Artificial Intelligence (AI) based classification techniques are developed to classify the faults and transients. The proposed approach demonstrates higher HIF detection accuracy (98.67%) and reduced detection latency (115.2 ms). Secondly, to facilitate electricity demand management, a remote consumer load identification methodology is developed to detect the load type from its turn-on transients. An edge computing-based architecture is proposed to facilitate the high-frequency analysis for load identification with minimum data transmission. Computationally efficient load identification methodologies are developed to enable their real-time deployment on resource constrained devices. The proposed approach is evaluated in real-time, and it achieves an average accuracy of 98% in identifying different loads. Finally, a deep neural network-based energy disaggregation framework is developed to separate the load specific energy usage from an aggregated signal. A generative approach is applied to model energy usage patterns. The proposed framework is evaluated using a real-world data set. It improves the signal aggregate error by 44% and mean aggregate error by 19% in comparison with other state-of-the-art techniques.
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(2021)ThesisIn recent years, electric power generation using renewable energy sources has experienced an exponential growth in the world energy market. Their unprecedented large scale penetration foresees a 100% renewable based power generation in near future. These sources require power electronic converters at various levels for power conversion and grid integration. The converters are fast acting devices and use advance control techniques in a hierarchical manner. With the retirement of the conventional synchronous generators, the modern power electronics based power system lacks the inertia property. Hence, they are more vulnerable to several grid transient events; for instance grid faults. Control functionalities classify these converters as grid-forming and grid-following. Both these types can act as grid supporting devices during grid fault. In contrast to grid-forming type, grid-following type converters are mostly used to support the grid. The act of supporting the grid with reactive power instead of tripping during a fault for a pre-defined duration is known as fault ride-through. These converters rely on a separate synchronization unit to inject grid current during both normal and fault condition. Recent grid fault events across the globe have revealed the inefficiency of such synchronization units. This is attributed to the delayed grid parameter estimation that eventually leads to the tripping of the converters rather ride-through. It indicates that the performance robustness of the synchronizing unit while considering the fault ride-through of converter needs to be further investigated thoroughly. In lieu of the above, accurate and fast grid voltage parameter estimation is essential for grid-connected converters. To achieve this objective, the contributions of this thesis are classified into two parts. The first part of the thesis deals with the fault detection for converters during a grid fault using digital signal processing (DSP) techniques. Faster fault detection is vital to safeguard the power converter as they have limited fault current carrying capacity. Hence a hybrid fault detection technique is proposed. The technique combines the features of two DSP techniques, Hilbert-Huang Transform (HHT) and Teager Energy Operator (TEO). This is called Teager-Huang in this thesis. With this proposed technique, several grid faults, balanced and unbalanced, in both the grid voltage magnitude and phase-angle jumps are detected. Further, comparisons of the fault energies are presented, which provides a benchmark for the severity of the grid faults. In the second part of the thesis, the synchronization aspect of the converter is investigated. For the purpose of analysis, the synchronization using the classical synchronous reference frame phase-locked loop (SRFPLL) is considered. Initially, the synchronization inefficiency of SRFPLL during the grid fault is explained in regards to the loss of synchronization (LOS) instability. It is shown that the cause for LOS during a fault may be initiated as results of very low grid voltage magnitude, high grid impedance or high current injection. The analysis emphasises on the occurrence of phase-angle jump (PAJ) during a fault. The thesis indicates that the conventional SRFPLL design parameters result in synchronization delay and insufficient damping to ride-through such PAJs. The decrease in the SRFPLL synchronization robustness highly affects the grid-connected converters. To enhance the grid synchronization performance during a grid fault with PAJ, a hybrid grid synchronization concept is proposed. It consists of both hybrid phase-angle estimators and hybrid frequency estimators. The hybrid frequency estimators contain several improved adaptive and PLL independent frequency estimation techniques. The proposed technique is designed to be compatible with both the three-phase and single-phase grid synchronization. To avoid voltage transients during the transition between the estimators, a transition scheme is presented. This is controlled based on the instantaneous phase-angle error measured by the estimators. The three-phase grid-connected converter is modelled using the proposed hybrid grid synchronization technique. The current controller of the converter is designed both in stationary and synchronous reference frame. Further, the fault ride-through (FRT) strategy is embedded in the converter controller. With the developed model, the FRT of the converter is tested during a fault. Both symmetrical and asymmetrical grid faults with PAJs are considered. The efficacy of the proposed technique is evaluated using both simulation and experimental validations. The last part of the thesis explores the FRT of single-phase power converters employing the proposed hybrid grid synchronization transition. The synchronization performance along with the current controller robustness during FRT is investigated.
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(2020)ThesisThe demand for high frequency, high power density and high efficiency on power converters has been increasing through the years. Solid state transformer (SST), which is typically formed by three stages, one being DC/AC inverter, which is controllable, one being medium-frequency transformer isolated converter and the other being AC/DC rectifier, has been developed quickly during past few decades to replace traditional 50Hz transformer. In SST, the use of planar transformer on print circuit boards (PCB) working at tens to hundreds of kilohertz can provide isolation and save space. In this thesis, research activities to develop a planar transformer isolated DC/DC converter for solid-state transformer are presented. To further increase efficiency, resonant converter with series LC and shunt LC filter is adopted to realize soft-switching, and to eliminate high order harmonics hence reducing unnecessary core loss and copper loss. For the designs of planar magnetics, PCB based litz wire air-core planar inductor and PCB based litz wire planar transformer are proposed, analyzed, simulated and tested, which shows better performance than traditional solid rectangular winding ones. By applying the novel litz wire inductor and transformer in the proposed DC/DC converter prototype, high order harmonics are eliminated and the efficiency of 90% is reached from experimental results, which can be further developed by an integrated system.
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(2020)ThesisThe Internet of Things (IoT) is being hailed as the next wave revolutionizing our society. Smart homes, enterprises, and cities are increasingly being equipped with a plethora of IoTs, ranging from smart-lights to smoke alarms and security cameras. While IoT networks have the potential to benefit society and our lives, they create privacy and security challenges not seen with traditional IT networks. The unprecedented scale and heterogeneity of IoT devices make today's security measures inapplicable to IoT networks. Due to the lack of tools for real-time visibility into IoT network activity, operators of such smart environments are not often aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. This thesis is the culmination of our efforts to develop techniques to profile the network behavioral pattern of IoTs, automate IoT identification and classification, deduce their operating context, and detect anomalous behavior indicative of cyber-attacks. We begin this thesis by surveying IoT market-segments, security risks, and stakeholder roles, while reviewing current approaches to vulnerability assessments, intrusion detection, and behavioral monitoring. For our first contribution, we collect traffic traces and characterize the network behavior of IoT devices via attributes such as activity cycles and signaling patterns. We develop a robust machine learning-based inference engine trained with these attributes and demonstrate real-time classification of 28 off-the-shelf IoT devices in the lab with over 99% accuracy. Our second contribution enhances the classification by reducing the cost of attribute extraction (via flow-level telemetry at multiple timescales) while also identifying IoT device states (bootup, user-interaction, and idle). Prototype implementation and evaluation demonstrate the ability of our supervised machine learning method to detect behavioral changes (including firmware updates) for five IoT devices. Our third and final contribution develops a modularized unsupervised inference engine that dynamically accommodates the addition of new IoT devices and/or updates to existing ones, without requiring system-wide retraining of the model. We demonstrate via experiments that our model can automatically detect attacks (e.g.: direct, spoofing, and reflection) and firmware changes in ten IoT devices with over 94% accuracy.
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(2021)ThesisAmong various physiological and behavioural traits, speech has gained popularity as an effective mode of biometric authentication. Even though they are gaining popularity, automatic speaker verification systems are vulnerable to malicious attacks, known as spoofing attacks. Among various types of spoofing attacks, replay attack poses the biggest threat due to its simplicity and effectiveness. This thesis investigates the importance of 1) improving front-end feature extraction via novel feature extraction techniques and 2) enhancing spectral components via adaptive front-end frameworks to improve replay attack detection. This thesis initially focuses on AM-FM modelling techniques and their use in replay attack detection. A novel method to extract the sub-band frequency modulation (FM) component using the spectral centroid of a signal is proposed, and its use as a potential acoustic feature is also discussed. Frequency Domain Linear Prediction (FDLP) is explored as a method to obtain the temporal envelope of a speech signal. The temporal envelope carries amplitude modulation (AM) information of speech resonances. Several features are extracted from the temporal envelope and the FDLP residual signal. These features are then evaluated for replay attack detection and shown to have significant capability in discriminating genuine and spoofed signals. Fusion of AM and FM-based features has shown that AM and FM carry complementary information that helps distinguish replayed signals from genuine ones. The importance of frequency band allocation when creating filter banks is studied as well to further advance the understanding of front-ends for replay attack detection. Mechanisms inspired by the human auditory system that makes the human ear an excellent spectrum analyser have been investigated and integrated into front-ends. Spatial differentiation, a mechanism that provides additional sharpening to auditory filters is one of them that is used in this work to improve the selectivity of the sub-band decomposition filters. Two features are extracted using the improved filter bank front-end: spectral envelope centroid magnitude (SECM) and spectral envelope centroid frequency (SECF). These are used to establish the positive effect of spatial differentiation on discriminating spoofed signals. Level-dependent filter tuning, which allows the ear to handle a large dynamic range, is integrated into the filter bank to further improve the front-end. This mechanism converts the filter bank into an adaptive one where the selectivity of the filters is varied based on the input signal energy. Experimental results show that this leads to improved spoofing detection performance. Finally, deep neural network (DNN) mechanisms are integrated into sub-band feature extraction to develop an adaptive front-end that adjusts its characteristics based on the sub-band signals. A DNN-based controller that takes sub-band FM components as input, is developed to adaptively control the selectivity and sensitivity of a parallel filter bank to enhance the artifacts that differentiate a replayed signal from a genuine signal. This work illustrates gradient-based optimization of a DNN-based controller using the feedback from a spoofing detection back-end classifier, thus training it to reduce spoofing detection error. The proposed framework has displayed a superior ability in identifying high-quality replayed signals compared to conventional non-adaptive frameworks. All techniques proposed in this thesis have been evaluated on well-established databases on replay attack detection and compared with state-of-the-art baseline systems.
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(2021)ThesisUnmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. Therefore, more research is still needed towards developing advanced reliable control strategies for UAVs to enable safe navigation in unknown and dynamic environments. This problem is even more challenging for multi-UAV systems where it is more efficient to utilize information shared among the networked vehicles. Therefore, the work presented in this thesis contributes towards the state-of-the-art in UAV control for safe autonomous navigation and motion coordination of multi-UAV systems. The first part of this thesis deals with single-UAV systems. Initially, a hybrid navigation framework is developed for autonomous mobile robots using a general 2D nonholonomic unicycle model that can be applied to different types of UAVs, ground vehicles and underwater vehicles considering only lateral motion. Then, the more complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed. To that end, advanced 3D reactive control strategies are developed adopting the sense-and-avoid paradigm to produce quick reactions around obstacles. A special case of navigation in 3D unknown confined environments (i.e. tunnel-like) is also addressed. General 3D kinematic models are considered in the design which makes these methods applicable to different UAV types in addition to underwater vehicles. Moreover, different implementation methods for these strategies with quadrotor-type UAVs are also investigated considering UAV dynamics in the control design. Practical experiments and simulations were carried out to analyze the performance of the developed methods. The second part of this thesis addresses safe navigation for multi-UAV systems. Distributed motion coordination methods of multi-UAV systems for flocking and 3D area coverage are developed. These methods offer good computational cost for large-scale systems. Simulations were performed to verify the performance of these methods considering systems with different sizes.
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(2021)ThesisPhotovoltaic (PV) energy is one of the most prominent renewable energy sources today. Traditionally, PV modules are connected in series to form a PV string, interfacing with PV inverter for grid connection. Since all PV modules are operating at the same current, the energy yield of the system is limited by the underperforming modules. PV optimizers, a concept which enhances maximum power point tracking (MPPT) of PV systems using power electronics, has been studied over the last few years. An emerging technique, submodule differential power processing (DPP) is proposed to improve the efficiency and enhance MPPT to a finer granularity. By diverting the differential current between submodules, DPP optimizers only process a fraction of the power the system produces, improving the overall efficiency. This thesis aims to research PV-to-PV DPP systems and its PV-to-Serial-Port variant, including modelling, control techniques, and MPPT strategies. The inverting buck-boost converter is the preferred topology for optimizers, as it is one of the simplest topologies which supports bidirectional power flow and both step-up and step-down voltage conversions. A small-signal model is derived as the basis of controller tuning involved in this thesis. A novel pairing between control inputs and outputs based on relative gain array analysis is proposed, mitigating coupling effects between optimizers, and simplifying the controller design. Furthermore, a double-loop outside-in exact MPPT strategy is presented, improving the tracking speed. Simultaneous voltage regulation of multiple submodules and exact MPPT strategy have been verified by experiments. Voltage equalization MPPT techniques are investigated as they eliminate the communication requirements between modules. Voltage equalization is investigated for both MPPT and flexible power point tracking (FPPT) applications, leading to the discovery of an unstable operation mode caused by linearization with the differential resistance method. Experiments validate that, with proposed tuning techniques closed-loop equalization is stable in regions where the power is sufficiently low to perform FPPT. A comparison of MPPT performance between open-loop and closed-loop equalizations shows that closed-loop equalization has better energy yield under severe mismatch, as it vastly reduces steady-state errors. Finally, a PV-to-Serial-Port variant, aiming to improve the practicality of DPP techniques is presented. With only one inter-module power connection and no communication requirements, this new architecture is more suitable for modular integration than its PV-to-PV counterpart. A novel topology of PV-to-Serial Port architecture which supports voltage equalization is proposed. A clear contribution that thoroughly analyses and compares the inductor sizing of practical PV-to-Serial-Port topologies considering arbitrary PV current mismatch is presented. It is validated by simulation that, with the combination of PV-to-Serial-Port and voltage equalization, MPPT can be performed at module-level autonomously and can handle sudden irradiance changes. A high-level comparison between systems in three core chapters is given in the conclusion chapter, outlining the techniques utilized in each chapter and their limitations. Experiments in this thesis are undertaken on two identical inverting buck-boost converter prototypes, rated at 100W, switching at 200kHz, and interfacing with a TI controlCARD. Control techniques and MPPT strategies are implemented digitally and can be applied to existing inverting buck-boost optimizers if power ratings and sensory requirements are met. The research outcome of this thesis improves the practicality of DPP techniques by simplifying control methods and eliminating communication requirements. Furthermore, it establishes the FPPT capability of DPP systems for providing grid support. Lastly, it provides a modular-integrable topology, enhancing the scalability of DPP systems.