Electrical and Electronic Engineering - Theses
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ItemTechno-economic modelling of distributed energy systems and energy communities( 2023-10)Power systems are experiencing an unprecedented transformation, driven by the massive uptake of distributed energy resources (DER) connected to distribution networks. This transformation challenges the traditional configuration of power systems, where large generators supplied energy to passive consumers, and gives rise to a decentralized setup, reshaping power system management and economics. Importantly, this entails that distribution networks, responsible for transporting and delivering electricity to customers, are undergoing a transformation into distributed energy systems, where electricity is consumed, generated, and also stored. As traditionally passive consumers adopt low-carbon technologies like PV systems and Battery Energy Storage Systems (BESS), they become active energy service providers, and increasingly seek opportunities to actively engage with the power system. In this context, energy communities have emerged with promising benefits for consumers, such as lower energy costs, reduced individual investments, and increased self-sufficiency of the community. However, as an emerging concept, the role and responsibilities of energy communities in power systems is unclear, and energy communities face many unanswered questions. Notably, it is yet to be defined if energy communities can balance community objectives, efficiently use the existing network and provide services to the power system, while being economically feasible. The challenges of energy communities are complex and multifaceted, requiring to consider technical, economic, regulatory and commercial aspects. Such comprehensive analysis of energy communities has only been presented in the existing literature qualitatively. This thesis sets out to provide a comprehensive quantitative study of energy communities, developing various techno-economic frameworks including regulatory and commercial aspects, to analyse the operation and investment problems in energy communities. The frameworks developed in this thesis are flexible and comprehensive, allowing to study diverse energy communities with various physical architectures and objectives; adopting various regulatory frameworks and commercial structures including different actors; co-optimizing participation in different markets, system-level and local services; and uncertainty in future local and system-level conditions. The versatility of the proposed frameworks allows to address the most fundamental issues of energy communities, such as if community-level DER provide benefits with respect to privately-owned behind-the-meter DER, as well as propose advancements that can be leveraged by energy communities and, more generally, distributed energy systems, such as the development of a pricing framework for distributed energy markets, and the operational impact of smart grid technologies. Through diverse case studies based on real energy community projects in Australia the potential of the frameworks developed is demonstrated, allowing to provide a novel analysis of energy communities. First, it was found that regulatory frameworks that consider energy communities as a single, independent entity with respect to the various markets and regulated costs results in economically feasible energy communities that also provide operational benefits to the system. Second, regulatory developments should be in place to incentivize distribution system operator engagement with the energy community, allowing energy communities to provide valuable network support services to efficiently manage the network operation, while allowing the energy community to accrue significant revenues. Due to the relevance of local network support, this issue was further explored. First by providing a novel pricing framework for distributed energy markets, sending adequate signals to DER that accurately value the benefits of local network support. Second, by studying the role of smart grid technologies on the provision of local services and their interactions with energy communities and DER.
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ItemVisible to Mid-Wave Infrared Photodetectors Based on Two-Dimensional Materials( 2023-12)The two-dimensional (2D) materials, such as graphene, are characterised as stable arrangements of atoms bonded via covalent or ionic bonds which form 2D planes (i.e. spanning the x and y direction). In their bulk crystal form, adjacent planes are held together via van der Waals forces (i.e. in the z direction). This is why sometimes they are referred to as the van der Waals materials. They have attracted tremendous attention in recent years as potential candidates for use in next-generation optoelectronic devices due to their tuneable bandgaps, high carrier mobility, high internal quantum yields, strong light-matter interaction, and mechanical flexibility. Among the various 2D materials, transition metal dichalcogenides (TMDCs), graphene (Gr), and black phosphorus (bP) have shown promising properties in the visible to mid-wave infrared (MWIR) spectral range as photodetectors. This is of great interest for applications such as visible/infrared imaging, optical communication, and spectrally sensitive detectors (such as those required for gas sensing). Compared with traditional three-dimensional (3D) compound semiconductor photodetectors, 2D material-based photodetectors have low densities of dangling bonds at their surfaces, strong light-matter interaction, and low thermal noise, which provides fundamental advantages in terms of signal-to-noise performance, particularly in infrared photodetection. In addition, the weak van der Waals forces holding planes together allow easy cleavage and assembly of 2D material heterojunctions as well as transferal onto established microelectronics platforms (such as silicon complementary metal-oxide-semiconductor chips). In this thesis, I present the design, fabrication, characterization, and modelling of photodetectors based on 2D materials in the vis to MWIR region. In the introduction chapters, I first discuss the basic concepts and principles of photodetection and 2D materials, and then review the state-of-the-art developments in this field. Following this I describe the experimental methods and techniques used in our work, including device fabrication, as well as materials and photodetector performance characterisation. In the first experimental chapter, I report our experimental results on the first demonstration of photodetectors based on ZrGeTe4, a new van der Waals material with a narrow bandgap in the SWIR region. I describe the device fabrication and characterisation of ZrGeTe4 based photodetectors and evaluate its performance as a photodetector under different conditions. I investigate the stability of ZrGeTe4 and prove that ZrGeTe4 is a promising candidate for stable, high-performance optoelectronic devices operating at room temperature in the SWIR region. The potential application of ZrGeTe4 as a position-sensitive lateral detector due to its asymmetric photocurrent. I demonstrate simple proof-of-concept broad spectrum photodetectors responsivities above 0.1 A W-1 across both the visible and short-wave infrared wavelengths. This corresponds to a specific detectivity of ~10 9 Jones at 1400 nm at room temperature. These devices show linearity in photoresponse over ~4 orders of magnitude and a fast response time of ~50 ns. As the first demonstration of photodetection using ZrGeTe4, these characteristics, measured on a simple proof-of-concept device without significant optimization, shows the exciting potential of ZrGeTe4 for room temperature IR optoelectronic applications. In the following chapter, I present a Fabry-Perot cavity enhanced bP MoS2 photodiode. I demonstrate the fabrication process and the optical structure of the device, which consists of a bP MoS2 heterojunction embedded in a Fabry-Perot cavity with two symmetrical dielectric/metal mirrors and claim that this device has promising potential for IR spectroscopic applications, such as gas sensing and imaging. This simple structure enables tunable narrow-band (down to 420 nm full-width-half-maximum) photodetection in the 2000 to 4000 nm range by adjusting the thickness of the Fabry-Perot cavity resonator. This is achieved whilst maintaining room temperature performance metrics comparable to previously reported 2D MWIR detectors. Zero bias specific detectivity and responsivity values of up to 1.7x10 9 Jones and 0.11 A W-1 at 3000 nm are measured, with a response time of less than 3 ns. These results introduce a promising family of 2D detectors with applications in MWIR spectroscopy. In the last experimental chapter, I fabricate a dual-gate pn junction photodiode by electrostatic doping. The hBN-bP-hBN dual-gate devices were fabricated and trialled for IR light collection/detection. It is shown that applying sufficiently large bias to one of the two rear gates, whilst holding the other at zero bias, leads to the formation of a lateral pn junction. Under IR illumination, this lateral pn junction exhibits the photovoltaic effect yielding a VOC as high as 175 mV and 74 mV, at 77 K and 295 K, respectively. These are the highest values reported for bP based dual-gate devices to date. When being used to detect light, under zero source-drain voltage, a specific detectivity of 8.5x10 8 and 2x10 7 Jones is measured at 77 K and 295 K, respectively. By modulating the back gate voltage, the dual-gate structure also allows switching between photoconductive and photovoltaic modes of operation. This allows a trade-off between low noise/fast response (photovoltaic mode) and high responsivity (photoconductive mode). Further, it is shown that the device can also be operated in a photoconductive mode of operation allowing a high responsivity of 0.55 A W-1 (VDS = -500 mV, 77 K). This development extends the application of dual-gate van der Waals materials photodetectors into the IR wavelength space. In the final chapter of this thesis, I summarise our main findings and contributions, and suggest some future directions and challenges for this research area.
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ItemNo Preview AvailableA Multifunctional Smart Field-Programmable Radio Frequency Surface( 2023-04)Wireless communication technology has completely transformed the way we communicate and access information. It operates on the principles of electromagnetic wave propagation, as described by Maxwell's equations. The scope of wireless communication is vast and includes satellite communication, handheld device communication, and Internet of Things (IoT), which have revolutionized fields of industry, healthcare, transportation, education, and entertainment. As demand for faster and more reliable communication continues to grow, a range of wireless communication standards has been developed, including WiFi, BLE, cellular networks, near-field communication (NFC), and ZigBee, each operating at a unique frequency range. Antennas that can operate across multiple communication standards have remained a challenge due to the interdependent factors of antenna geometry, size, and RF characteristics. As the number of devices with wireless connectivity increases dramatically, the spectrum resource is getting limited, which results in congestion and reduced performance. Reconfigurable antennas have been intensively studied in the last few decades to mitigate this challenge. Although reconfigurability in operating frequency, radiation pattern and polarization have been implemented, limitations including lack of programmability, pattern diversity, and self-adaptive capability against environmental interference exist. To address these limitations, we proposed a new concept called Field-programmable RF surface (FPRFS), which allows for the control of current flow on the surface to achieve desired antenna characteristics and impedance matching capabilities. This work starts with the theoretical analysis and creates the mathematical model for the FPRFS in antenna, impedance matching network, and filter applications. Our research demonstrated the reconfigurability of FPRFS antennas in operating frequency, radiation pattern, and polarization reconfigurability with radiation gain and efficiency levels that are comparable to those of conventional fixed patch antennas and enhanced immunity to surrounding obstacles. We developed a software algorithm for the FPRFS that enables it to automatically optimize its configuration in real-time, thereby adapting to changing load impedance or environmental interferences.
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ItemPolarization effects and high speed transmission in coherent optical OFDM systems(University of Melbourne, 2010)
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ItemSteady-state multi-energy flow problem in coupled energy networks( 2023-07)Energy systems worldwide are experiencing a rapid transition towards a low-carbon future, calling for greater energy efficiency and flexibility in utilising energy networks. This transition is accompanied by the growing adoption of multi-energy technologies like combined heat and power (CHP), power-to-heat, and power-to-gas units, leading to rising interdependencies between different energy networks, particularly in district and energy community applications. Hence, more effective utilisation of coupled energy networks (CEN) can bring several efficiency and flexibility benefits. Developing efficient energy flow models for CEN analysis is thus key to fully unlock these inherent benefits. The steady-state multi-energy flow (MEF) problem is the cornerstone of CEN analysis and lays the foundation for optimal operation and flexibility studies. This thesis presents a systematic evaluation of the MEF problem for steady-state analysis of CEN, exemplified in the case of coupled electricity, heat, and gas networks, by highlighting three main principles, namely formulations, coupling strategies, and solution techniques. Regarding formulations, a MEF framework is developed to incorporate various formulations for steady-state analysis of CEN, while demonstrating their impact on the convergence and computational properties of MEF models. With respect to coupling strategies, a systematic analysis is conducted on three essential coupling strategies, i.e., Decoupled, Decomposed, and Integrated, which may be associated with parallel, sequential, and simultaneous MEF computations, respectively. A set of fundamental underlying principles that characterise the coupling effectiveness in the MEF problem is introduced, namely, underlying formulations, problem size, interdependencies between networks, and calculation sequence, while highlighting their role in appraising different coupling strategies. In terms of solution techniques, besides classical Newtown-Raphson (NR), the performance of other potentially suitable algorithms, such as Quasi-NR, Levenberg-Marquardt, and Trust-Region, is extensively studied for solving MEF problems. A novel cross-over strategy is then proposed to utilise the synergistic benefits of studied algorithms for improving convergence properties without compromising computational efficiency. Building upon the insights gained from the MEF analysis, a fast decomposed algorithm is introduced for solving large-scale coupled electricity and gas networks with hydrogen injection modelling and gas composition tracking. Finally, this thesis provides novel insights and practical recommendations to identify the most suitable formulations, coupling strategies, and solution techniques for solving the steady-state MEF problem in a robust and computationally efficient way.
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ItemCommunication Receivers with Low-Resolution Quantization: Fundamental Limits and Task-based Designs( 2023-08)The use of low-resolution analog-to-digital converters (ADCs) in communication receivers has gained significant interest in the research community since it addresses practical issues in 5G/6G deployment such as massive data processing, high power consumption, and high manufacturing cost. An ADC equipped in communication receivers is often designed such that its quantization thresholds are equally-spaced or the distortion between its input and output is minimized. These design approaches, however, may yield suboptimal performance as they neglect the underlying system task that the ADCs are intended to be used for. This presents an opportunity for us to explore receiver quantization designs that cater to specific communication tasks (e.g. symbol detection, channel estimation) and to understand how quantization impacts various aspects of receiver performance such as error rate, channel capacity, estimation error. In this thesis, we consider five independent research problems related to the communication receivers with low-resolution quantizers. Three of these research problems deal with capacity analysis of certain communication channels with quantized outputs. More precisely, we derive the capacity-achieving input distributions for four different channels with phase-quantized observations and the Gaussian channel with polar-quantized observations. For the channels with b-bit phase quantizer at the output, 2^b-phase shift keying modulation scheme can attain the channel capacity. Meanwhile, the capacity can be achieved in the Gaussian channel with polar quantization by an input distribution with amplitude phase shift keying structure. Capacity bounds for MIMO Gaussian channel with analog combiner and 1-bit sign quantizers are also established in this thesis. The remaining two research problems fall under the category of task-based quantizer design. The idea is to design the quantizer in accordance to the underlying system task rather than simply minimize its input-output distortion. Focusing on M-ary pulse amplitude modulation (PAM) receiver with symmetric scalar quantizer, the closed-form expression of the symbol error rate is derived as a function of quantizer structure and position of equiprobable PAM symbols. The derived expression is used to design the quantizer according to the symbol detection task. The high signal-to-noise ratio (SNR) behavior of the error rate of the quantized communication system is characterized. Our final work is a development of a new design and analysis framework for task-based quantizers with hybrid analog-to-digital architecture. In contrast to existing task-based quantization frameworks, the theoretical predictions of our proposed framework perfectly coincides with the simulated results. Moreover, the proposed frameworks can be used in data acquisition systems with non-uniform quantizers and observations with unbounded support.
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ItemManaging Future DER-Rich Distribution Networks with a Distributed Approach: Optimal Power Flow and ADMM( 2023-06)The growing adoption of Distributed Energy Resources (DERs) is making distribution networks (i.e., both medium voltage [MV] and low voltage [LV] networks) to not only consume power but also to produce it, creating bidirectional power flows, which was something unexpected to happen when these networks were designed. This unexpected situation is creating some challenges for distribution companies to operate their networks, which includes voltage excursions (i.e., overvoltage or undervoltage) and congestion of transformers and/or conductors. To deal with these challenges, distribution companies have been using rule-based approaches to manage their controllable network assets (e.g., transformers with tap changers) and DERs (e.g., PV systems). However, rule-based approaches are very likely to become impracticable in the future, when the number of DERs is expected to be much higher, increasing the complexity of management. Besides, the higher amount of DERs is very likely to require a real-time operation of all controllable elements (i.e., DERs, OLTC-fitted transformers), which would inevitably press distribution companies to become much more active on managing these controllable elements. In this context, more advanced techniques will be required to handle the real-time operation of all controllable elements, which will have great number of variables (e.g., individual setpoints for controllable elements) and constraints (e.g., voltage and thermal limits) to be simultaneously considered. An advanced technique that has great potential to manage such complex problem is the AC OPF, but it is not scalable to be used for DER-rich, realistic large-scale integrated MV-LV distribution networks. In this PhD project, the following research is carried out to address the scalability issues of the conventional nonconvex AC OPF, particularly found in large-scale problems. Key findings and achievements are also highlighted. - An ADMM-based nonconvex three-phase AC OPF tailored for integrated MV-LV distribution networks is proposed. Its performance is tested in DER-rich, realistic large-scale integrated MV-LV networks with more than 20,000 single-phase equivalent nodes and more than 4,600 customers. The proposed ADMM-based nonconvex three-phase AC OPF shows to be accurate and faster than the conventional approach for large distribution networks. - A strategy to choose penalty parameters that allows fast convergence for the proposed ADMM-based algorithm was developed in this thesis. It is based on using different penalty parameters to each split variable, which facilitates the selection of penalty parameters that better adapts to each variable, and on using the engineering knowledge of distribution networks (i.e., number of houses, typical demand, PV sizes, maximum feeder capacity) to estimate adequate initial values for the penalty parameters, which then are fine tunned. The selected penalty parameters proved to quickly converge the proposed ADMM-based algorithm. - The implementation and performance assessment of the proposed ADMM-based nonconvex three-phase AC OPF was carried out for four engineering applications: calculation of setpoints for active power of PV systems, calculation of setpoints for active and reactive power of PV systems, calculation of setpoints for active power of PV systems as well as OLTC-fitted transformer tap positions, and calculation of setpoints for active and reactive power of PV systems as well as OLTC-fitted transformer tap positions. The proposed ADMM-based OPF has similar performance to the conventional OPF (i.e., nonconvex three-phase AC OPF) on calculating setpoints that ensure network integrity for all four applications. However, the proposed ADMM-based OPF is much faster than the conventional OPF. Therefore, the quality of the results and faster solution time across all investigated applications and time-varying conditions makes the proposed ADMM-based OPF a good alternative to solve large-scale, DER-rich three-phase AC OPF problems. - With the ADMM split, which separates the MV network problem from the LV network problems, voltage regulation devices (e.g., OLTC-fitted transformer) located at the MV network cannot sense voltage problems that occur at the end of LV feeders. This happens because the ADMM-based algorithm only shares the split point variables, which is located at the start of LV feeders, where there are no voltage problems. So, the MV network problem does not “know” about the voltage issues at the end of the LV feeder. In order to make these voltage regulation devices to sense voltage problems in another subproblem, hence enabling them to correct voltage issues, a novel adaptation on the ADMM-based algorithm was proposed. - An ADMM-based linearised three-phase AC OPF tailored for integrated MV-LV distribution networks is proposed. Its performance is tested in DER-rich, realistic large-scale integrated MV-LV networks with more than 20,000 single-phase equivalent nodes and more than 4,600 customers. This creates a formulation that is faster than the ADMM-based nonconvex three-phase AC OPF, which is ready for real-time (control cycles of 1 minute) operation of distribution networks. - A discussion on other potential applications of the proposed ADMM-based OPF formulations is carried out on the context of bottom-up services provision and TSO-DSO coordination.
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ItemDistributed Failure-Tolerant Anomaly Detection in Cognitive Radio Networks( 2023-04)The communications landscape has seen exciting developments through the emergence of small, low-cost, wireless devices. Developments in these devices have led to unprecedented connectivity and distributed computational resources—ready to support new applications. Such applications provide new benefits to end users (through cognitive radio and Internet-of-Things, IoT, to name a few), as well as new attack vectors for malicious users—with a higher number of exposed devices and communications. In this work, we investigate the use of these new wireless networking devices to make wireless communication and networking more secure by analysing wireless activity throughout a network and training anomaly detection models to identify any unusual behaviour. Using their flexible communications, onboard computation, and ability to record wireless network data, we explore state-of-the-art methods to learn patterns in network behaviour using distributed sensing and computational resources. These methods span classical and modern anomaly detection approaches, each with its own benefits and drawbacks in terms of performance, resource usage, and reliability. Throughout this work, the tradeoff between these benefits and drawbacks is outlined and new collaborative anomaly detection methods are proposed. The methods and tools in this thesis have been analysed in various network environments, to strengthen present and future wireless networks.
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ItemThermal Multispectral Imaging and Spectroscopy with Optical Metasurfaces and Deep Learning( 2022-12)Spectral imaging captures information in one or more selective bands across the electromagnetic spectrum, permitting the objects in the world to be identified by their absorption or reflection characteristics. Advancements in spectrally selective imaging have primarily been in colour imaging in the visible domain; however, infrared detectors have also enjoyed technological advances that position them ideally for thermal spectral imaging. Advanced spectrally selective imaging systems in longwave infrared (LWIR) thermal wavelengths of 8-14 microns can produce unique thermal fingerprints of objects by recording the heat radiation emitted from objects, thereby creating additional knowledge of the world otherwise difficult to acquire with colour cameras. Therefore, advanced spectral imaging finds important applications in precision agriculture (e.g., early detection of plant diseases), non-invasive medical diagnosis (e.g., vein and dental analysis, skin screening), mining (e.g., non-destructive testing), environmental monitoring (e.g., greenhouse gas detection) and recycling (e.g., plastic classification). However, existing LWIR multi- and hyperspectral imaging systems are expensive and bulky (with cryogenic cooling) and demand time and resources to process several images. Further, LWIR spectral imaging is hindered by the lack of materials responding to thermal wavelengths to design wavelength filters and the low resolution of thermal sensors to design a multi-band filter mosaic compared to their counterpart in the visible wavelengths. Recently, miniaturized infrared spectrometers were reported in the thermal wavelengths. However, they work only with a single isolated object using an active blackbody in the background and fail to detect multiple objects in real scenes. They collect an average emission from multiple objects using single or multiple detectors, which cannot be further resolved due to missing spatial information. There has been an ever-increasing demand for miniaturized and CMOS-compatible LWIR sensors performing imaging spectroscopy to realize their full potential with increased on-chip integration and new compact applications. In this thesis, I design and demonstrate lightweight and high-performance computational infrared imaging technology to enable joint spatial and spectral data acquisition in LWIR wavelengths. I propose and discuss promising solutions for handheld, mass-producible and affordable LWIR multi- and hyperspectral sensing systems using existing monochrome thermal sensors with a focus on plasmonic filters, sensor engineering and artificial intelligence. The first part of this thesis is focused on designing narrowband filter technology towards LWIR multi- and hyperspectral imagers. I begin by presenting optical metasurfaces and designing nano-optical filters with hexagonal lattices of hole/disk geometries to create surface plasmonic resonances in the LWIR regime. I perform comprehensive detector studies and detailed analyses of nano optical filters to accurately tailor the spectral responsivities of the LWIR plasmonic filters for imaging applications. I propose CMOS standard infrared plasmonic filters offering horizontal scalability, narrow spectral width, micron size thickness, and high transmission features. In the second part of this thesis, I explore time-resolved and spatially-resolved multispectral imaging systems for acquiring spatial image information in selective spectral bands. I substantiate the findings from the plasmonic filter simulations by experimentally realizing the novel LWIR plasmonic filters. Their instrumentation is explored by stacking into thermal image sensors through a filter wheel, and by integrating the filter mosaic into the camera to make a compact single-sensor imaging system. I experimentally demonstrate their time- or spatial-multiplexing performance in real-time and recover high-resolution multispectral images with deep imaging. In the third part of this thesis, I develop a deep learning-based LWIR imaging spectroscopy system prototype for acquiring more spectral information with selective spatial images in real time. This is a computational LWIR spectral imaging system acquired by the joint design of a snapshot multispectral imager at the hardware front, and a novel deep learning-based algorithmic spectroscopy concept for rapid spectral reconstruction at the software front. Snapshot images are acquired in selective spectral bands using LWIR plasmonic filters stacked to multiple detectors, which are further processed with deep neural network architecture to rapidly predict the spectra. The power of our deep learning-based imaging spectrometer is experimentally demonstrated by identifying four minerals: amethyst, calcite, pyrite, and quartz. The proposed technique is a simple and approximate 'uncooled LWIR thermal hyperspectral imaging system', which can be used to identify multiple objects by retrieving the spectral fingerprint in a real scene without recording a large number of images and without needing an active blackbody source. I thus demonstrate next-generation thermal sensing systems by merging nanoplasmonic sensors and artificial intelligence. Our results will form the basis for a snapshot, lightweight, compact, and low-cost hyperspectral LWIR imagers enabling diverse applications in chemical detection, precision agriculture, disease diagnosis, environmental sensing and industry vision.
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ItemInformation-theoretic Analysis For Machine Learning and Transfer Learning: Bounds and Applications( 2023-03)Traditional machine learning is characterized by the assumptions that the training data and target data are drawn from the same distributions. However, in practice, obtaining these data may be expensive and difficult. Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different distributions. The domain adaptation problems are widely investigated and used to improve the predictive results for one certain domain by transferring useful information from another (possibly) related domain where it is easy and cheap to obtain the data. Therefore, developing high-performance transfer learning techniques is necessary. One may ask how do we guarantee that the transfer learning is useful and efficient? In this thesis, we investigate the learning performance of the transfer learning algorithms from an information-theoretic perspective, where one broad line of work considers the learning setting where in the training phase we only have access to labelled data from the source distribution mu, possibly with some additional unlabelled or labelled data from the target distribution mu' that we are interested in the testing phase. A popular approach in this context is to formulate a measure of discrepancy between the distributions mu and mu' and to give test error bounds in terms of this discrepancy. In this sense, we are particularly interested in the generalization error, which is defined as the difference between the empirical training loss and the population loss under mu' for a given algorithm, and this quantity indicates if the output hypothesis of the algorithm has been overfitted (or underfitted). This quantity can be viewed as the distribution (over both data and algorithm) divergences between the training and testing phases. From this perspective, the information-theoretic approach will benefit from different perspectives. In this thesis, we first give a review of information-theoretic analysis for generalization error in traditional machine learning problems with identical training and testing data distributions. We then propose a fast generalization framework that enhances learning performance by identifying the key conditions and improving the learning rate, where the improvement shifts the typical information-theoretic bounds from sublinear convergence to linear convergence. Next, we extend this analysis to transfer learning under various learning settings, viewed from different perspectives. Initially, we use the variational representation of KL divergence to derive upper bounds for general transfer learning algorithms under the batch learning setting. These data-algorithm-dependent bounds offer valuable insights into the impact of domain divergence on generalization ability. We then extend the batch learning setting to the online learning setting, viewed from a Bayesian perspective, and consider transfer learning under the supervised learning setting. We view prediction from a causal perspective using the proposed potential outcome framework and derive corresponding excess risks under different distribution shifting scenarios. These bounds are useful in orienting general transfer learning problems and identifying whether transfer learning is practical. To demonstrate the practical applications of our theoretical results, we propose bound-based algorithms and show their versatility in real-world problems.