Electrical and Electronic Engineering - Theses
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ItemPlanning of Future-Proof Low Voltage Residential Networks( 2023-05)The increasing penetration of residential rooftop photovoltaics (PV) and home charging of electric vehicles (EVs) is presenting technical challenges for distribution companies responsible for managing the poles and wires. These challenges include problems such as voltage rise and asset congestion, which are caused by reverse power flows from PV systems. Additionally, there are issues of voltage drop and asset congestion that result from EV charging. Distribution networks are experiencing these problems because the existing low voltage (LV) networks were not designed for PV and EVs. To host PV and EVs, solutions such as thicker conductors or On-load Tap Changer (OLTC) must be added to the existing networks. Most of the existing LV networks were planned traditionally, by appropriately sizing three-phase conductors of distribution lines and suitable LV distribution transformer to ensure that customer voltages and power flows in network assets (transformer, lines) are within designed limits. To host PV and EVs in traditionally designed networks, many research works in the literature focused on adding smart voltage regulation devices such as OLTC to avoid using thicker conductors for three-phase line segments. Some also suggested the use of thicker conductors for distribution lines with a transformer using nominal voltage setting or an off-load tap changer. However, a detailed cost comparison of the mentioned design has not been done for the brand-new three-phase LV networks with 100% PV and EVs, i.e., when each house has a PV system and an electric car. Such a comparison can help identify the most cost-effective design for brand-new LV networks that can host 100% residential PV and EVs without requiring the addition of solutions later. This thesis fills the mentioned research gap by proposing an optimal power flow (OPF) based methodology to plan the brand-new three-phase LV residential networks for 100% PV and EVs. The developed methodology determines suitable conductor sizes and optimal tap changer position (depending on the design alternative) while the topology of the LV network follows the street layout. Additionally, it compares three design alternatives to determine the most cost-effective design. The compared design alternatives include appropriately sized conductors for three-phase line segments with either nominal voltage settings, off-load tap changer fitted transformer, or OLTC fitted transformers. Realistic considerations related to the tap changers, sizes of conductors available in the market, the impact of parallel unbalanced LV feeders on each other, and the impact of connected medium voltage (MV) with their LV parts are included in this planning. The proposed planning methodology is applied on a realistic Australian neighborhood with 89 single-phase residential customers. For the case study neighborhood, it is concluded that the most cost-effective design depends on the distance of the LV transformer from the zone substation (HV/MV transformer). Due to the impact of the connected MV network, voltage varies on the primary side of the LV transformer. The closer the distance, the lower are the voltage variations on the primary side of LV transformer, and therefore, the lower need for voltage regulation. For such LV networks, thicker conductors for lines and a transformer fitted with off-load tap changer provide the most economical design. On the other hand, for a group of customers located far away, the use of a transformer fitted with OLTC, and thinner conductors is the most economical design due to the need for better voltage regulation. The single tap setting of off-load tap changer needs a combination of thicker conductors for the lines, whose cost is not justifiable in such a scenario. This analysis helps us understand that no single design alternatives is economically feasible for all LV neighborhoods, and rather, the characteristics of the network are important to be considered. With the proposed three-phase methodology, and implementation on a neighborhood, this research work provides a detailed insight of the most cost-effective design for the future LV networks with higher penetrations of PV and EVs. It can guide distribution companies to make their three-phase LV networks future-proof, by selecting the most cost-effective design for neighborhoods with different characteristics.
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ItemExtremum Seeking Control for Systems with Input Hysteresis( 2023)Extremum seeking control (ESC) is a class of data-driven online optimization techniques that can find an optimum value of an unknown steady-state input-output mapping of a controlled dynamical system using its input and/or output measurements. The extremum seeking literature is extensive and many algorithms were proposed in the past 20 years. The focus of this thesis is extremum seeking for systems with actuators that exhibit hysteresis, which we represent using a simple Bouc-Wen hysteresis model. For instance, magneto-restrictive, piezo-ceramics and shape memory alloy actuators exhibit such hysteresis behaviour. Using simulations, we first demonstrate that a standard continuous time extremum seeking scheme does not perform well when applied to such systems with hysteresis nonlinearities. Next, we propose a modification of this ESC by adding to it a high-frequency sinusoidal dither signal and, then, prove that this modified scheme achieves extremum seeking. Our analysis demonstrates that the standard assumption of the existence of a unique minimum or maximum in the steady-state map does not hold for systems with hysteresis. Yet, we prove that the modified scheme achieves extremum seeking for such systems if the ESC parameters are tuned appropriately. Finally, we demonstrate our theoretical results via simulations. The proof of our main result relies on the Lyapunov stability theory, partial averaging, and singular perturbation techniques.
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ItemA Block Coordinate Descent approach for solving Graph SLAM( 2021)Simultaneous Localisation and Mapping (SLAM) refers to the problem of estimating the position of a mobile robot navigating in an unknown environment while simultaneously constructing a map of it, using measurements collected by sensors mounted on the robot, such as cameras, lasers, radars, or inertial sensors. SLAM is of particular interest when there is no prior knowledge of the environment nor external sources of localisation (compass, GPS). In this sense, SLAM aims for autonomy of robot motion and environment discovery. The graph-based formulation of the SLAM problem, also commonly referred to as Graph SLAM, maximum a posteriori estimation, factor graph optimisation or smoothing and mapping (SAM), is considered the current de-facto standard formulation for SLAM. This approach defines the SLAM problem as a nonlinear least squares minimisation problem, commonly solved via successive linearisation methods such as Gauss-Newton. However, iterative line search methods have limitations in terms of convergence guarantees and scalability, which suggest the research potential for alternative optimisation algorithms. In our research, we study an alternative numerical method for solving the Graph SLAM problem: the Block Coordinate Descent method. By partitioning the problem into a series of optimisation subproblems, this approach may offer comparatively better performance than iterative linearisation algorithms, such as lower per-iteration computational complexity, scalability and parallel processing capabilities. Importantly, this method is not dependant on linearisation, and under certain conditions, may offer convergence guarantees towards stationary points. We present our Block Coordinate Descent approach by systematically analysing the attributes of the optimisation subproblems originating from the use of this numerical method on a Graph SLAM problem formulation based on particular inertial, bearing and range measurement models: the Affine Motion Model, the Affine Bearing Model and the Squared Range Model. We verify the resulting optimisation subproblems satisfy conditions that offer convergence guarantees and scalability properties. Additionally, we evaluate our Block Coordinate Descent approach by implementing the resulting algorithm in a simulated environment using real-world datasets, comparing its performance to the Gauss-Newton line search method.
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ItemAdversarial Robustness in High-Dimensional Deep Learning( 2021)As applications of deep learning continue to be discovered and implemented, the problem of robustness becomes increasingly important. It is well established that deep learning models have a serious vulnerability against adversarial attacks. Malicious attackers targeting learning models can generate so-called "adversarial examples'' that are able to deceive the models. These adversarial examples can be generated from real data by adding small perturbations in specific directions. This thesis focuses on the problem of explaining vulnerability (of neural networks) to adversarial examples, an open problem which has been addressed from various angles in the literature. The problem is approached geometrically, by considering adversarial examples as points which lie close to the decision boundary in a high-dimensional feature space. By invoking results from high-dimensional geometry, it is argued that adversarial robustness is impacted by high data dimensionality. Specifically, an upper bound on robustness which decreases with dimension is derived, subject to a few mathematical assumptions. To test this idea that adversarial robustness is affected by dimensionality, we perform experiments where robustness metrics are compared after training neural network classifiers on various dimension-reduced datasets. We use MNIST and two cognitive radio datasets for our experiments, and we compute the attack-based empirical robustness and attack-agnostic CLEVER score, both of which are approximations of true robustness. These experiments show correlations between adversarial robustness and dimension in certain cases.
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ItemAssessing the Impacts of DER on Customer Voltages Using Smart Meter-Driven Low Voltage Line Models( 2020)The rapid adoption of distributed energy resources (DER) in low voltage (LV) networks is driving the need for distribution companies to assess their impacts on customer voltages in any demand/generation condition (also known as what-if analyses). Although this can be done by running conventional power flow analyses, there are two main challenges. The first one is that LV line models (three-phase LV feeder lines and single-phase service lines) are needed. However, the corresponding impedances are often poorly recorded by distribution companies. In other words, the information is incomplete or not available. The second challenge is that, if such studies are needed for operational purposes (calculations in near real-time), then implementing power flows to be run for hundreds of LV feeders can be a complex task for distribution companies. Several studies have attempted to solve the challenges of impedance estimation and simplified voltage calculations, but there are still some gaps. Given the rollout of smart meters in many places, several works have exploited smart meter measurements to estimate impedances of LV line models. However, in most cases, the three-phase nature of LV feeders (i.e. the phase couplings) is not adequately considered; and thus, such approaches cannot cater for the needs of inherently unbalanced LV networks. For the voltage calculations, existing simplified methods are based on the single-phase voltage drop equations and an additional ‘unbalanced factor’. Given that the ‘unbalanced factor’ is determined either empirically or using data-driven techniques that require large amounts of data, such methods cannot be precise or practical enough for their actual implementation by distribution companies. This thesis proposes a practical approach to determine customer voltages (in what-if analyses) using smart meter-driven LV line models that adequately capture the effects among the three phases. Firstly, impedances (three-phase LV feeder lines and single-phase service lines) are estimated using linearised voltage drop equations and a regression technique. This process exploits historical time-series measurements from smart meters and at the head of the LV feeder and assumes that the customer connectivity and customer phase connection are known. Then, using the linearised voltage drop equations and the estimated impedances, simplified calculations of customer voltages can be carried out for what-if analyses (any demand/generation condition). The proposed approach is demonstrated on realistic LV networks from Australia and the UK. Impedances are estimated considering realistic weekly historical meter measurements (i.e. active power, reactive power, and voltage magnitudes) with a 15-minute resolution (672 time steps). Voltage calculations (what-if analyses) consider weekly demand and generation profiles with 1-minute resolution (10,080 time steps). Results show a very good accuracy for most of the estimated impedances. More importantly, the calculated voltages are not only highly accurate but are also obtained much faster than with a power flow engine. Consequently, the findings suggest that the proposed approach is accurate and practical enough for its use by distribution companies.