Electrical and Electronic Engineering - Theses

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http://hdl.handle.net/11343/363

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Planning of Future-Proof Low Voltage Residential Networks

Zeb, Muhammad Zulqarnain ( 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.
Extremum Seeking Control for Systems with Input Hysteresis

Yang, Yuxin ( 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.
A Block Coordinate Descent approach for solving Graph SLAM

Garces Almonacid, Javier Andres ( 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.
Adversarial Robustness in High-Dimensional Deep Learning

Karanikas, Gregory Jeremiah ( 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.
Assessing the Impacts of DER on Customer Voltages Using Smart Meter-Driven Low Voltage Line Models

Wang, Yiqing ( 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.
Probabilistic Energy Management Systems in PV-Rich Communities

Cicek van der Heijden, Nihan ( 2019)

Increasing popularity of renewable and Distributed Energy Resources (DER) and introduction of smart meters are changing the way electricity distribution grids have been operated. The stochastic nature of renewable sources adds new challenges to distribution grid operations. Communities, which are defined as groups of individual customers that utilise renewable energy sources, are especially impacted by these challenges due to their lack of scale and know-how. In this thesis, we focus on PV-rich communities that have a number of end-users equipped with rooftop photovoltaic (PV) panels without any local storage. For such PV-rich communities, it would be beneficial to model and analyse the statistical properties of DERs and their demand. Historical data can help understand the stochastic behaviour of community DER and demand, and model them as random sequences. These random sequences are used as a basis for optimal decision-making on financial contracts between communities and energy generators. Unlike stochastic optimisation, forecasting, and the Monte Carlo simulation, our methodology enables PV-rich communities to conduct long-term planning, spot-market exposure risk analysis, fine-tuning power purchase agreements, and a good understanding of statistical properties of distribution networks utilising PV systems. Our approach benefits from data science and uses models and existing data in a computationally efficient manner. With the help of our proposed model-based tool, communities are able to plan their long term financial agreements without conducting a high number of simulations.
Output constrained extremum seeking: theory and application to UAV communication chains

Liao, Chwen-Kai ( 2019)

Typically, the mobile ad-hoc network (MANET) refers to networks that do not rely on a pre-existing infrastructure such as wired routers to provide communication support. Ideally, a MANET is self-configuring, and nodes in the network can be dynamically added, removed, and change their locations as necessary. The goal of this thesis is to develop a distributed controller to restore a short-term communication service in a disaster-stricken area, through deploying a team of UAV-mounted communication relays. The deployed relays acting as mobile routers provide communication service for people in the disaster-stricken area. To serve more people, the deployed MANET is preferred to scatter in a highly populated region. In other words, we set the sparsely populated region as the constrained area where the deployed MANET are not preferred to enter. Since the environmental conditions such as humidity and obstacles within the signal path can affect, for instance, the path loss coefficient and the signal decay rate while modelling the signal distribution of the relay node. Without an accurate signal distribution model, deploying MANET to fixed locations using a signal-model-based approach can easily render the result suboptimal. In this regard, we proposed a novel extremum seeking control scheme, a model-free online optimisation strategy, to optimise the MANET communication quality, and meanwhile subject to the area constraint. Under reasonable assumptions and parameter tuning, the derived controller is shown to provide semi-global practical asymptotic stability guarantees for a class of multi-input multi-output dynamic plant. The developed method extends the known class of algorithms by explicitly incorporating constraints to meet the requirements of the UAV-based system described above. Numerical simulations of signal chaining using MANET with an area constraint are given to validate the proposed strategy.
Impact of Rooftop Solar on Distribution Network Planning

Gupta, Ashish Bert ( 2019)

Electricity networks have been undergoing significant transformation recently, especially in terms of embedded generation. There has been a lot of focus on demand fluctuations from solar and wind farms that are being connected onto high voltage (HV) grids in energy markets. But the distribution low voltage (LV) grid may prove the most challenging for the network owners and market operators. This is because rooftop solar, whether installed in commercial or residential areas, is leading to high demand fluctuations within the last mile. Customer-installed solar is also causing voltages to rise, but it is the Distribution Network Operator (DNO) on which the responsibility of voltage regulation falls. There is hence greater importance for the DNO to have full visibility of the LV feeder voltages at all times, accurately analysing proposed connections, and meeting the regulators’ and government expectations of enabling solar penetration. Voltage monitoring and regulating infrastructure at the LV level, though, is expensive to implement and hence scarce due to its huge scale. Utilities hence employ empirical or statistical techniques to calculate voltage drop and voltage rise. Conservative allowances for demand diversity and unbalance can lead to erroneous results and can form the basis of considerable utility capital expenditure programs. Utility expenditure in turn usually leads to an increase in customer bills over time. A small number of utilities in the world have access to voltage data from smart metering infrastructures, such as in Victoria, Australia, but ownership of data is becoming an open question. Data availability also presents a different problem to them, as these meters are leading to an extraordinary amount of near real-time data, which they are failing to fully embrace. They see smart-technology driven initiatives as a form of disruption and are slow or unwilling to adapt to the changing nature of the grid. This dissertation details the use of data analytics for forecasting future voltages on the network. Standard machine learning techniques are used to create a non-linear regression model fit to train parameters that reflect the operational status of the feeder. These parameters reflect load diversity and unbalance as well as generator diversity and unbalance. The trained model consequently accurately predicts voltages on the feeder with additional connections. A load-flow simulation of a real-world network is carried out. Training and testing are performed on data from different halves of the year. Predicted voltages are compared to simulation results to confirm the high accuracy, even though consumption patterns and solar irradiation patterns change due to different seasons in the test data. Hence, by leveraging interval metering data, it is shown how standard machine learning methods can be used to develop forecasting capabilities. The methodology developed in this thesis can used as a planning tool to quickly and accurately evaluate future rate of recurrence of voltage violations; and predict the voltage headroom available on the LV feeder. This is a significant outcome as predictability of LV feeder voltages is a concern for the utilities, consumers as well as regulating bodies. The presented method will enable more loads and PVs onto the network without the need of new assets such as distribution transformers or LV feeders, that may be left underutilised. It will also help resolve certain quality of supply issues such as voltage drop complaints; and help better prioritise and technically analyse constrained areas of the network. It is clear that high-quality, high-volume data analysis will play a key role in resolving the needs of the electricity industry. This thesis serves as an interface between network planning engineers and data scientists who will solve the emerging energy constraints, play a part in minimising customer energy prices and assist in the transition to decentralised clean energy sources.
Automatic assessment system for quantification and classification of pure tremor and Parkinsonian tremor

Ranjan, Rajesh ( 2019)

With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor(essential tremor). Tremor is an almost incurable disease, and it gets worse with the increase in age and improper diagnosis. The clinicians provide the diagnosis which can only limit the severity, but the patients have to visit the doctor frequently. The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In most of the countries especially which are developing or underdeveloped due to the inadequate facility or lack of rehabilitation centers for neurological disorder patient the monitoring and adequate assistance are not possible. Moreover, the improper coordination of the patient with the doctors could lead to a severer case of tremor and early ambulatory condition. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease motor rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. Automatic quantification of severity scores can help the clinician to accurately and quickly recognize the severity of tremor in the patient. Hence they can provide the necessary quantity of dosage of drugs to the patient. Continuous quantification of severity of tremor can also help the clinician and caretakers in assessing the improvement in patients concerning the diagnosis being assigned to them. Thereby the dosage of drugs can be reduced or increased accordingly, and the caretakers can provide less or more frequent assistance. The current trends in technological advances have been assertive in solving critical healthcare problems. Various devices integrated with the machine learning tools can prove highly beneficial in building an automatic assessment tool for quantification of tremor severity in agreement to the clinical rating scale. In our research, we focus on developing a system for automatic quantification and classification of tremor which can provide accurate severity scores and differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device. In this research, a study was conducted in the neuro clinic to assess the upper wrist movement of the patient suffering from pure(essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed per the Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fft based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as an input feature for various classification tools for distinguishing the PT and ET tremor types. K-nearest neighbor-based approach gave superior performance results in the quantification of tremor severity while SVM classifier using radial basis kernel showed excellent results in the classification of both tremor types. Thus, an automatic system for efficient quantification and classification of tremor was developed using feature extraction methods and supervised learning classification tools.
Acoustic beamforming analysis for wearable blind aid applications

Lim, Wei Shen William ( 2018)

The World Health Organisation estimates 36 million are blind worldwide; in addition, 217 million have severe or moderate visual impairment. Over the past decades, there has been substantial research in alleviating blindness and visual impairment. However, the blind community has yet to widely accept a single electronic travel aid (ETA) solution; the low cost white cane still remains the most popular device for orientation and mobility. One major limitation of current ETAs is their poor cost-benefit ratio. However, semiconductor advances may have reached a point where previous limitations are now surmountable as miniaturisation, flexible, low-cost and low-power circuits have been key enablers of wearable technology. Sonar has consistently been the preferred modality for single-sensor ETAs. The thesis aims to study performance characteristics of various beamforming aspects in their relation to developing a wearable-sonar system for blind aid applications. The scope of analysis covers 1) Classical Beamfomers 2) Beamforming Augmentation (Geometry, Shading, Adaptive Algorithms) 3) Spherical (3D) Beamsteering and Conformal Arrays

Electrical and Electronic Engineering - Theses

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