Electrical and Electronic Engineering - Theses

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    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.
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    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.
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    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.
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    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.
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    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
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    Automatic analysis of 4D laryngeal CT scans to assist diagnosing of voice disorders
    Hewavitharanage, Sajini Ruwanthika Gintota ( 2017)
    Vocal folds are the two smooth bands of muscles located in larynx just above the trachea. Humans produce voice by vibrating the vocal folds using the air coming from the lungs. This abduction and adduction of vocal folds are controlled by the muscles connected to thyroid cartilage, cricothyroid cartilages and arytenoid cartilages. When vocal muscles are misused or excessively used, they can be strained or damaged and voice disorders may occur. Furthermore, vocal folds can be damaged and the connecting cartilages and muscles can be affected due to the effect of other illnesses like Parkinson's disease (PD), multiple sclerosis (MS), myasthenia gravis (MG), strokes or tumours. PD is a neuro-degenerative disease which currently has neither cure nor any pathological tests to detect. The disease progresses very slowly over the years and symptoms appear when approximately 70% of the neuron cells have ceased to function. Usual symptoms are tremors and stiffness in the body muscles which results in difficulty moving most of the body parts externally as well as internally. Consequently, vocal folds and laryngeal muscles get affected and PD patients suffer from vocal impairments. Furthermore, previous studies carried out using laryngoendoscopy, laryngostroboscopy and laryngeal electromyography of PD patients found that those patients have an abnormal phase closure and abnormal laryngeal muscle activity. Moreover, in 2014, a study carried out using a group of early PD patients demonstrated increased glottis area and reduced inter-arytenoid distance in subjects. Therefore, laryngeal measurements could be used as a biomarker for early detection of PD. However, segmenting the vocal folds region from volumetric laryngeal computed tomography (CT) images is a tedious task, when it is done manually. Manual segmentation schemes require lot of expert knowledge and time, and often provide poor objective and reproducible results. In this project, we hope to develop a novel automated algorithm to segment the vocal folds region and measure the laryngeal parameters. This thesis consists of two major parts; first it proposes a fully automated segmentation method for segmenting the rima glottidis from 3D laryngeal CT scans and generates the time series for rima glottidis areas, which in future can be used to develop an automated diagnosis tool for voice disorders. The gray-level difference features are learnt through a support vector machine classifier and several post processing algorithms are introduced to refine the final segmentation result. Second, a fully automated method to estimate the vocal plane position in a 3D laryngeal CT volume using computer vision algorithms and techniques is proposed. Vocal plane position is identified using anatomical markers like thyroid cartilage and vertebral bones and these markers are segmented using gray-scale and edge-based features. The experiments are conducted using a private data set from the Movement Disorder Clinic at Monash Medical Centre The detailed implementations of the two methods including feature extraction, kernel selection, post processing and validation are explained in this thesis.
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    Adaptive control of voltage sourced inverters in microgrid
    Wu, Zhiding ( 2017)
    The microgrid is a new scheme of future distribution power grid with small scale and the integration of distributed generation (DG). Generally, the interface between the DG and the utility grid is a voltage sourced inverter (VSI) and a power filter, aiming to (i) regulate the electricity injection in grid-connected mode and the additional voltage \& frequency in islanding mode, (ii) eliminate the high-frequency harmonics caused by pulse width modulation (PWM) controlled VSI, respectively. The coefficients of VSI controller and the parameters of power filter should be designed properly to achieve system stability and to prevent harmonic injection and excessive power loss. In the equivalent structure of VSI based DG unit, the grid impedance is another component that exists between the filter and the ideal grid, which is an aggregate impedance of the whole network. However, it is usually seen to be uncertain or unknown in the practical system that will highly affect the controller design and output performance of VSI, both in grid-connected and islanded modes. Imprudent selection of filter parameters may cause worse filtering outcomes and significant power losses. The rationale for the need for optimal filter design is that conventional designs methods are only able to provide a range of values for each parameter and therefore are unable to guarantee performance. This thesis proposes an optimal design method of power filter with passive damping to address these problems. The novelty of the proposed method stems from using multi-objective optimisation approach to find optimal values of filter parameters using the genetic algorithm. The objective of the optimisation is to attain high harmonic attenuation performance and small switching frequency ripple while achieving low power consumption. The proposed method is verified through simulation studies carried out on a three-phase grid-connected VSI based DG system, using the parameter values obtained from the proposed design method. The simulation results demonstrate that the new method can achieve a higher level of ripple reduction, greater harmonic attenuation, and higher system efficiency than existing design methods. Controller design for grid-connected and islanded VSI is based on the knowledge of equivalent grid impedance (or network impedance). Grid impedance is determined by experience or calculated through the technical manuals of overhead line or underground cable in the system. However, the actual line impedance may vary due to the variation of temperature, humidity, and ageing. Any inconsistency of grid impedance between the control loop and the real value will lead to poor output performance and even instability of VSI. By using the information of grid topology and multiple measurement scenarios of bus voltage and power, a network impedance estimation (NIE) is proposed to calculate every line impedance of the grid based on reverse power flow. The Newton-Raphson iterative method is employed to solve the proposed NIE problem, while the corresponding Jacobian matrix is formulated. Then the impedance of every line in the network can be obtained iteratively. The NIE method is verified through three benchmark systems. Estimation results show that great accuracy and fast iteration of the proposed method can be realised. The proposed NIE process can operate online to provide the estimated value of network impedance continuously. Based on the knowledge of network topology, the equivalent grid impedance of every DG unit can be computed subsequently. When the \emph{LCL} filter is properly designed by the proposed optimisation method, the accurate model of VSI can be obtained. For grid-connected mode, an adaptive state feedback controller integrated with NIE process is presented. The state feedback gain is well designed by using the estimated impedance values of NIE. In islanded mode, the output feedback controller with droop control is more suggested since its simplicity and good power sharing. However, sharing performance is very sensitive concerning the value of impedance. Hence, an adaptive controller with virtual impedance is proposed based on the NIE method to achieve the desired performance. Then the output impedance of every VSI can be adequately designed. Grid-connected and islanded simulations are carried out to verify the proposed control method in a 14-bus benchmark microgrid. Results demonstrate the effectiveness and superiority of the NIE based adaptive methods, which are better in comparison with the conventional method in both grid-connected and islanded operations. Moreover, it also shows that the circulating current among multiple islanded DG units is eliminated and the stability of system frequency can be preserved during the process of impedance variation.
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    Short-reach optical communication using directly modulated lasers
    YUAN, FENG ( 2016)
    During the past decade, with the rapid development of cloud computing, smart phones and mobile Internet, there has been an increasing demand for high-capacity and high reliable metropolitan area networks and access networks. Unlike long-haul transmission systems, where bulky and expensive optical components are normally used, the short-to-medium reach optical link which is in the range of a few kilometres to hundreds of kilometres is naturally costsensitive. Therefore, in recent years, instead of using the traditional LiNbO3-based intensity and I/Q modulators, the compact and low-cost directly modulated laser (DML) has drawn plenty of attention and becomes an attractive candidate in such short-reach applications. DML-based optical transmission systems have been extensively explored and successfully demonstrated with various configurations, including using different modulation, detection and multiplexing methods. Among these demonstrations, DML has already shown its capability to realize optical access links for a data rate of 100 Gbps and beyond. Despite its promising performance that has been reported, not only the potential of DML has not been fully exploited, but also its limitations as an optical transmitter has not been thoroughly discussed. It is well-known that one of the major drawbacks of ordinary DML being used as a transmitter is its intrinsic frequency chirp, which, after combining with the fibre chromatic dispersion, becomes an increasingly detrimental factor to data rate enhancement. Efforts have been made to overcome such obstacles, such as employing inverse dispersion fibre, optical injection locking, optical filters and specially designed DMLs. The effectiveness of the above approaches on extending the transmission reach and increasing the data rate has been rather limited. Additionally, they always require extra devices, adding more complexity and cost to the system, and are thus not desirable for low-cost implementations. In this work, utilizing the digital signal processing (DSP) facilitated coherent detection (COHD), we propose a new modulation scheme on DML-based systems, which is called complexmodulated DML (or CM-DML). It has been shown that a significant optical signal-to-noise ratio (OSNR) sensitivity improvement can be achieved compared with the traditional intensityonly detection method. Besides, we also experimentally demonstrated that the CM-DML system exhibits a reasonable tolerance for reduced receiver bandwidth. In a nutshell, it is feasible to realize CM-DML systems using cost-effective receivers with narrow bandwidth.
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    A low-power minaturised intracranial pressure monitoring microsystem
    Ghanbari, Mohammad Meraj ( 2017)
    The ultimate goal of this work is realisation of a fully implantable chronic intracranial pressure (ICP) monitoring system. Due to the required mm-scale form factor of the implantable device, the available power is scarce. This calls for investigation of new circuit and sensor integration techniques to decrease the total power consumption of the system down to a few hundreds of nano watts. So the main focus of this work is design of an ultra-low power integrated circuit (IC) for measuring ICP. Power consumption minimization of the sensing system proposed in this work paves the way for integration of an RF-power scavenger or biological fuel cells. The proposed sensing system also takes full advantage of Invensense MEMS-CMOS process to heterogeneously integrate the sensor and interface. This integration type requires no post-processing and results in sub-pF sensor-interface parasitic interconnection capacitance Cp which is an order of magnitude smaller than previously reported Cp’s.
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    The energy efficiency of EDFA and Raman Fiber Amplifier
    Wang, Peng ( 2016)
    Optical fibre links using optical amplifiers in combination with advanced modulation formats and Forward Error Correction (FEC) are promising technologies to increase transmission distance as well as the capacity of communication systems. The rapidly increasing energy consumption of telecommunication networks is driving network designers to consider how to minimize energy consumption of optical fibre links by choosing the right combination of optical amplifier, advanced modulation format and error correction technology. This thesis involves development of a model for calculating the lower limit of power consumption of EDFAs when designing an optical fibre link. We compare the energy efficiency of Distributed Raman Fiber Amplifiers (DRFA) and Erbium-Doped Fiber Amplifiers (EDFA) used in long-haul transmission systems. This comparison accounts for the interaction between optical link power, signal quality (as measured by the Bit Error Rate (BER)), and the use of FEC. We show that deploying DRFAs in some scenarios may be more energy efficient than EDFAs, despite their intrinsic requirement for higher pump powers.