Electrical and Electronic Engineering - Theses
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ItemProbabilistic Energy Management Systems in PV-Rich Communities( 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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ItemOutput constrained extremum seeking: theory and application to UAV communication chains( 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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ItemImpact of Rooftop Solar on Distribution Network Planning( 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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ItemAutomatic assessment system for quantification and classification of pure tremor and Parkinsonian tremor( 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.