Electrical and Electronic Engineering - Research Publications
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ItemA Probabilistic Reverse Power Flows Scenario Analysis Framework(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020)
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ItemExtremum Seeking Control with Sporadic Packet Transmission for Networked Control Systems(IEEE, 2020-06)Extremum Seeking Control (ESC) is a data-driven optimization technique that can steer a dynamic plant towards an extremum of an unknown but measurable, input to steady-state map. In the context of Networked Control Systems (NCS) a new implementation method for ESC inspired by the well known Luus-Jaakola algorithm is proposed. The main motivation is to minimize the communication burden associated with the search phase of ESC. In the proposed method the controller only requires a notification of a change registered at the sensor, rather than the full information available at the sensor. This event based approach leads to sporadic packet transmission. In addition the proposed method is able to directly account for constraints whilst seeking for the desired extremum. The constraints may be of the inequality or equality type. The algorithm's behavior is illustrated on a networked water pump control system.
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ItemPolicy Options to Regulate PV in Low Voltage Grids-Australian Case with International Implications(Springer Nature, 2019-12)This article shows a socio-technical evaluation of the Australian case which has international implications for energy policy and regulation. Australia is one of few places globally that have faced domestic PV (photovoltaic system) adoption of above 50% of feeder connections. This leads to grid issues and is an emerging issue globally. Grid issues include over-voltage, thermal overload, frequency instability and voltage instability. This paper offers a policy process to regulate PV. This research extends earlier econometric modelling of Australian PVadoption data and extends it to focus on PVregulation in low voltage grids. This paper explores five policy options to help regulate PVin low voltage grids: the role of distribution businesses, inverter regulation, PVexport limits, cost reflective pricing, and storage. Policy complexity comes from the need to incorporate many stakeholder perspectives, and this research contributes to policy clarity by seeking a consensus.
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ItemLearning control in robot-assisted rehabilitation of motor skills–a review(Informa UK Limited, 2016-01-02)
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ItemA Comparative Testing Study of Commercial 18650-Format Lithium-Ion Battery Cells(ELECTROCHEMICAL SOC INC, 2015)
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ItemOptimal Charging of Electric Vehicles Taking Distribution Network Constraints Into Account(IEEE Press, 2015)The increasing uptake of electric vehicles suggests that vehicle charging will have a significant impact on the electricity grid. Finding ways to shift this charging to off-peak periods has been recognized as a key challenge for integration of electric vehicles into the electricity grid on a large scale. In this paper, electric vehicle charging is formulated as a receding horizon optimization problem that takes into account the present and anticipated constraints of the distribution network over a finite charging horizon. The constraint set includes transformer and line limitations, phase unbalance, and voltage stability within the network. By using a linear approximation of voltage drop within the network, the problem solution may be computed repeatedly in near real time, and thereby take into account the dynamic nature of changing demand and vehicle arrival and departure. It is shown that this linear approximation of the network constraints is quick to compute, while still ensuring that network constraints are respected. The approach is demonstrated on a validated model of a real network via simulations that use real vehicle travel profiles and real demand data. Using the optimal charging method, high percentages of vehicle uptake can be sustained in existing networks without requiring any further network upgrades, leading to more efficient use of existing assets and savings for the consumer.
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ItemFourier Tract Sampling (FouTS): A framework for improved inference of white matter tracts from diffusion MRI by explicitly modelling tract volume(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2015-10-15)Diffusion MRI tractography algorithm development is increasingly moving towards global techniques to incorporate "downstream" information and conditional probabilities between neighbouring tracts. Such approaches also enable white matter to be represented more tangibly than the abstract lines generated by the most common approaches to fibre tracking. However, previously proposed algorithms still use fibre-like models of white matter corresponding to thin strands of white matter tracts rather than the tracts themselves, and therefore require many components for accurate representations, which leads to poorly constrained inverse problems. We propose a novel tract-based model of white matter, the 'Fourier tract', which is able to represent rich tract shapes with a relatively low number of parameters, and explicitly decouples the spatial extent of the modelled tract from its 'Apparent Connection Strength (ACS)'. The Fourier tract model is placed within a novel Bayesian framework, which relates the tract parameters directly to the observed signal, enabling a wide range of acquisition schemes to be used. The posterior distribution of the Bayesian framework is characterised via Markov-chain Monte-Carlo sampling to infer probable values of the ACS and spatial extent of the imaged white matter tracts, providing measures that can be directly applied to many research and clinical studies. The robustness of the proposed tractography algorithm is demonstrated on simulated basic tract configurations, such as curving, twisting, crossing and kissing tracts, and sections of more complex numerical phantoms. As an illustration of the approach in vivo, fibre tracking is performed on a central section of the brain in three subjects from 60 direction HARDI datasets.
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ItemThe dynamics of the epileptic bran reveal long-memory processes(FRONTIERS MEDIA SA, 2014)The pattern of epileptic seizures is often considered unpredictable and the interval between events without correlation. A number of studies have examined the possibility that seizure activity respects a power-law relationship, both in terms of event magnitude and inter-event intervals. Such relationships are found in a variety of natural and man-made systems, such as earthquakes or Internet traffic, and describe the relationship between the magnitude of an event and the number of events. We postulated that human inter-seizure intervals would follow a power-law relationship, and furthermore that evidence for the existence of a long-memory process could be established in this relationship. We performed a post hoc analysis, studying eight patients who had long-term (up to 2 years) ambulatory intracranial EEG data recorded as part of the assessment of a novel seizure prediction device. We demonstrated that a power-law relationship could be established in these patients (β = - 1.5). In five out of the six subjects whose data were sufficiently stationary for analysis, we found evidence of long memory between epileptic events. This memory spans time scales from 30 min to 40 days. The estimated Hurst exponents range from 0.51 to 0.77 ± 0.01. This finding may provide evidence of phase-transitions underlying the dynamics of epilepsy.