Browsing by Author "Westwick, David"
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- ItemOpen AccessA Pre-Placement Individual Net Length Estimation Model and an Application for Modern Circuits(2011) Farshidi, Amin; Behjat, Laleh; Westwick, David
- ItemOpen AccessAlgorithms for non-convex system identification(2007) Jazayeri, Seyed Pouyan; Rosehart, William Daniel; Westwick, David
- ItemOpen AccessAlgorithms for the reduction of clutter in tissue sensing adaptive radar (TSAR) signals(2007) Kurrant, Douglas John; Westwick, David; Fear, Elise
- ItemOpen AccessA Biologically Inspired Supervised Learning Rule for Audio Classification with Spiking Neural Networks(2021-06-15) Peterson, Dylan George; Leung, Henry; Westwick, David; Uddin, Gias; Wang, XinAudio classification has many practical applications such as noise pollution monitoring, wildlife monitoring, audio surveillance, speech recognition, and more. For many of these applications, deploying classifiers on low powered devices for persistent monitoring is desirable. Artificial neural networks (ANN) have achieved significant success for audio classification tasks. However, it may not always be feasible to deploy current state-of-the-art ANNs to embedded devices due to their memory footprint and power consumption. Biologically inspired neural networks, also known as spiking neural networks (SNN), have been shown to significantly reduce power consumption during inference when compared with equivalent ANNs. They have also been theoretically proven to be more computationally powerful per unit than ANNs. These two properties make SNNs an attractive solution for machine learning tasks on low powered embedded devices, such as at the edge in an Internet of Things (IoT) sensor network. However, SNNs tend to lag behind in performance when compared to ANNs. This is partially because training SNNs is difficult since the standard backpropagation algorithm is not directly applicable due to the non-differentiable spiking nature of SNNs. Encoding data into spike trains compatible with SNNs is also an unresolved question when applying SNNs. This work compares different spike encoding schemes for audio data, and a learning algorithm for multilayer SNNs inspired by biologically plausible learning rules is developed. The proposed learning rule is then successfully applied to simple pattern recognition and audio classification tasks.
- ItemOpen AccessBlind identification of a Wiener-Bose structure using expectation maximization(2011) Bu bshait, Abdullah Saad; Westwick, David
- ItemOpen AccessBlind Identification of the Electromechanical Modes of a Power System using a Wiener Model(2016) Hossain, Tasnim; Westwick, David; Rosehart, William; Zareipour, Hamidreza; Nowicki, Edwin PeterIn this thesis, a blind system identification technique using a Wiener model is used to estimate the power system modes. A Wiener model, a universal approximator consisting of a dynamic linear system followed by a memoryless nonlinear element, is used to estimate the power system nonlinearities. It also constructs a set of intermediate data, which can be used by a linear estimation technique such as subspace identification for estimating the power system electromechanical modes. In this research, a blind variant of a subspace method, Numerical Algorithm for Subspace State Space System IDentification (N4SID), is used. The algorithm is tested with simulation data from the Kundur two-area network. The accuracy and reliability of these estimates are accessed by carrying out Monte Carlo simulations. The estimated results obtained from the simulated system using a Wiener model were very accurate with reduced prediction errors and was a good fit for the power system.
- ItemOpen AccessData-Driven Modeling of Wind Turbine Structural Dynamics and Its Application to Wind Speed Estimation(2016-01-27) Saberi Nasrabad, Vahid; Sun, Qiao; Westwick, David; Wood, David; Mohamad, AbdulmajeedIn wind turbine control systems, the wind speed measurement is used in order to derive the optimal shaft speed for achieving the Maximum Power Point Tracking (MPPT) and to adjust the pitch angle optimally for protecting the turbine from excessive loading. In this thesis, a tower detection based effective wind speed estimation method is proposed. The tower dynamics is identified using subspace system identification method. To estimate the effective wind speed, an online model-based aerodynamic thrust force estimator is designed and implemented using Kalman filter and recursive least square algorithm. The estimated aerodynamic thrust force is used as an input to a neural network estimator to solve the inverse aerodynamic thrust force equation and estimate the effective wind speed. Finally, the simulation results for effective wind speed estimation for a turbulent wind field are presented and an evaluation method based on correlation coefficient is used to validate the results.
- ItemOpen AccessDevelopment of an Autotuner for Plants with Stochastic Disturbances(2017-12-20) Brebnor, Dave; Foley, Michael; Clarke, Matthew; Svrcek, William; Westwick, David; Kwok, EzraThe most universally exercised model which amply describes the transient behaviour of a wide range of chemical processes is the first-order-plus-deadtime model. An identification experiment must be conducted to generate the data required to fit an accurate first-order-plus deadtime model. Relay feedback procedures have superseded the conventional step response identification tests for the purpose of tuning proportional-integral-derivative controllers. However, these techniques are known to perform poorly in the presence of process disturbances. Developing an autotuner that performs well in the presence of stationary stochastic disturbances is the chief objective of this dissertation. This is accomplished by the inclusion of two separate first-order filters in the relay feedback loop. A few guidelines are provided for the selection of the filter time constants. After the identification procedure yields the process parameters, a model-based tuning rule can be employed for controller tuning. Simulations of a continuous stirred tank heater and experiments conducted on a pilot-plant stirred tank heater illustrate the feasibility of the proposed autotuner.
- ItemOpen AccessIdentification of a model of sound transmission in the human knee: vibroarthrographic signals as a diagnostic tool(2005) Dempsey, Erika; Westwick, David
- ItemOpen AccessIdentification of linear, time-varying systems(2005) Sanyal, Sreemoyi; Westwick, David
- ItemOpen AccessIdentification of time-varying nonlinear systems(2007) Ikharia, Bashiru Isa; Westwick, David
- ItemOpen AccessIdentifying the Sources and Parameters of Disturbances in Power Systems(2022-06-19) Mansouri Habibabadi, Mohammad; Knight, Andrew Michael; Westwick, David; Nielsen, Jorgen S; Pahlevani, Majid; Nowicki, Edwin Peter; Chen, Yu (Christine)The high penetration of renewable energy in power systems makes power systems dynamically more complicated; hence, a power system faces frequent disturbances, including electromechanical oscillations, harmonics and subharmonics, and forced oscillations. Furthermore, because a power system is dynamically complicated, the classic methods that mainly rely on the system’s dynamic model cannot work as it is hard to have the details of the power systems dynamic or is impossible. Therefore, measurement-based methods, which do not need the system dynamic, have received significant attention in recent years. This dissertation offers measurement-based methods to identify and mitigate three disturbances, including electromechanical oscillations, harmonics and subharmonics, and forced oscillations. The contribution of this research can be divided into four parts.The first contribution proposes a measurement-based strategy to estimate the parameters of the multi-mode electromechanical oscillations. This strategy employs the cascade structure of Damped-SOGI to estimate the parameters of electromechanical oscillations. The advantages of the proposed method are the capability of online implementation, robustness against noise, no need for dynamics of the system, no need to pre-filtering and pre-processing, and simplicity.The second contribution of this thesis is an algorithm to mitigate electromechanical oscillations. In this algorithm, the dynamic of electromechanical oscillations is extracted by the Damped-SOGI; afterward, this dynamic is placed in the control loop. Thus, the electromechanical oscillations are rejected from the power systems output based on the Internal Model Principle (IMP). Computer simulation and experimental results confirm the effectiveness of the proposed method.An algorithm to identify the source of harmonics and subharmonics is proposed as the third contribution of this dissertation. In this algorithm, the N4SID, an identification method, extracts the power system’s dynamics and inputs. Afterward, the source of the subharmonic is identified by studying the correlations between various extracted inputs. Simplicity and robustness against noise are of the advantages of the proposed algorithm.Locating the source of forced oscillations is tackled by subspace identification in the fourth contribution of this thesis. In this algorithm, the dynamic model of the power system is identified by subspace identification. Using the identified model, the dynamic responses of the forced oscillation are extracted in each PMU location. Finally, the source of forced oscillation is deter- mined by studying the magnitude and phase or the correlations of extracted forced oscillations. The capability to distinguish between forced oscillations and electromechanical oscillations is of the advantages of the proposed algorithm.
- ItemOpen AccessIntegrated Expansion Planning of Electricity, Heat, and Gas in Presence of Demand and Wind Uncertainty(2022-06-22) Mozafari Jovein, Yasaman; Rosehart, William; Bergerson, Joule; Westwick, David; Zinchenko, Yuriy; Yanushkevich, Svetlana; Gokaraju, RamakrishnaRecent shift towards higher renewable penetration in power systems, has resulted in increasing gas-fired generation capacity in power systems, implying electricity and gas infrastructure interdependency. Furthermore, highly efficient combined heat and power (CHP) units lead to heat and electricity interdependency. It is crucial to consider these interdependencies in the expansion planning of energy systems for an effective and reliable investment planning and policy design. In this thesis, the problem of integrated expansion planning of electricity, heat, and gas in presence of demand and wind uncertainty is addressed. A comprehensive multi-area planning model including generation, CHPs, boilers, transmission network, and gas pipeline expansion is proposed. The advantages of integrated approach versus the non-integrated approaches in terms of cost and emission is illustrated through simulations on a simplified Alberta energy system model. Representative operating scenario selection method is used to model wind and demand uncertainties, and to address computational complexity of a central planning model. Load duration curve technique is compared with k-means and k-means++ clustering, the impact of initialization is investigated, and the impact of spatial data correlation on the solution is analyzed. To overcome drawbacks of k-means clustering, application of algebraic multi-grid clustering in scenario selection for integrated energy system expansion planning is explored. The simulation results on a modified IEEE-118 bus test system and a 14-node gas network shows that the algebraic multi-grid clustering outperforms the classical methods such as k-means by following the benchmark case more closely. Finally, to ensure robustness of the obtained investment plan, an adaptive robust optimization model including electricity demand, heat demand, and wind uncertainty is proposed. The model ensures reliability targets in both electricity and heat sector are met. The simulation results on the IEEE-118 bus test system verify the effectiveness of the proposed model in dealing with uncertainties and meeting future electricity and heat demand reliably.
- ItemOpen AccessMicroelectrodes for Neural Stimulation: Effects of Geometry(2013-10-08) Ghazavi Khorasgani, Atefeh; Westwick, David; Dalton, ColinMulti-electrode arrays are non-invasive devices for neural stimulation in vivo and in vitro. Improving the efficiency of these devices is desired for stimulating neurons over extended periods of time. In this work, changing the geometry of the electrode used in the array is investigated. This approach will improve efficiency without requiring fundamental changes in the fabrication process, allowing for ease of implementation. A model is presented to study features that provide optimum stimulation threshold from different sizes and shapes of electrodes. Specially, a single neuron-electrode interface was modelled and cell depolarization generated by stimulating the cell by different electrode shapes were compared. The geometries investigated were star, spiral, serpentine, and circular electrode shapes. Based on the simulations, the electrode geometries were designed and then fabricated into a planar microelectrode array test device. Proof of principle in vitro experimentation was then conducted, with the results being compared to the simulations.
- ItemOpen AccessModel Predictive Control of DFIG-Based Wind Power Generation Systems(2013-04-12) Soliman, Mostafa; Malik, Om; Westwick, DavidNovel control strategies that improve the cost effectiveness of wind energy conversion systems are proposed in this thesis. The main focus is on grid-connected variable-speed variable-pitch wind turbines equipped with doubly fed induction generators (DFIGs). At the wind turbine control level, a multivariable control strategy based on model predictive control techniques is proposed. The proposed strategy is formulated for the whole operating region of the wind turbine, i.e., both partial and full load regimes. The pitch angle and generator torque are controlled simultaneously to maximize energy capture, mitigate drive train dynamic loads, and smooth the power generated while reducing the pitch actuator activity. This has the effect of improving the efficiency and the power quality of the electrical power generated, and increasing the life expectancy of the installation. Extensive simulation studies show that the proposed control strategy provides superior performance when compared to classical control strategies commonly used in the litterature. For applications having fault tolerant control requirements, such as offshore wind farms, a new wind turbine control strategy based on adaptive subspace predictive control is proposed. In contrast with subspace predictive control algorithms previously proposed in the literature, the proposed strategy ensures offset-free tracking. The effectiveness of the proposed strategy is illustrated by simulating a wind turbine under normal operation and a fault in the hydraulic pitch system. Another control problem considered in this thesis is the design of the generator control system to ensure fault ride through for DFIG-based wind turbines. This requirement is dictated by recent grid codes, and it necessitates that the DFIG should be connected to the grid and capable of providing reactive power support during large voltage dips. This is challenging for DFIG-based wind turbines due to their partially rated power converters. In this thesis, a novel control strategy, based on using model predictive control and a dynamic series resistance protection scheme, is proposed to ensure fault ride through requirement.
- ItemOpen AccessModelling, simulation and identification of induction machines in continuous and discrete time(2004) Pan, Juntao; Westwick, David; Nowicki, Ed P.
- ItemOpen AccessA Multi-Purpose Continuum Robot for Minimally Invasive Surgery(2023-01-25) Esfandiari, Mojtaba; Sutherland, Garnette; Tavakoli, Mahdi; Westwick, David; Goldsmith, PeterThis thesis is about ''A Multi-Purpose Continuum Robot for Minimally Invasive surgery'' which consists of five chapters. It starts with an Introduction and Literature Review that studies some of the most famous surgical robotic systems and analyses their pros and cons. Chapter two has to do with the problem statement and challenges that need to be addressed while designing our specific surgical tool, and a CAD design of a flexible continuum robot will be done for our brain surgery application. In chapter three, the kinematic modeling of the proposed robot is analyzed by a new model based on the Euler spirals and the results are compared with conventional constant curvature models. In chapter four, a model predictive control algorithm is proposed that considers the input saturation constraints on robot actuators. Finally, a discussion and conclusion will be provided in chapter five.
- ItemOpen AccessNeural-adaptive control of coker-off boilers in the heavy-oil industry(2011) Mahmoodi Takaghaj, Sanaz; Macnab, Chris; Westwick, David
- ItemOpen AccessA New Length-Based Algebraic Multigrid Clustering Algorithm(Hindawi Publishing Corporation, 2012-02-25) Rakai, Logan; Farshidi, Amin; Behjat, Laleh; Westwick, David
- ItemOpen AccessNonlinear and Intelligent Control of a Class of Uncertain Systems with Unknown Disturbance Sources(2022-01) Bagheri, Pedram; Behjat, Laleh; Sun, Qiao; Dimitrov, Vassil; Pieper, Jeffrey; Westwick, David; Shi, YangThe existence of phenomena such as uncertainties, unknown disturbance sources, input saturation, and non-affine inputs makes an interesting and yet challenging control problem. Taking a nonlinear approach to such problems compounds the complexities as superposition cannot be utilized. One cannot address each of the aforementioned phenomena in isolation and prove stability in combination. This thesis aims to develop novel nonlinear controllers for a class of uncertain and non-affine systems with unknown disturbance sources. To apply the control methods developed in this thesis to a real-world system, variable-speed variable-pitch wind turbines are used in the majority of this thesis. The reason is the complexity of their dynamics such as uncertainties, unknown disturbance source, and non-affine inputs, which make them a perfect candidate for a nonlinear control design leading to performance with higher efficiency. In doing so, in the first chapter of this thesis, an introduction to the current control problem is presented; the second chapter discusses the existing methods in the literature along with existing gaps for improvements. The continuation of this thesis is paper-based composed of four publications where each addresses a complexity associated with the control of nonlinear systems. In Paper 1, a combination of adaptive and Nussbaum-type-function-based controllers is proposed to tackle uncertainties and disturbance sources as well as the non-affine dynamics. Paper 2 builds upon what is developed in Paper 1 and tackles some of the inadequacies; more specifically, the proposed adaptive laws developed in Paper 1 are made more robust. In addition, asymptotic stability is achieved in both torque and pitch phases. In Paper 3, input saturation is added to the existing unfavorable phenomena such as uncertainties, unknown disturbance sources, and non-affine dynamics. In paper 4, as alluded earlier, although wind turbines are used in the previous chapters, the proposed approaches in this thesis can be applied to a wide range of nonlinear systems with uncertainties and unknown disturbance sources. Hence, to showcase this, the control methodology in this thesis is used to develop a novel way to improve the modeling and control of flexible manipulators. Finally, in the last chapter, a summary of the contributions and suggestions for further research on the current topic are presented.