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Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

The continuous growth in power system electric grid by adding new substations lead to construct many new transmission lines, transformers, control devices, and circuit breakers to connect the capacity (generators) to the demand (loads). These components will have a very heavy influence on the performance of the electric grid. The renewable technical solutions for these issues can be found by robust algorithms which can give us a full picture of the current state of the electrical network by monitoring the behavior of phase and voltage magnitude. In this thesis, the major idea is to implement several algorithms including weighted least square, extend kalman filter, and interior point method in three different electrical networks including IEEE 14, 30, and 118 to compare the performance of these algorithms which is represented by the behavior of phases and magnitude voltages as well as minimize the residual of the balance load flow real time measurements to distinguish which one is more robust. Also to have a particular understanding of the comparison between unconstraint and constraint algorithms.

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<p dir="ltr">Resilient engine operation hugely depends on proper functioning of the engine’s sensors, enabling efficient feedback control of the engine systems operation. When the sensors on the engine measure a physical quantity incorrectly, it leads the engine control system to determine that the sensor measuring the physical quantity has failed. This failure may be attributed to a sensor stick failure, bias failure, drift failure, or failure occurring due to physical wear and tear of the sensor. Failure of crucial engine sensors may have adverse effects on engine operation, and in most cases leading into a limp home mode or a torque limitation mode. This affects the engine performance and efficiency. The engine under study in this work is a medium duty marine engine with diesel fuel. Sensor failures in the middle of a marine operation can hugely impact its mission. Therefore, fault tolerant control systems are essential to counter these challenges occurring due to sensor failures. In this thesis, an advanced nonlinear fault detection and state estimation algorithm is developed and implemented on a GT-Power engine model, employing a sophisticated co-simulation approach. The focus is on a 6.7L Cummins diesel engine, for which a detailed nonlinear state space model is constructed. This model accurately replicates critical engine parameters, such as pressures, temperatures, and engine speed, by integrating various submodels. These sub-models estimate key parameters like cylinder inlet charge flow, valve flow, cylinder outlet temperature, turbocharger turbine flow, and charge air cooler flow. To assess the model’s accuracy and reliability, it is rigorously validated against a truth reference GT-Power engine model. The results demonstrate exceptional performance, with the nonlinear model exhibiting a minimal percentage performance error of less than 5% under steady-state conditions and less than 15% during transient conditions. The core of the Fault Detection and State Estimation (FDSE) modules consists of a bank of Extended Kalman Filters (EKF). These filters are meticulously designed to estimate vital engine states, generate residuals, and assess these residuals even in the presence of process and measurement noise. This approach enables the detection of sensor faults and facilitates controller reconfiguration, ensuring the engine’s robustness in the face of unexpected sensor failures. Crucially, the nonlinear physics-based model serves as the foundation for the state transition functions utilized in the design of the observer bank. Residuals generated by the EKFs are evaluated using both fixed and adaptive thresholding techniques masking the sensor faults at the time step at which it is detected, ensuring robust performance not only in steady-state conditions but also during varying transient load conditions. To comprehensively evaluate the system’s resilience in practical scenarios, multiple sensor stuck failures are introduced into the GT-Power model. A software-in-the-loop co-simulation strategy is meticulously established, employing both the GT-Power truth reference engine model and the nonlinear Fault Detection and State Estimation (FDSE) model within the Simulink environment. This unique co-simulation approach provides a platform to assess the FDSE performance and its effect on engine performance in simulated sensor fault scenarios. The FDSE module is able to detect sensor failures which deviate at least 5% from their actual values. The percentage estimation error is less than 10% under steady state conditions and less than 20% under transient load conditions. Ultimately, this process creates analytical redundancy, not only forming the basis of state estimation but also empowering the engine to maintain its performance in the presence of sensor faults.</p>

<p> Motivated by the increasing need to monitor safety-critical systems subject to uncer-<br> tainties, a novel set-membership approach is proposed to estimate the state of a dynamical<br> system with unknown-but-bounded exogenous inputs. By fully utilizing the system struc-<br> tural information, the proposed algorithm can address both computational efficiency and<br> estimation accuracy without requiring restrictive conditions on the system. Particularly,<br> the system is first decomposed into the strongly observable subsystem and the weakly un-<br> observable subsystem. To make full use of the subsystem’s properties, a set-membership<br> observer based on the unknown input observer and an ellipsoidal set-membership observer<br> are designed for the two subsystems, respectively. Then, the resulting set estimates from<br> each subsystem are fused and transformed to obtain the set estimate for the original system,<br> which is guaranteed to bound the actual system state. The conditions for the boundedness<br> of the proposed set estimate are discussed, and the proposed set-membership observer is also<br> tested numerically using illustrative examples.</p>

Magnetic Resonance Imaging (MRI) is an important diagnostic tool for imaging soft tissue without the use of ionizing radiation. Moreover, through advanced signal processing, MRI can provide more than just anatomical information, such as estimates of tissue-specific physical properties. Signal processing lies at the very core of the MRI process, which involves input design, information encoding, image reconstruction, and advanced filtering. Based on signal modeling and estimation, it is possible to further improve the images, reduce artifacts, mitigate noise, and obtain quantitative tissue information. In quantitative MRI, different physical quantities are estimated from a set of collected images. The optimization problems solved are typically nonlinear, and require intelligent and application-specific algorithms to avoid suboptimal local minima. This thesis presents several methods for efficiently solving different parameter estimation problems in MRI, such as multi-component T2 relaxometry, temporal phase correction of complex-valued data, and minimizing banding artifacts due to field inhomogeneity. The performance of the proposed algorithms is evaluated using both simulation and in-vivo data. The results show improvements over previous approaches, while maintaining a relatively low computational complexity. Using new and improved estimation methods enables better tissue characterization and diagnosis. Furthermore, a sequence design problem is treated, where the radio-frequency excitation is optimized to minimize image artifacts when using amplifiers of limited quality. In turn, obtaining higher fidelity images enables improved diagnosis, and can increase the estimation accuracy in quantitative MRI.

This thesis looks at hardware algorithms that help reduce dynamic power dissipation in video encoder applications. Computational complexity of motion estimation and the data traffic between external memory and the video processing engine are two main reasons for large power dissipation in video encoders. While motion estimation may consume 50% to 70% of total video encoder power, the power dissipated in external memory such as the DDR SDRAM can be of the order of 40% of the total system power. Reducing power dissipation in video encoders is important in order to improve battery life of mobile devices such as the smart phones and digital camcorders. We propose hardware algorithms which extract only the important features in the video data to reduce the complexity of computations, communications and storage, thereby reducing average power dissipation. We apply this concept to design hardware algorithms for optimizing motion estimation matching complexity, and reference frame storage and access from the external memory. In addition, we also develop techniques to reduce searching complexity of motion estimation. First, we explore a set of adaptive algorithms that reduce average power dissipated due to motion estimation. We propose that by taking into account the macro-block level features in the video data, the average matching complexity of motion estimation in terms of number of computations in real-time hardwired video encoders can be significantly reduced when compared against traditional hardwired implementations, that are designed to handle most demanding data sets. Current macro-block features such as pixel variance and Hadamard transform coefficients are analyzed, and are used to adapt the matching complexity. The macro-block is partitioned based on these features to obtain sub-block sums, which are used for matching operations. Thus, simple macro-blocks, without many features can be matched with much less computations compared to the macro-blocks with complex features, leading to reduction in average power dissipation. Apart from optimizing the matching operation, optimizing the search operation is a powerful way to reduce motion estimation complexity. We propose novel search optimization techniques including (1) a center-biased search order and (2) skipping unlikely search positions, both applied in the context of real time hardware implementation. The proposed search optimization techniques take into account and are compatible with the reference data access pattern from the memory as required by the hardware algorithm. We demonstrate that the matching and searching optimization techniques together achieve nearly 65% reduction in power dissipation due to motion estimation, without any significant degradation in motion estimation quality. A key to low power dissipation in video encoders is minimizing the data traffic between the external memory devices such as DDR SDRAM and the video processor. External memory power can be as high as 50% of the total power budget in a multimedia system. Other than the power dissipation in external memory, the amount of data traffic is an important parameter that has significant impact on the system cost. Large memory traffic necessitates high speed external memories, high speed on-chip interconnect, and more parallel I/Os to increase the memory throughput. This leads to higher system cost. We explore a lossy, scalar quantization based reference frame compression technique that can be used to reduce the amount of reference data traffic from external memory devices significantly. In this scheme, the quantization is adapted based on the pixel range within each block being compressed. We show that the error introduced by the scalar quantization is bounded and can be represented by smaller number of bits compared to the original pixel. The proposed reference frame compression scheme uses this property to minimize the motion compensation related traffic, thereby improving the compression scheme efficiency. The scheme maintains a fixed compression ratio, and the size of the quantization error is also kept constant. This enables easy storage and retrieval of reference data. The impact of using lossy reference on the motion estimation quality is negligible. As a result of reduction in DDR traffic, the DDR power is reduced significantly. The power dissipation due to additional hardware required for reference frame compression is very small compared to the reduction in DDR power. 24% reduction in peak DDR bandwidth and 23% net reduction in average DDR power is achieved. For video sequences with larger motion, the amount of bandwidth reduction is even higher (close to 40%) and reduction in power is close to 30%.

<div>The electric power system of a deep space vehicle is mission-critical, and needs to operate autonomously because of high latency in communicating with ground-based mission control. Key tasks to be automated include managing loads under various physical constraints, continuously monitoring the system state to detect and locate faults, and efficiently responding to those faults. </div><div><br></div><div>This work focuses on three aspects for achieving autonomous, fault-tolerant operation in the dc power system of a spacecraft. First, a sequential procedure is proposed to estimate the node voltages and branch currents in the power system from erroneous sensor measurements. An optimal design for the sensor network is also put forth to enable reliable sensor fault detection and identification. Secondly, a machine-learning based approach that utilizes power-spectrum based features of the current signal is suggested to identify component faults in power electronic converters in the system. Finally, an optimization algorithm is set</div><div>forth that decides how to operate the power system under both normal and faulted conditions. Operational decisions include shedding loads, switching lines, and controlling battery charging. Results of case studies considering various faults in the system are presented.</div>

The thesis presents algorithms based on a linear algebraic solution for the identification of the parameters of the FIR system using only higher order statistics when only the output of the system corrupted by additive Gaussian noise is observed. All the traditional parametric methods of estimating the parameters of the system have been based on the 2nd order statistics of the output of the system. These methods suffer from the deficiency that they do not preserve the phase response of the system and hence cannot identify non-minimum phase systems. To circumvent this problem, higher order statistics which preserve the phase characteristics of a process and hence are able to identify a non-minimum phase system and also are insensitive to additive Gaussian noise have been used in recent years. Existing algorithms for the identification of the FIR parameters based on the higher order cumulants use the autocorrelation sequence as well and give erroneous results in the presence of additive colored Gaussian noise. This problem can be overcome by obtaining algorithms which do not utilize the 2nd order statistics. An existing relationship between the 2nd order and any Ith order cumulants is generalized to a relationship between any two arbitrary k, Ith order cumulants. This new relationship is used to obtain new algorithms for FIR system identification which use only cumulants of order > 2 and with no other restriction than the Gaussian nature of the additive noise sequence. Simulation studies are presented to demonstrate the failure of the existing algorithms when the imposed constraints on the 2nd order statistics of the additive noise are violated while the proposed algorithms perform very well and give consistent results. Recently, a new algebraic approach for parameter estimation method denoted the Linear Combination of Slices (LCS) method was proposed and was based on expressing the FIR parameters as a linear combination of the cumulant slices. The rank deficient cumulant matrix S formed in the LCS method can be expressed as a product of matrices which have a certain structure. The orthogonality property of the subspace orthogonal to S and the range space of S has been exploited to obtain a new class of algorithms for the estimation of the parameters of a FIR system. Numerical simulation studies have been carried out to demonstrate the good behaviour of the proposed algorithms. Analytical expressions for the covariance of the estimates of the FIR parameters of the different algorithms presented in the thesis have been obtained and numerical comparison has been done for specific cases. Numerical examples to demonstrate the application of the proposed algorithms for channel equalization in data communication and as an initial solution to the cumulant matching nonlinear optimization methods have been presented.