M.Ing. (Electrical & Electronic Engineering) / Please refer to full text to view abstract

This paper sheds light on the differences and similarities in natural gas trading at the National Balancing Point in the UK and the Henry Hub located in the US. For this, we analyze traders' expectations and implement a mechanical forecasting model that allows traders to predict future spot prices. Based on this, we compute the deviations between expected and realized spot prices and analyze possible reasons and dependencies with other market variables. Overall, the mechanical predictor performs well, but a small forecast error remains which can not be characterized by the explanatory variables included.

The first experimental demonstration of feedback suppression of rotating external kink modes near the ideal wall limit in a tokamak using Kalman filtering to discriminate the n = 1 kink mode from background noise is reported. In order to achieve the highest plasma pressure limits in tokamak fusion experiments, feedback stabilization of long-wavelength, external instabilities will be required, and feedback algorithms will need to distinguish the unstable mode from noise due to other magnetohydrodynamic activity. When noise is present in measurements of a system, a Kalman filter can be used to compare the measurements with an internal model, producing a realtime, optimal estimate for the system's state. For the work described here, the Kalman filter contains an internal model that captures the dynamics of a rotating, growing instability and produces an estimate for the instability's amplitude and spatial phase. On the High Beta Tokamak-Extended Pulse (HBT-EP) experiment, the Kalman filter algorithm is implemented using a set of digital, field-programmable gate array controllers with 10 microsecond latencies. The feedback system with the Kalman filter is able to suppress the external kink mode over a broad range of spatial phase angles between the sensed mode and applied control field, and performance is robust at noise levels that render feedback with a classical, proportional gain algorithm ineffective. Scans of filter parameters show good agreement between simulation and experiment, and feedback suppression and excitation of the kink mode are enhanced in experiments when a filter made using optimal parameters from the experimental scans is used.

The purpose of online parameter learning and modeling is to validate and restore the properties of a structure based on legitimate observations. Online parameter learning assists in determining the unidentified characteristics of a structure by offering enhanced predictions of the vibration responses of the system. From the utilization of modeling, the predicted outcomes can be produced with a minimal amount of given measurements, which can be compared to the true response of the system. In this simulation study, the Kalman filter technique is used to produce sets of predictions and to infer the stiffness parameter based on noisy measurement. From this, the performance of online parameter identification can be tested with respect to different noise levels. This research is based on simulation work showcasing how effective the Kalman filtering techniques are in dealing with analytical uncertainties of data. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

The use of small satellites to measure the native electric field of the earth has historically presented many problems as a result of the generally modest pointing capabilities of small satellites. In spite of this, the cost of small satellites makes them ideal for just such scientic missions. This thesis details many of the constraints of electric field measuring missions as well as the requirements on any spacecraft designed to accomplish such. The data from a small sounding rocket mission is then analyzed and its usefulness discussed. Possible other methods for use are also discussed.

Sensors are getting smaller, inexpensive and sophisticated, with an increased availability. Compared to 25 years ago, an object tracking system now can easily achieve twice the accuracy, a much larger coverage and fault tolerance, without any significant changes in the overall cost. This is possible by simply employing more than just one sensor and processing measurements from individual sensors sequentially (or even in a batch form). %This is the centralized scheme of multi-sensor target tracking wherein the sensors send their individual detections to a central facility, where tracking related tasks such as data association, filtering, and track management etc. are performed. This is also perhaps the simplest solution for a multi-sensor approach and also optimal in the sense of minimum mean square error (MMSE) among all other multi-sensor scenario. In sophisticated sensors, the number of detections can reach thousands in a single frame. The communication and computation load for gathering all such detections at the fusion center will hamper the system's performance while also being vulnerable to faults. A better solution is a distributed architecture wherein the individual sensors are equipped with processing capabilities such that they can detect measurements, extract clutter, form tracks and transmit them to the fusion center. The fusion center now fuses tracks instead of measurements, due to which this scheme is commonly termed track-level fusion. In addition to sub-optimality, the track-level fusion suffers from a very coarse problem, which occurs due to correlations between the tracks to be fused. Often, in realistic scenarios, the cross-correlations are unknown, without any means to calculate them. Thus, fusion cannot be performed using traditional methods unless extra information is transmitted from the fusion center. This thesis proposes a novel and generalized method of fusing any two probability density functions (pdf) such that a positive cross-correlation exists between them. In modern tracking systems, the tracks are essentially pdfs and not necessarily Gaussian. We propose harmonic mean density based fusion and prove that it obeys all the necessary requirements of being a viable fusion mechanism. We show that fusion in this case is a classical example of agreement between the fused and participating densities based on average $\chi^2$ divergence. Compared to other such fusion techniques in the literature, the HMD performs exceptionally well. Transmitting covariance matrices in distributed architecture is not always possible in cases for e.g. tactical and automotive systems. Fusion of tracks without the knowledge of uncertainty is another problem discussed in the thesis. We propose a novel technique for local covariance reconstruction at the fusion center with the knowledge of estimates and a vector of times when update has occurred at local sensor node. It has been shown on a realistic scenario that the reconstructed covariance converges to the actual covariance, in the sense of Frobenius norm, making fusion without covariance, possible. / Thesis / Doctor of Philosophy (PhD)

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The identification of a structural system deterministically or probabilistically is a topic of considerable interest and importance for its condition assessment and prediction. Many identification approaches, however, require the input which is not always available. Specifically, it may be impossible to know the input or, alternately, the measurement of the input is much more unreliable than the dynamic state measurement. Along these lines, engineers try to extract as much information as possible from the available output data to reduce the need for knowing the input. Three new methodologies are developed here to address this challenge. Initially, the input-parameter-state estimation capabilities of a novel unscented Kalman filter, for real time monitoring applications, is examined on both linear and nonlinear systems. The unknown input is estimated in two stages within each time step. Firstly, the predicted dynamic states and the system's parameters provide an estimation of the input. Secondly, the corrected with measurements (updated) dynamic states and parameters provide a final input estimation for the current time step. Subsequently, the estimation of the dynamic states, the parameters, and the input of systems subjected to wind loading is examined using a sequential Kalman filter. The procedure considers two Kalman filters in order to estimate initially the dynamic states using kinematic constraints, and subsequently the system parameters along with the input, in an online fashion. Finally, the input-parameter-state estimation capabilities of a new residual-based Kalman filter are examined for both complete and limited output information conditions. The filter is based on the residual of the predicted and measured dynamic state output, as well as on the residual of the system model estimation. The considered sensitivity analysis is developed using a real time sensitivity matrix formulated by the filtered dynamic states.