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You searched for +publisher:"University of Vermont" +contributor:("Paul D. Hines"). Showing records 1 – 3 of 3 total matches.

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University of Vermont

1. Rezaei, Pooya. Cascading Failure Risk Estimation and Mitigation in Power Systems.

Degree: PhD, Electrical Engineering, 2016, University of Vermont

Electricity is a critical component in our daily life. Because it is almost always available, we take it for granted. However, given the proper conditions, blackouts do happen every once in a while and can cause discomfort at a minimum, and a catastrophe in rare circumstances. The largest blackouts typically include cascading failures, which are sequences of interdependent outages. Although timely and effective operator intervention can often prevent a cascade from spreading, such interventions require ample situational awareness. The goals of this dissertation are twofold: to provide power system operators with insight into the risk of blackouts given the space of potential initiating outages, and to evaluate control systems that might mitigate cascading failure risk. Accordingly, this dissertation proposes a novel method to estimate cascading failure risk. It is shown that this method is at least two orders of magnitude faster in estimating risk, compared with a traditional Monte-Carlo simulation in two test systems including a large-scale real power grid model. This method allows one to find critical components in a system and suggests ideas for how to reduce blackout risk by preventive measures, such as adjusting initial dispatch of a system. In addition to preventive measures, it is also possible to use corrective control strategies to reduce blackout sizes. These methods could be used once the system is under stress (for example if some of the elements are overloaded) to stop a potential cascade before it unfolds. This dissertation focuses on a distributed receding horizon model predictive control strategy to mitigate overloads in a system, in which each node can only control other nodes in its local neighborhood. A distributed approach not only needs less communication and computation, but is also a more natural fit with modern power system operations, in which many control centers manage disjoint regional networks. In addition, a distributed controller may be more robust to random failures and attacks. A central controller benefits from perfect information, and thus provides the optimal solution. This dissertation shows that as long as the local neighborhood of the distributed method is large enough, distributed control can provide high quality solutions that are similar to what an omniscient centralized controller could achieve, but with less communication requirements (per node), relative to the centralized approach. Advisors/Committee Members: Paul D. Hines.

Subjects/Keywords: Electrical and Electronics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rezaei, P. (2016). Cascading Failure Risk Estimation and Mitigation in Power Systems. (Doctoral Dissertation). University of Vermont. Retrieved from https://scholarworks.uvm.edu/graddis/482

Chicago Manual of Style (16th Edition):

Rezaei, Pooya. “Cascading Failure Risk Estimation and Mitigation in Power Systems.” 2016. Doctoral Dissertation, University of Vermont. Accessed July 03, 2020. https://scholarworks.uvm.edu/graddis/482.

MLA Handbook (7th Edition):

Rezaei, Pooya. “Cascading Failure Risk Estimation and Mitigation in Power Systems.” 2016. Web. 03 Jul 2020.

Vancouver:

Rezaei P. Cascading Failure Risk Estimation and Mitigation in Power Systems. [Internet] [Doctoral dissertation]. University of Vermont; 2016. [cited 2020 Jul 03]. Available from: https://scholarworks.uvm.edu/graddis/482.

Council of Science Editors:

Rezaei P. Cascading Failure Risk Estimation and Mitigation in Power Systems. [Doctoral Dissertation]. University of Vermont; 2016. Available from: https://scholarworks.uvm.edu/graddis/482


University of Vermont

2. Ghanavati, Goodarz. Statistical Analysis of High Sample Rate Time-series Data for Power System Stability Assessment.

Degree: PhD, Electrical Engineering, 2015, University of Vermont

The motivation for this research is to leverage the increasing deployment of the phasor measurement unit (PMU) technology by electric utilities in order to improve situational awareness in power systems. PMUs provide unprecedentedly fast and synchronized voltage and current measurements across the system. Analyzing the big data provided by PMUs may prove helpful in reducing the risk of blackouts, such as the Northeast blackout in August 2003, which have resulted in huge costs in past decades. In order to provide deeper insight into early warning signs (EWS) of catastrophic events in power systems, this dissertation studies changes in statistical properties of high-resolution measurements as a power system approaches a critical transition. The EWS under study are increases in variance and autocorrelation of state variables, which are generic signs of a phenomenon known as critical slowing down (CSD). Critical slowing down is the result of slower recovery of a dynamical system from perturbations when the system approaches a critical transition. CSD has been observed in many stochastic nonlinear dynamical systems such as ecosystem, human body and power system. Although CSD signs can be useful as indicators of proximity to critical transitions, their characteristics vary for different systems and different variables within a system. The dissertation provides evidence for the occurrence of CSD in power systems using a comprehensive analytical and numerical study of this phenomenon in several power system test cases. Together, the results show that it is possible extract information regarding not only the proximity of a power system to critical transitions but also the location of the stress in the system from autocorrelation and variance of measurements. Also, a semi-analytical method for fast computation of expected variance and autocorrelation of state variables in large power systems is presented, which allows one to quickly identify locations and variables that are reliable indicators of proximity to instability. Advisors/Committee Members: Paul D. Hines, Taras I. Lakoba.

Subjects/Keywords: Autocorrelation function; Critical slowing down; Phasor measurement unit; Power system stability; Stochastic differential equations; Stochastic processes; Electrical and Electronics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ghanavati, G. (2015). Statistical Analysis of High Sample Rate Time-series Data for Power System Stability Assessment. (Doctoral Dissertation). University of Vermont. Retrieved from https://scholarworks.uvm.edu/graddis/333

Chicago Manual of Style (16th Edition):

Ghanavati, Goodarz. “Statistical Analysis of High Sample Rate Time-series Data for Power System Stability Assessment.” 2015. Doctoral Dissertation, University of Vermont. Accessed July 03, 2020. https://scholarworks.uvm.edu/graddis/333.

MLA Handbook (7th Edition):

Ghanavati, Goodarz. “Statistical Analysis of High Sample Rate Time-series Data for Power System Stability Assessment.” 2015. Web. 03 Jul 2020.

Vancouver:

Ghanavati G. Statistical Analysis of High Sample Rate Time-series Data for Power System Stability Assessment. [Internet] [Doctoral dissertation]. University of Vermont; 2015. [cited 2020 Jul 03]. Available from: https://scholarworks.uvm.edu/graddis/333.

Council of Science Editors:

Ghanavati G. Statistical Analysis of High Sample Rate Time-series Data for Power System Stability Assessment. [Doctoral Dissertation]. University of Vermont; 2015. Available from: https://scholarworks.uvm.edu/graddis/333

3. Saunders, Daniel Curtis. Wind Turbine Wake Interactions - Characterization of Unsteady Blade Forces and the Role of Wake Interactions in Power Variability Control.

Degree: PhD, Mechanical Engineering, 2017, University of Vermont

Growing concerns about the environmental impact of fossil fuel energy and improvements in both the cost and performance of wind turbine technologies has spurred a sharp expansion in wind energy generation. However, both the increasing size of wind farms and the increased contribution of wind energy to the overall electricity generation market has created new challenges. As wind farms grow in size and power density, the aerodynamic wake interactions that occur between neighboring turbines become increasingly important in characterizing the unsteady turbine loads and power output of the farm. Turbine wake interactions also impact variability of farm power generation, acting either to increase variability or decrease variability depending on the wind farm control algorithm. In this dissertation, both the unsteady vortex wake loading and the effect of wake interaction on farm power variability are investigated in order to better understand the fundamental physics that govern these processes and to better control wind farm operations to mitigate negative effects of wake interaction. The first part of the dissertation examines the effect of wake interactions between neighboring turbines on the variability in power output of a wind farm, demonstrating that turbine wake interactions can have a beneficial effect on reducing wind farm variability if the farm is properly controlled. In order to balance multiple objectives, such as maximizing farm power generation while reducing power variability, a model predictive control (MPC) technique with a novel farm power variability minimization objective function is utilized. The controller operation is influenced by a number of different time scales, including the MPC time horizon, the delay time between turbines, and the fluctuation time scales inherent in the incident wind. In the current research, a non-linear MPC technique is developed and used to investigate the effect of three time scales on wind farm operation and on variability in farm power output. The goal of the proposed controller is to explore the behavior of an "ideal" farm-level MPC controller with different wind, delay and horizon time scales and to examine the reduction of system power variability that is possible in such a controller by effective use of wake interactions. The second part of the dissertation addresses the unsteady vortex loading on a downstream turbine caused by the interaction of the turbine blades with coherent vortex structures found within the upstream turbine wake. Periodic, stochastic, and… Advisors/Committee Members: Jeffrey S. Marshall, Paul D. Hines.

Subjects/Keywords: blade lift; model predictive control; power variability; vortex-body interaction; vortex cutting; wind farms; Mechanical Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Saunders, D. C. (2017). Wind Turbine Wake Interactions - Characterization of Unsteady Blade Forces and the Role of Wake Interactions in Power Variability Control. (Doctoral Dissertation). University of Vermont. Retrieved from https://scholarworks.uvm.edu/graddis/745

Chicago Manual of Style (16th Edition):

Saunders, Daniel Curtis. “Wind Turbine Wake Interactions - Characterization of Unsteady Blade Forces and the Role of Wake Interactions in Power Variability Control.” 2017. Doctoral Dissertation, University of Vermont. Accessed July 03, 2020. https://scholarworks.uvm.edu/graddis/745.

MLA Handbook (7th Edition):

Saunders, Daniel Curtis. “Wind Turbine Wake Interactions - Characterization of Unsteady Blade Forces and the Role of Wake Interactions in Power Variability Control.” 2017. Web. 03 Jul 2020.

Vancouver:

Saunders DC. Wind Turbine Wake Interactions - Characterization of Unsteady Blade Forces and the Role of Wake Interactions in Power Variability Control. [Internet] [Doctoral dissertation]. University of Vermont; 2017. [cited 2020 Jul 03]. Available from: https://scholarworks.uvm.edu/graddis/745.

Council of Science Editors:

Saunders DC. Wind Turbine Wake Interactions - Characterization of Unsteady Blade Forces and the Role of Wake Interactions in Power Variability Control. [Doctoral Dissertation]. University of Vermont; 2017. Available from: https://scholarworks.uvm.edu/graddis/745

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