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Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly

Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly organized into the following two parts: I) spatio-temporal wind power analysis for wind generation forecast and integration, and II) data mining and information fusion of synchrophasor measurements toward secure power grids. Part I is centered around wind power generation forecast and integration. First, a spatio-temporal analysis approach for short-term wind farm generation forecasting is proposed. Specifically, using extensive measurement data from an actual wind farm, the probability distribution and the level crossing rate of wind farm generation are characterized using tools from graphical learning and time-series analysis. Built on these spatial and temporal characterizations, finite state Markov chain models are developed, and a point forecast of wind farm generation is derived using the Markov chains. Then, multi-timescale scheduling and dispatch with stochastic wind generation and opportunistic demand response is investigated. Part II focuses on incorporating the emerging synchrophasor technology into the security assessment and the post-disturbance fault diagnosis of power systems. First, a data-mining framework is developed for on-line dynamic security assessment by using adaptive ensemble decision tree learning of real-time synchrophasor measurements. Under this framework, novel on-line dynamic security assessment schemes are devised, aiming to handle various factors (including variations of operating conditions, forced system topology change, and loss of critical synchrophasor measurements) that can have significant impact on the performance of conventional data-mining based on-line DSA schemes. Then, in the context of post-disturbance analysis, fault detection and localization of line outage is investigated using a dependency graph approach. It is shown that a dependency graph for voltage phase angles can be built according to the interconnection structure of power system, and line outage events can be detected and localized through networked data fusion of the synchrophasor measurements collected from multiple locations of power grids. Along a more practical avenue, a decentralized networked data fusion scheme is proposed for efficient fault detection and localization.
ContributorsHe, Miao (Author) / Zhang, Junshan (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Hedman, Kory (Committee member) / Si, Jennie (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
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Description
This thesis concerns the impact of energy storage on the power system. The rapidly increasing integration of renewable energy source into the grid is driving greater attention towards electrical energy storage systems which can serve many applications like economically meeting peak loads, providing spinning reserve. Economic dispatch is performed with

This thesis concerns the impact of energy storage on the power system. The rapidly increasing integration of renewable energy source into the grid is driving greater attention towards electrical energy storage systems which can serve many applications like economically meeting peak loads, providing spinning reserve. Economic dispatch is performed with bulk energy storage with wind energy penetration in power systems allocating the generation levels to the units in the mix, so that the system load is served and most economically. The results obtained in previous research to solve for economic dispatch uses a linear cost function for a Direct Current Optimal Power Flow (DCOPF). This thesis uses quadratic cost function for a DCOPF implementing quadratic programming (QP) to minimize the function. A Matlab program was created to simulate different test systems including an equivalent section of the WECC system, namely for Arizo-na, summer peak 2009. A mathematical formulation of a strategy of when to charge or discharge the storage is incorporated in the algorithm. In this thesis various test cases are shown in a small three bus test bed and also for the state of Arizona test bed. The main conclusions drawn from the two test beds is that the use of energy storage minimizes the generation dispatch cost of the system and benefits the power sys-tem by serving the peak partially from stored energy. It is also found that use of energy storage systems may alleviate the loading on transmission lines which can defer the upgrade and expansion of the transmission system.
ContributorsGupta, Samir (Author) / Heydt, Gerald T (Thesis advisor) / Vittal, Vijay (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2012
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Description
This thesis is focused on the study of wind energy integration and is divided into two segments. The first part of the thesis deals with developing a reliability evaluation technique for a wind integrated power system. A multiple-partial outage model is utilized to accurately calculate the wind generation availability. A

This thesis is focused on the study of wind energy integration and is divided into two segments. The first part of the thesis deals with developing a reliability evaluation technique for a wind integrated power system. A multiple-partial outage model is utilized to accurately calculate the wind generation availability. A methodology is presented to estimate the outage probability of wind generators while incorporating their reduced power output levels at low wind speeds. Subsequently, power system reliability is assessed by calculating the loss of load probability (LOLP) and the effect of wind integration on the overall system is analyzed. Actual generation and load data of the Texas power system in 2008 are used to construct a test case. To demonstrate the robustness of the method, relia-bility studies have been conducted for a fairly constant as well as for a largely varying wind generation profile. Further, the case of increased wind generation penetration level has been simulated and comments made about the usability of the proposed method to aid in power system planning in scenarios of future expansion of wind energy infrastructure. The second part of this thesis explains the development of a graphic user interface (GUI) to demonstrate the operation of a grid connected doubly fed induction generator (DFIG). The theory of DFIG and its back-to-back power converter is described. The GUI illustrates the power flow, behavior of the electrical circuit and the maximum power point tracking of the machine for a variable wind speed input provided by the user. The tool, although developed on MATLAB software platform, has been constructed to work as a standalone application on Windows operating system based computer and enables even the non-engineering students to access it. Results of both the segments of the thesis are discussed. Remarks are presented about the validity of the reliability technique and GUI interface for variable wind speed conditions. Improvements have been suggested to enable the use of the reliability technique for a more elaborate system. Recommendations have been made about expanding the features of the GUI tool and to use it to promote educational interest about renewable power engineering.
ContributorsSinha, Anubhav (Author) / Heydt, Gerald T (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Ayyanar, Raja (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2012
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Description
As the world embraces a sustainable energy future, alternative energy resources, such as wind power, are increasingly being seen as an integral part of the future electric energy grid. Ultimately, integrating such a dynamic and variable mix of generation requires a better understanding of renewable generation output, in addition to

As the world embraces a sustainable energy future, alternative energy resources, such as wind power, are increasingly being seen as an integral part of the future electric energy grid. Ultimately, integrating such a dynamic and variable mix of generation requires a better understanding of renewable generation output, in addition to power grid systems that improve power system operational performance in the presence of anticipated events such as wind power ramps. Because of the stochastic, uncontrollable nature of renewable resources, a thorough and accurate characterization of wind activity is necessary to maintain grid stability and reliability. Wind power ramps from an existing wind farm are studied to characterize persistence forecasting errors using extreme value analysis techniques. In addition, a novel metric that quantifies the amount of non-stationarity in time series wind power data was proposed and used in a real-time algorithm to provide a rigorous method that adaptively determines training data for forecasts. Lastly, large swings in generation or load can cause system frequency and tie-line flows to deviate from nominal, so an anticipatory MPC-based secondary control scheme was designed and integrated into an automatic generation control loop to improve the ability of an interconnection to respond to anticipated large events and fluctuations in the power system.
ContributorsGanger, David (Author) / Vittal, Vijay (Thesis advisor) / Zhang, Junshan (Thesis advisor) / Hedman, Kory (Committee member) / Undrill, John (Committee member) / Arizona State University (Publisher)
Created2016