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Deng, Yi

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    Modulation, control, and applications of multilevel converters for power systems with high penetration of wind energy
    (Georgia Institute of Technology, 2016-05-20) Deng, Yi
    The proposed research focuses on developing modulation and control methods for multilevel converters so as to optimize their applications in wind energy generation and transmission. This dissertation first establishes the inherent relationship between the space vector modulation (SVM) and a phase-voltage modulation technique (called the nearest-level modulation): the two modulation methods are functionally equivalent. Consequently, a simplified SVM scheme for multilevel converters is proposed, which is independent of the level number of the converter and for the first time achieves the same easy implementation as phase-voltage modulation techniques. The three-level active neutral-point-clamped (ANPC) converter is well suited to control high-power wind turbine generators, but suffers from unequal power loss distribution among its semiconductor devices. This dissertation proposes a new modulation scheme, called the adaptive doubled frequency PWM (ADF-PWM), to achieve the power loss balancing control for the ANPC converter. In applications of high voltage direct current (HVDC) transmission systems connecting large wind farms over a long distance to a utility network, the modular multilevel converter (MMC) is the best choice, because of its modularity and scalability to meet any voltage level requirements. This dissertation proposes an optimized control method for the MMC based on the proposed simplified SVM scheme, which significantly improves the capacitor voltage balancing and circulating current suppression.
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    Causal Discovery Methods for Climate Networks
    (Georgia Institute of Technology, 2010-12-20) Ebert-Uphoff, Imme ; Deng, Yi
    This paper suggests new methods for the development of network models in climate research. Current climate networks, first introduced in 2004 by Tsonis and Roebber, define network edges based on correlation of node pairs, resulting in a correlation network. The key idea of this paper is to introduce techniques from causal reasoning to derive climate networks, specifically constraint based structure learning. This approach is expected to yield networks that better represent the causal connections in the network, by containing less edges and with all causal pathways still present. The anticipated advantage of a network with less edges is a more manageable model size that makes it easier to gain new insights about causal relationships in the climate system. The goal of this paper is to provide researchers in the climate area with an intuitive understanding of the causal discovery process, specifically of constraint based structure learning. We review the basic principles of constraint based structure learning, namely how cause-and-effect relationships of variables can be learned from observational data using conditional independence tests. Tutorial-style examples illustrate this process. Finally, we review available algorithms and software packages from other disciplines that can be applied to derive climate networks. There are no simulation results provided in this paper (work in progress), thus we do not yet know how much reduction is achieved through this method compared to existing methods. However, applications of similar techniques for protein interaction modeling has yielded tremendous savings, making it possible to gain significant understanding of causal pathways from the obtained network graphs.