University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Distributed Optimal Active Power Control of Multiple Generation Systems

Abstract: A novel coordinated power controller design framework is proposed to optimize the active power output of multiple generators in a distributed network. Each bus in the distributed generation systems includes two function modules: a distributed economic dispatch (DED) module and a cooperative control (CC) module. By virtue of the distributed consensus theory, a DED algorithm is proposed and utilized to calculate the optimal active power generation references for each generator. The CC module receives and tracks the active power generation references such that the generation–demand balance is guaranteed at minimum operating cost while satisfying all generation constraints. The distributed control and management strategies enhance the redundancy and the plug-and-play capability in microgrids. Optimal properties and convergence rates of the proposed distributed algorithms are strictly proved. Simulation studies further demonstrate the effectiveness of the proposed approach.

Multitask Spectral Clustering by Exploring Intertask Correlation

Abstract: Clustering, as one of the most classical research problems in pattern recognition and data mining, has been widely explored and applied to various applications. Due to the rapid evolution of data on the Web, more emerging challenges have been posed on traditional clustering techniques: 1) correlations among related clustering tasks and/or within individual task are not well captured; 2) the problem of clustering out-of-sample data is seldom considered; and 3) the discriminative property of cluster label matrix is not well explored. In this paper, we propose a novel clustering model, namely multitask spectral clustering (MTSC), to cope with the above challenges. Specifically, two types of correlations are well considered: 1) intertask clustering correlation, which refers the relations among different clustering tasks and 2) intratask learning correlation, which enables the processes of learning cluster labels and learning mapping function to reinforce each other. We incorporate a novel $\boldsymbol {\ell }_{\boldsymbol {2,p}}$ -norm regularizer to control the coherence of all the tasks based on an assumption that related tasks should share a common low-dimensional representation. Moreover, for each individual task, an explicit mapping function is simultaneously learnt for predicting cluster labels by mapping features to the cluster label matrix. Meanwhile, we show that the learning process can naturally incorporate discriminative information to further improve clustering performance. We explore and discuss the relationships between our proposed model and several representative clustering techniques, including spectral clustering, $k$ -means and discriminative $k$ -means. Extensive experiments on various real-world datasets illustrate the advantage of the proposed MTSC model compared to state-of-the-art clustering approaches.

Distributed Finite-Time Voltage and Frequency Restoration in Islanded AC Microgrids

Abstract: This paper investigates the distributed finite-time restoration of inverter voltage and frequency terms in an islanded ac microgrid. Formulating networked inverters of ac microgrids as a cooperative multiagent system, the voltage and frequency restoration can be cast as synchronization problems, while the active power sharing can be viewed as a consensus problem. One popular distributed control approach is the neighbor-based linear consensus protocol, where the consensus at the frequency and voltage set points is achieved over an infinite-time horizon with an exponential convergence. To achieve accelerated convergence and better disturbance rejection properties, a distributed finite-time control protocol, based on feedback linearization approach, is proposed for voltage restoration, which synchronizes the voltage term at each inverter to the reference value in finite time period. Then, a finite-time control protocol for both frequency restoration and active power sharing problems is proposed to synchronize the microgrid frequency to the nominal value, and share the active power among inverters based on their ratings in a finite time. Rigorous Lyapunov proofs are provided to establish the upper bounds on the convergence times. Numerical simulation studies verify the effectiveness of the proposed control protocols.

Optimality conditions for maximizations of set-valued functions

Abstract: The maximization with respect to a cone of a set-valued function into possibly infinite dimensions is defined, and necessary and sufficient optimality conditions are established. In particular, an analogue of the Fritz John necessary optimality conditions is proved using a notion of derivative defined in terms of tangent cones.