A Model-Based Approach for Small-Signal Stability Assessment of Unbalanced Power Systems

In this paper, a modeling technique for small-signal stability assessment of unbalanced power systems is presented. Since power distribution systems are inherently unbalanced, due to its lines and loads characteristics, and the penetration of distributed generation into these systems is increasing nowadays, such a tool is needed in order to ensure a secure and reliable operation of these systems. The main contribution of this paper is the development of a phasor-based model for the study of dynamic phenomena in unbalanced power systems. Using an assumption on the net torque of the generator, it is possible to precisely define an equilibrium point for the phasor model of the system, thus enabling its linearization around this point, and, consequently, its eigenvalue/eigenvector analysis for small-signal stability assessment. The modeling technique presented here was compared to the dynamic behavior observed in ATP simulations and the results show that, for the generator and controller models used, the proposed modeling approach is adequate and yields reliable and precise results.

The contribution of a murine CNS-TB model for the understanding of the host-pathogen interactions in the formation of granulomas

Central nervous system (CNS) tuberculosis (TB) is the most severe form of TB, characterized morphologically by brain granulomas and tuberculous meningitis (TBM). Experimental strategies for the study of the host-pathogen interaction through the analysis of granulomas and its intrinsic molecular mechanisms could provide new insights into the neuropathology of TB. To verify whether cerebellar mycobacterial infection induces the main features of the disease in human CNS and better understand the physiological mechanisms underlying the disease, we injected bacillus Calmette-Guerin (BCG) into the mouse cerebellum. BCG-induced CNS-TB is characterized by the formation of granulomas and TBM, a build up of bacterial loads in these lesions, and microglial recruitment into the lesion sites. In addition, there is an enhanced expression of signaling molecules such as nuclear factor-kappa B (NF-kappa B) and there is a presence of inducible nitric oxide synthase (iNOS) in the lesions and surrounding areas. This murine model of cerebellar CNS-TB was characterized by cellular and biochemical immune responses typically found in the human disease. This model could expand our knowledge about granulomas in TB infection of the cerebellum, and help characterize the physiological mechanisms involved with the progression of this serious illness that is responsible for killing millions people every year. (C) 2012 Elsevier B.V. All rights reserved.

In-Flight Collision Avoidance Controller Based Only on OS4 Embedded Sensors

The major goal of this research was the development and implementation of a control system able to avoid collisions during the flight for a mini-quadrotor helicopter, based only on its embedded sensors without changing the environment. However, it is important to highlight that the design aspects must be seriously considered in order to overcome hardware limitations and achieve control simplification. The controllers of a UAV (Unmanned Aerial Vehicle) robot deal with highly unstable dynamics and strong axes coupling. Furthermore, any additional embedded sensor increases the robot total weight and therefore, decreases its operating time. The best balance between embedded electronics and robot operating time is desired. This paper focuses not only on the development and implementation of a collision avoidance controller for a mini-robotic helicopter using only its embedded sensors, but also on the mathematical model that was essential for the controller developing phases. Based on this model we carried out the development of a simulation tool based on MatLab/Simulink that was fundamental for setting the controllers' parameters. This tool allowed us to simulate and improve the OS4 controllers in different modeled environments and test different approaches. After that, the controllers were embedded in the real robot and the results proved to be very robust and feasible. In addition to this, the controller has the advantage of being compatible with future path planners that we are developing.

Closed-loop stable model predictive control of integrating systems with dead time

Model predictive control (MPC) applications in the process industry usually deal with process systems that show time delays (dead times) between the system inputs and outputs. Also, in many industrial applications of MPC, integrating outputs resulting from liquid level control or recycle streams need to be considered as controlled outputs. Conventional MPC packages can be applied to time-delay systems but stability of the closed loop system will depend on the tuning parameters of the controller and cannot be guaranteed even in the nominal case. In this work, a state space model based on the analytical step response model is extended to the case of integrating time systems with time delays. This model is applied to the development of two versions of a nominally stable MPC, which is designed to the practical scenario in which one has targets for some of the inputs and/or outputs that may be unreachable and zone control (or interval tracking) for the remaining outputs. The controller is tested through simulation of a multivariable industrial reactor system. (C) 2012 Elsevier Ltd. All rights reserved.