Future smart microgrids need increased flexibility and intelligence in control and optimization to maintain a generation-load balance. This concern becomes more significant today because of lack of conventional Automatic Generation Control (AGC) and spinning reserves which introduce new issues for providing ancillary services. Moreover, due to increasing renewable energy penetration in power systems, conventional controllers may be unable to maintain the system stability. In response to this issue, this paper presents an intelligent control algorithm using fuzzy logic and particle swarm optimization (PSO). Furthermore, the effect of Demand Response (DR) in continuously balancing generation and demand, when the output from wind and photovoltaic (PV) varies naturally, is proposed. Simulation results are examined on an islanded microgrid case study. The performance of the proposed controller is compared with conventional control design and the effect of DR in fast power compensation is proved.
This paper proposes a new genetic algorithm codification for distribution network restoration. Restoration is a multi-constraint combinatorial non-linear optimization problem for which conventional mathematical programming techniques become computationally very costly. Heuristic approaches have been used to tackle this problem. A codification previously used in the literature for distribution network reconfiguration is modified in order to be applied to power system restoration. To check its validity it is tested on a 33 bus test system. The outcomes are compared to those of previous optimization approaches proving to be computationally efficient and showing good results.
A TE-pass polarizer that uses metal vias for absorption of TM light is proposed. A 100-micron device can achieve 20dB extinction ratio with 0.23dB insertion loss. The polarizer is compatible with standard silicon-on-insulator foundry processes.
Despite the fact that robots have reached a high level of autonomy in recent years, the need for human presence in certain situations is still essential especially in search and rescue operations. The human extends the robots capabilities using current technologies. While current robotic devices are able to navigate, locate, and map search and rescue areas, some interventions require high degree of dexterity and information exchange that implies cooperation between the agents intervening in the area - human and/or robots. This paper presents a framework for modelling the coordination between human responders and robots in search and rescue scenarios using Decentralised Multi-agent Partially Observable Markov Decision Processes (Dec-MPOMDP). In this framework the human is treated as an intelligent agent with separate observations and actions that are communicated with the remain team (human and robots) to reach the level of synergy required to accomplish joined tasks.
In this paper, we develop haptic interface system for bilateral shared autonomous systems for unmanned vehicles over open communication networks. The proposed bilateral shared interface system allows human user to control, navigate and interact with dynamic remote environments without using vision systems in remote robot systems. The design combines shared control algorithms with virtual impedance force field. The design reflects interaction forces mapped by virtual impedance force field to the human operator by haptic device. Experiment results on laboratory illustrate the effectiveness of the proposed method for real-time applications. The key feature of the proposed haptic interface is that the interaction model only uses laser technology equipped with the slave MAVs
In this paper, we propose a method for mapping images from a capsule-based endoscope in a way that is more informative to physicians: the technique uses visual/inertial-based data fusion to obtain a 3D map of the lumen from capsule images, also paving the way for the implementation of a path planning and autonomous locomotion and inspection. Capsule endoscopy is a non-invasive procedure for gastrointestinal diagnosis. It does not require sedation and it is comfortable and well-tolerated by patient. However, the problem with such procedure is that a huge number of images is collected, which require time to investigate and diagnose; furthermore, the capsule movement is not controlled leading, in some cases, to inaccurate diagnosis. In this context, a mapping of the lumen is required to guarantee a higher reliability of the inspection, enabling the medical doctor to evaluate all the parts of the lumen for a better diagnosis.
This paper presents a practical algorithm for navigating through an unknown environment while compactly mapping it in a triangulation-based data structure. The novelty in this work comes from coupling the well-known motion planning algorithm, the RRT, with a new mapping data structure, the Dynamic Triangulation Tree DTT. This composition is implemented to optimize the navigation of the robot used during the mapping process of the unknown environment. The performance is evaluated through simulations to show that the proposed planner is practical and easy to implement.
Carbon nanotubes were synthesized by Low Pressure Chemical Vapor Deposition (LPCVD) method at 700?C. Here acetylene gas was used as precursor gas with the annealed (at 500?C) Fe catalyst was prepared by coating with magnetron sputtering on Si substrate. Vertically aligned CNTs grew with the catalyst annealing time in the range of 3 to 15 min. Reactions conditions such as catalyst annealing time and precursor gas pressure were altered in order to study their effect on the resulting CNTs. Raman spectra confirmed the presence of SWCNTs in the synthesized CNTs. Catalyst annealing time of 15 min gave rise to the formation of more SWCNTs than in the case of the lower annealing time. The study revealed that the increase in the catalyst annealing time causes an increase on the SWCNTs growth.
This paper investigates the effect of stress on graphene and silicon based strain sensors. Graphene is a one atom thick layer of carbon atoms that shows unique electrical and mechanical properties due to its small thickness. We have conducted a detailed study of the design of a strain sensor based on graphene. We present the results of our simulation model developed on COMSOL multiphysics. A comparison showed that the sensitivity of graphene to stress is about five times more than that of silicon.
We discuss fabrication and characterization of TSVs filled with carbon nano-materials (CNM) for dual function of sensing and vertical interconnect for hostile environment applications (Corrosive High Temperature and Pressure). Nano-composites, made by functionalization of CNTs were integrated using dispersion in epoxy resin to fill up the TSVs and provide sensing surface. The results reveal ability for the nano-composite to fill vias with electrical conductivity path and sensing established through the wafer backside.
