A Management Tool for the Replication of Operating Systems in Wireless Communication Networks

To simplify computer management, various administration systems based on wired connections adopt advanced techniques to manage software configuration. Nevertheless, the strong relation between hardware and software makes for an individualism of that management, besides penalizing computational mobility and ubiquity. All these issues lead to degradation of scalability, flexibility and the facility to install and maintain distributed applications. This article presents an environment for centralized wireless communication network management, named WSE-OS (Wireless Sharing Environment - Operating Systems): a model based on Virtual Desktop Infrastructure (VDI) which associates virtualization techniques and safe remote access systems to create a distributed architecture as a base for a managing system. WSE-OS is capable of accomplishing the replication of operating system images using wireless communication network, besides offering abstraction of hardware to its clients, making the management more flexible and independent of wired connections. Results obtained from this work indicate that WSE-OS allows disseminating, through a single software configuration, the execution of data related to operating system images in client computers. WSE-OS can also be used as a management tool for operating systems in a wireless network.

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

An efficient neural approach to economic load dispatch in power systems

A neural approach to solve the problem defined by the economic load dispatch in power systems is presented in this paper, Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements the ability of neural networks to realize some complex nonlinear function makes them attractive for system optimization the neural networks applyed in economic load dispatch reported in literature sometimes fail to converge towards feasible equilibrium points the internal parameters of the modified Hopfield network developed here are computed using the valid-subspace technique These parameters guarantee the network convergence to feasible quilibrium points, A solution for the economic load dispatch problem corresponds to an equilibrium point of the network. Simulation results and comparative analysis in relation to other neural approaches are presented to illustrate efficiency of the proposed approach.

Experience gained and difficulties in performing dynamic load test in composite piles made with steel rails

The results of dynamic instrumentation in two construction sites that used steel rails as pile foundation are presented in this paper. The first dynamic load tests were executed in piles made by single steel rails of the type TR32 and TR37. In the second group of dynamic tests, the piles were made by a composition of two and three steel rails of the same type TR37. The difficulties in placing the sensors, the effect of hammer blow eccentricity and the influence of the non-uniform welding along the pile length are presented in detail and discussed.

Influences of thermal and electric load fluctuations in the cogeneration attractiveness

The use of mean values of thermal and electric demand can be justifiable for synthesising the configuration and for estimating the economic results because it simplifies the analysis in a preliminary feasibility study of a cogeneration plant. For determining the cogeneration scheme that best fits the energetic needs of a process several cycles and combinations must be considered, and those technically feasible will be analysed according to economic models. Although interesting for a first approach, this procedure do not consider that the peaks and valleys present in the load patterns will impose additional constraints relatively to the equipment capacities. In this paper, the effects of thermal and electric load fluctuation to the cogeneration plant design were considered. An approach for modelling these load variability is proposed for comparing two competing thermal and electric parity competing schemes. A gas turbine associated to a heat recovery steam generator was then proposed and analysed for thermal- and electric-following operational strategies. Thermal-following option revealed to be more attractive for the technical and economic limits defined for this analysis. (c) 2006 Elsevier Ltd. All rights reserved.

Load compensation in four-wire electrical power systems

This work describes a methodology for power factor control and correction of the unbalanced currents in four-wire electric circuits. The methodology is based on the insertion of two compensation networks, one wye-grounded neutral and another in delta, in parallel to the load. The mathematical development has been proposed in previous work [3]. In this paper, however, the methodology was adapted to accept different power factors for the system to be compensated. on the other hand, the determination of the compensation susceptances is based on the instantaneous values of the load currents. The results are obtained using the MatLab - Simulink environment.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

Methodology for load compensation in four-wire electrical power systems based on instantaneous sampling

This work describes a methodology for power factor control and correction of the unbalanced currents in four-wire electric circuits. The methodology is based on the insertion of two compensation networks, one wye-grounded neutral and other in delta, in parallel to the load. The mathematical development has been proposed in previous work [3]. In this paper, however, the determination of the compensation susceptances is based on the instantaneous values of load currents. The results are obtained using the MatLab-Simulink enviroment

Optimal switch allocation for automatic load transfer in distribution substations

This work proposes a methodology for optimized allocation of switches for automatic load transfer in distribution systems in order to improve the reliability indexes by restoring such systems which present voltage classes of 23 to 35 kV and radial topology. The automatic switches must be allocated on the system in order to transfer load remotely among the sources at the substations. The problem of switch allocation is formulated as nonlinear constrained mixed integer programming model subject to a set of economical and physical constraints. A dedicated Tabu Search (TS) algorithm is proposed to solve this model. The proposed methodology is tested for a large real-life distribution system. © 2011 IEEE.

Short-term multinodal load forecasting in distribution systems using general regression neural networks

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE.

Evaluating serial ZigBee devices for application in wireless networked control systems

Networked control systems (NCS) are distributed control system where the sensors, actuators and controllers are physically separated and connected through communication networks. NCS represent the evolution of networked control architectures providing greater modularity and control decentralization, ease maintenance and diagnosis and lower cost of implementation. A recent trend in this research topic is the development of NCS using wireless networks (WNCS) enabling interoperability between existing wired and wireless systems. This paper evaluates a serial RS-232 ZigBee device as a wireless sensor link in NCS. In order to support this investigation, relevant performance metrics for wireless control applications such as jitter, time delay and messages lost are highlighted and calculated to evaluate the device capabilities. In addition the control performance of an implemented motor control system using the device is analyzed. Experimental results led to the conclusion that serial RS-232 ZigBee devices can be used to implement WNCS and the use of this device delay information in the PID controller discretization can improve the control performance of the system. © 2012 IEEE.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

A contribuition to the study of channel coding in wireless communication systems

Pós-graduação em Engenharia Elétrica - FEIS

MODEL-BASED COMPENSATION FOR BURST MESSAGE LOSS IN WIRELESS NETWORKED CONTROL SYSTEMS: EXPERIMENTAL RESULTS

A recent trend in networked control systems (NCSs) is the use of wireless networks enabling interoperability between existing wired and wireless systems. One of the major challenges in these wireless NCSs (WNCSs) is to overcome the impact of the message loss that degrades the performance and stability of these systems. Moreover, this impact is greater when dealing with burst or successive message losses. This paper discusses and presents the experimental results of a compensation strategy to deal with this burst message loss problem in which a NCS mathematical model runs in parallel with the physical process, providing sensor virtual data in case of packet losses. Running in real-time inside the controller, the mathematical model is updated online with real control signals sent to the actuator, which provides better reliability for the estimated sensor feedback (virtual data) transmitted to the controller each time a message loss occurs. In order to verify the advantages of applying this model-based compensation strategy for burst message losses in WNCSs, the control performance of a motor control system using CAN and ZigBee networks is analyzed. Experimental results led to the conclusion that the developed compensation strategy provided robustness and could maintain the control performance of the WNCS against different message loss scenarios.