A Gradient-Type Method For Real-Time State Estimation Of Water Distribution Networks

Drinking water distribution networks risk exposure to malicious or accidental contamination. Several levels of responses are conceivable. One of them consists to install a sensor network to monitor the system on real time. Once a contamination has been detected, this is also important to take appropriate counter-measures. In the SMaRT-OnlineWDN project, this relies on modeling to predict both hydraulics and water quality. An online model use makes identification of the contaminant source and simulation of the contaminated area possible. The objective of this paper is to present SMaRT-OnlineWDN experience and research results for hydraulic state estimation with sampling frequency of few minutes. A least squares problem with bound constraints is formulated to adjust demand class coefficient to best fit the observed values at a given time. The criterion is a Huber function to limit the influence of outliers. A Tikhonov regularization is introduced for consideration of prior information on the parameter vector. Then the Levenberg-Marquardt algorithm is applied that use derivative information for limiting the number of iterations. Confidence intervals for the state prediction are also given. The results are presented and discussed on real networks in France and Germany.

Design and Analysis of Optical Interconnection Networks for a Dataflow Parallel Computer

This work shows the design, simulation, and analysis of two optical interconnection networks for a Dataflow parallel computer architecture. To verify the optical interconnection network performance on the Dataflow architecture, we have analyzed the load balancing among the processors during the parallel programs executions. The load balancing is a very important parameter because it is directly associated to the dataflow parallelism degree. This article proves that optical interconnection networks designed with simple optical devices can provide efficiently the dataflow requirements of a high performance communication system.

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.

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors

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. This paper presents a novel approach to solve robust parameter estimation problem for nonlinear model with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

Application of neural networks to identify features of dynamical grounding systems

The accurate identification of features of dynamical grounding systems are extremely important to define the operational safety and proper functioning of electric power systems. Several experimental tests and theoretical investigations have been carried out to obtain characteristics and parameters associated with the technique of grounding. The grounding system involves a lot of non-linear parameters. This paper describes a novel approach for mapping characteristics of dynamical grounding systems using artificial neural networks. The network acts as identifier of structural features of the grounding processes. So that output parameters can be estimated and generalized from an input parameter set. The results obtained by the network are compared with other approaches also used to model grounding systems.

Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parametes and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.

A novel approach to robust parameter estimation using neurofuzzy systems

A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for model parameters. Techniques of fuzzy logic control lead the network to its equilibrium points. Simulated examples are presented as an illustration of the proposed technique. The result represent a significant improvement over previously proposed methods. (C) 1999 IMACS/Elsevier B.V. B.V. All rights reserved.

Multi-objective evolutionary particle swarm optimization in the assessment of the impact of distributed generation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Time-series-based maximization of distributed wind power generation integration

Energy policies and technological progress in the development of wind turbines have made wind power the fastest growing renewable power source worldwide. The inherent variability of this resource requires special attention when analyzing the impacts of high penetration on the distribution network. A time-series steady-state analysis is proposed that assesses technical issues such as energy export, losses, and short-circuit levels. A multiobjective programming approach based on the nondominated sorting genetic algorithm (NSGA) is applied in order to find configurations that maximize the integration of distributed wind power generation (DWPG) while satisfying voltage and thermal limits. The approach has been applied to a medium voltage distribution network considering hourly demand and wind profiles for part of the U.K. The Pareto optimal solutions obtained highlight the drawbacks of using a single demand and generation scenario, and indicate the importance of appropriate substation voltage settings for maximizing the connection of MPG.

Levenberg-Marquardt application to two-phase nonlinear parameter estimation for finned-tube coil evaporators

A procedure for calculation of refrigerant mass flow rate is implemented in the distributed numerical model to simulate the flow in finned-tube coil dry-expansion evaporators, usually found in refrigeration and air-conditioning systems. Two-phase refrigerant flow inside the tubes is assumed to be one-dimensional, unsteady, and homogeneous. In themodel the effects of refrigerant pressure drop and the moisture condensation from the air flowing over the external surface of the tubes are considered. The results obtained are the distributions of refrigerant velocity, temperature and void fraction, tube-wall temperature, air temperature, and absolute humidity. The finite volume method is used to discretize the governing equations. Additionally, given the operation conditions and the geometric parameters, the model allows the calculation of the refrigerant mass flow rate. The value of mass flow rate is computed using the process of parameter estimation with the minimization method of Levenberg-Marquardt minimization. In order to validate the developed model, the obtained results using HFC-134a as a refrigerant are compared with available data from the literature.

Evaluating distributed generation impacts with a multiobjective index

Evaluating the technical impacts associated with connecting distributed generation to distribution networks is a complex activity requiring a wide range of network operational and security effects to be qualified and quantified. One means of dealing with such complexity is through the use of indices that indicate the benefit or otherwise of connections at a given location and which could be used to shape the nature of the contract between the utility and distributed generator. This paper presents a multiobjective performance index for distribution networks with distributed generation which considers a wide range of technical issues. Distributed generation is extensively located and sized within the IEEE-34 test feeder, wherein the multiobjective performance index is computed for each configuration. The results are presented and discussed.

Using artificial neural networks for identification of electrical losses in transformers during the manufacturing phase

The paper describes a novel neural model to estimate electrical losses in transformer during the manufacturing phase. The network acts as an identifier of structural features on electrical loss process, so that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through experimental data taking into account core losses, copper losses, resistance, current and temperature. The results obtained in the simulations have shown that the developed technique can be used as an alternative tool to make the analysis of electrical losses on distribution transformer more appropriate regarding to manufacturing process. Thus, this research has led to an improvement on the rational use of energy.

Prediction via neural networks of the residual hydrogen peroxide used in photo-fenton processes for effluent treatment

This communication proposes the use of neural networks in the prediction of residual concentrations of hydrogen peroxide from the treatment of effluents through Advanced Oxidative Processes (AOP's), in particular, the photo-Fenton process. To verify the efficiency of the oxidative process, the Chemical Oxygen Demand (COD) parameter, the values of which may be modified by the presence of oxidizing agents such as residual hydrogen peroxide, is frequently taken in account. The analysis of the H2O2 interference was performed by spectrophotometry at 450 nm wavelength, via the monitoring of the reaction of ammonia with metavanadate. The results of the hydrogen peroxide residual concentration were modeled via a feedforward neural network, with the correlation coefficients between actual and predicted values above 0.96, indicating good prediction capacity.

Evaluating distributed time-varying generation through a multiobjective index

In the last decade, distributed generation, with its various technologies, has increased its presence in the energy mix presenting distribution networks with challenges in terms of evaluating the technical impacts that require a wide range of network operational effects to be qualified and quantified. The inherent time-varying behavior of demand and distributed generation (particularly when renewable sources are used), need to be taken into account since considering critical scenarios of loading and generation may mask the impacts. One means of dealing with such complexity is through the use of indices that indicate the benefit or otherwise of connections at a given location and for a given horizon. This paper presents a multiobjective performance index for distribution networks with time-varying distributed generation which consider a number of technical issues. The approach has been applied to a medium voltage distribution network considering hourly demand and wind speeds. Results show that this proposal has a better response to the natural behavior of loads and generation than solely considering a single operation scenario.