Transmission lines distance protection using artificial neural networks

This work presents the development and implementation of an artificial neural network based algorithm for transmission lines distance protection. This algorithm was developed to be used in any transmission line regardless of its configuration or voltage level. The described ANN-based algorithm does not need any topology adaptation or ANN parameters adjustment when applied to different electrical systems. This feature makes this solution unique since all ANN-based solutions presented until now were developed for particular transmission lines, which means that those solutions cannot be implemented in commercial relays. (c) 2011 Elsevier Ltd. All rights reserved.

Comparison Between Voltage Control Structures of Synchronous Machines

This paper compares the behaviour of two different control structures of automatic voltage regulators of synchronous machines equipped with static excitation systems. These systems have a fully controlled thyristor bridge that supplies DC current to the rotor winding. The rectifier bridge is fed by the stator terminals through a step-down transformer. The first control structure, named ""Direct Control"", has a single proportional-integral (PI) regulator that compares stator voltage setpoint with measured voltage and acts directly on the thyristor bridge`s firing angle. This control structure is usually employed in commercial excitation systems for hydrogenerators. The second structure, named ""Cascade Control"", was inspired on control loops of commercial DC motor drives. Such drives employ two PIs in a cascade arrangement, the external PI deals with the motor speed while the internal one regulates the armature current. In the adaptation proposed, the external PI compares setpoint with the actual stator voltage and produces the setpoint to the internal PI-loop which controls the field current.

Residual stresses in titanium nitride thin films obtained with step variation of substrate bias voltage during deposition

In this work, a series of depositions of titanium nitride (TiN) films on M2 and D2 steel substrates were conducted in a Triode Magnetron Sputtering chamber. The temperature; gas flow and pressure were kept constant during each run. The substrate bias was either decreased or increased in a sequence of steps. Residual stress measurements were later conducted through the grazing X-ray diffraction method. Different incident angles were used in order to change the penetration depth and to obtain values of residual stress at different film depths. A model described by Dolle was adapted as an attempt to calculate the values of residual stress at each incident angle as a function of the value from each individual layer. Stress results indicated that the decrease in bias voltage during the deposition has produced compressive residual stress gradients through the film thickness. On the other hand, much less pronounced gradients were found in one of the films deposited with increasing bias voltage. (C) 2010 Elsevier B.V. All rights reserved.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

Knowledge transfer in product development processes: A case study in small and medium enterprises (SMEs) of the metal-mechanic sector from Sao Paulo, Brazil

This paper reports a research that evaluated the product development methodologies used in Brazilian small and medium-sized metal-mechanic enterprises (SMEs), in a specific region of Sao Paulo. The tool used for collecting the data was a questionnaire, which was developed and applied through interviews conducted by the researchers in 32 companies. The main focus of this paper can be condensed in the synthesis-question ""Is only the company responsible for the development?"" which was analyzed thoroughly. The results obtained from this analysis were evaluated directly (through the respective percentages of answers) and statistically (through the search of an index which demonstrates if two questions are related). The results point to a degree of maturity in SMEs, which allows product development to be conducted in cooperation networks. (C) 2007 Elsevier Ltd. All rights reserved.

MODELLING AND CONTROL OF ERSW PROCESSES BY NEURONAL NETWORK

A study on the use of artificial intelligence (AI) techniques for the modelling and subsequent control of an electric resistance spot welding process (ERSW) is presented. The ERSW process is characterized by the coupling of thermal, electrical, mechanical, and metallurgical phenomena. For this reason, early attempts to model it using computational methods established as the methods of finite differences, finite element, and finite volumes, ask for simplifications that lead the model obtained far from reality or very costly in terms of computational costs, to be used in a real-time control system. In this sense, the authors have developed an ERSW controller that uses fuzzy logic to adjust the energy transferred to the weld nugget. The proposed control strategies differ in the speed with which it reaches convergence. Moreover, their application for a quality control of spot weld through artificial neural networks (ANN) is discussed.

On-line fault diagnostic system for proton exchange membrane fuel cells

In this paper, a supervisor system, able to diagnose different types of faults during the operation of a proton exchange membrane fuel cell is introduced. The diagnosis is developed by applying Bayesian networks, which qualify and quantify the cause-effect relationship among the variables of the process. The fault diagnosis is based on the on-line monitoring of variables easy to measure in the machine such as voltage, electric current, and temperature. The equipment is a fuel cell system which can operate even when a fault occurs. The fault effects are based on experiments on the fault tolerant fuel cell, which are reproduced in a fuel cell model. A database of fault records is constructed from the fuel cell model, improving the generation time and avoiding permanent damage to the equipment. (C) 2007 Elsevier B.V. All rights reserved.

TiAlN coatings deposited by triode magnetron sputtering varying the bias voltage

TiAlN films were deposited on AISI O1 tool steel using a triode magnetron sputtering system. The bias voltage effect on the composition, thickness, crystallography, microstructure, hardness and adhesion strength was investigated. The coatings thickness and elemental composition analyses were carried out using scanning electron microscopy (SEM) together with energy dispersive X-ray (EDS). The re-sputtering effect due to the high-energy ions bombardment on the film surface influenced the coatings thickness. The films crystallography was investigated using X-ray diffraction characterization. The X-ray diffraction (XRD) data show that TiAlN coatings were crystallized in the cubic NaCl B1 structure, with orientations in the {111}, {200} {220} and {311} crystallographic planes. The surface morphology (roughness and grain size) of TiAlN coatings was investigated by atomic force microscopy (AFM). By increasing the substrate bias voltage from -40 to -150 V, hardness decreased from 32 GPa to 19 GPa. Scratch tester was used for measuring the critical loads and for measuring the adhesion. (C) 2011 Elsevier B. V. All rights reserved.

Effect of precracking method on K(Ic) results for medium-density polyethylene tested under cryogenic condition

The effect of different precracking methods on the results of linear elastic K(Ic) fracture toughness testing with medium-density polyethylene (MDPE) was investigated. Cryogenic conditions were imposed in order to obtain valid K(Ic) values from specimens of suitable size. Most conservative K(Ic) values were obtained by slow pressing a fresh razor blade at the notch root of the specimen. Due to the low deformation level imposed on the crack tip region, the slow pressing razor blade technique also produced less scatter in fracture toughness results. It has been shown that the slow stable crack growth preceding catastrophic brittle failure during K(Ic) tests in MOPE under cryogenic conditions should not be disregarded as it has relevant physical meaning and may affect the fracture toughness results. (C) 2010 Elsevier Ltd. All rights reserved.

Texture and mechanical properties evolution of a deep drawing medium carbon steel during cold rolling and subsequent recrystallization

Medium carbon steels are mostly used for simple applications; however, new applications have been developed for which good sheet metal formability is required. These types of steels have an inherent low formability. A medium-carbon hot-rolled SAE 1050 steel was selected for this study. It has been cold rolled with thickness reductions varying between 7 and 80%. The samples obtained were used to evaluate the strain hardening curve. For samples with a 50 and 80% thickness reduction, an annealing heat treatment was performed to achieve recrystallization. The material was characterized in the ""as-received"", cold rolled and annealed conditions using several methods: optical metallography, X-ray diffraction (texture), Vickers hardness, and tensile testing. For large thickness reductions, the SAE 1050 steel presented low elongation, less than 2%, and yield strength (YS) and tensile strength (TS) around 1400 MPa. Texture in the ""as-received"" condition showed strong components on the {001} plane, in the < 100 >, < 210 > and (110) directions. After cold rolling, the texture did not present any significant changes for small thickness reductions, however. It changed completely for large ones, where gamma, < 111 >//ND, alpha, < 110 > HRD, and gamma prime, < 223 >//ND, fibres were strengthened. After annealing, the microstructure of the SAE 1050 steel was characterized by recrystallized ferrite and globular cementite. There was little change in the alpha fibre for the 50% reduction, whereas for the 80% reduction, its intensity increased. Both gamma and gamma prime fibres vanished upon annealing for 50 and 80% reductions alike. (c) 2008 Elsevier B.V. All rights reserved.

Medium carbon steel deep drawing: A study on the evolution of mechanical properties, texture and simulations, from cold rolling to the end product

Medium carbon steels are mostly used for simple applications; nevertheless new applications have been developed for which good sheet formability is required. This class of steels has an inherent low formability. A medium carbon hot rolled SAE 1050 steel has been selected for this study. It has been cold rolled with reductions in the 7-80% range. Samples have been used to assess the cold work hardening curve. For samples with a 50 and 80% thickness reduction, an annealing heat treatment has been performed to obtain recrystallization. The material has been characterized in the ""as received"", cold rolled and annealed conditions, using several methods: optical microscopy, X-ray diffraction (texture), Vickers hardness and tensile testing. The 50% cold rolled and recrystallized material has been further studied in terms of sheet metal formability and texture evolution during the actual stamping of a steel toecap that has been used to validate the finite element simulations. (C) 2008 Elsevier B.V. All rights reserved.

The influence of different voltage waveforms and grain sizes in electrical steels losses

Square and two-level pulse width modulation (PWM) magnetic induction waveforms are investigated and their effect on electrical steels losses as a function of the grain size is determined. The increase of hysteresis losses-as compared to that resulting from sinusoidal voltages-occurs only for two-level PWM waveforms. Total losses are lower for square waveform, and the difference between losses under square and sinusoidal waveform increase with increasing grain size, result explained with the loss separation model. (C) 2008 Elsevier B.V. All rights reserved.

Treatment of Aqueous Effluents Containing Phenol by the O(3), O(3)-UV, and O(3)-H(2)O(2) Processes: Experimental Study and Neural Network Modeling

In this work, the oxidation of the model pollutant phenol has been studied by means of the O(3), O(3)-UV, and O(3)-H(2)O(2) processes. Experiments were carried out in a fed-batch system to investigate the effects of initial dissolved organic carbon concentration, initial, ozone concentration in the gas phase, the presence or absence of UVC radiation, and initial hydrogen peroxide concentration. Experimental results were used in the modeling of the degradation processes by neural networks in order to simulate DOC-time profiles and evaluate the relative importance of process variables.

Integrated analysis of cooling water systems: Modeling and experimental validation

Cooling towers are widely used in many industrial and utility plants as a cooling medium, whose thermal performance is of vital importance. Despite the wide interest in cooling tower design, rating and its importance in energy conservation, there are few investigations concerning the integrated analysis of cooling systems. This work presents an approach for the systemic performance analysis of a cooling water system. The approach combines experimental design with mathematical modeling. An experimental investigation was carried out to characterize the mass transfer in the packing of the cooling tower as a function of the liquid and gas flow rates, whose results were within the range of the measurement accuracy. Then, an integrated model was developed that relies on the mass and heat transfer of the cooling tower, as well as on the hydraulic and thermal interactions with a heat exchanger network. The integrated model for the cooling water system was simulated and the temperature results agree with the experimental data of the real operation of the pilot plant. A case study illustrates the interaction in the system and the need for a systemic analysis of cooling water system. The proposed mathematical and experimental analysis should be useful for performance analysis of real-world cooling water systems. (C) 2009 Elsevier Ltd. All rights reserved.

Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm

We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newton`s method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The analysis of transient and steady-state performances at each individual node within the network is developed by using a weighted spatial-temporal energy conservation relation and confirmed by computer simulations. The simulation results also verify that the proposed algorithm provides not only a faster convergence rate but also an improved steady-state performance as compared to an LMS-based scheme. In addition, the new approach attains an acceptable misadjustment performance with lower computational and memory cost, provided the number of regressor vectors and filter length parameters are appropriately chosen, as compared to a distributed recursive-least-squares (RLS) based method.