Evaluation of the fretting fatigue behaviour of commercially pure titanium

Fretting fatigue occurs when the contact surfaces of two components undergo small oscillatory movement while they are subjected to a clamping force. A cyclic external load gives rise to the early initiation of fatigue cracks, thus reducing their service life. In this paper, the fretting fatigue behaviour of commercially pure titanium flat samples (1.5 mm thick) is evaluated. A fretting device composed of a frame, load cell, and two screw-mounted cylindrical fretting pads with convex extremities was built and set to a servo-hydraulic testing machine. The fatigue tests were conducted under load control at a frequency of 10 Hz and stress ratio R = 0.1, with various contact load values applied to the fretting pads. Additional tests under inert environment allowed assessing the role of oxidation on the wear debris formation. The fracture surfaces and fretting scars were analysed via scanning electron microscopy in order to evaluate the surface damage evolution and its effect on the fatigue crack features. The effect of the fretting condition on the S-N curve of the material in the range of 10(4)-10(6) cycles is described. Fatigue crack growth calculations allowed estimating the crack initiation and propagation lives under fretting conditions. The effect of the fretting condition in fatigue life is stronger for the lower values of cyclic stress and does not seem to depend on the contact loading value.

Describing fatigue crack growth and load ratio effects in Al 2524 T3 alloy with an enhanced exponential model

The fatigue crack behavior in metals and alloys under constant amplitude test conditions is usually described by relationships between the crack growth rate da/dN and the stress intensity factor range Delta K. In the present work, an enhanced two-parameter exponential equation of fatigue crack growth was introduced in order to describe sub-critical crack propagation behavior of Al 2524-T3 alloy, commonly used in aircraft engineering applications. It was demonstrated that besides adequately correlating the load ratio effects, the exponential model also accounts for the slight deviations from linearity shown by the experimental curves. A comparison with Elber, Kujawski and "Unified Approach" models allowed for verifying the better performance, when confronted to the other tested models, presented by the exponential model. (C) 2012 Elsevier Ltd. All rights reserved.

Study of the dimensions of double-torsion test specimens

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

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.

Electrical load forecasting formulation by a fast neural network

The objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Esquemas alternativos para o passo de parametrização do método da continuação baseados em parâmetros físicos

O método de fluxo de carga convencional é considerado inadequado para se obter o ponto de máximo carregamento (PMC) de sistemas de potência, devido à singularidade da matriz Jacobiana neste ponto. Os métodos da continuação são ferramentas eficientes para a solução deste tipo de problema, visto que técnicas de parametrização podem ser utilizadas para evitar a singularidade da matriz Jacobiana. Neste trabalho, novas opções para a etapa de parametrização do método da continuação são apresentadas. Mostra-se que variáveis com claro significado físico podem ser utilizadas na etapa de parametrização. As seguintes variáveis foram testadas: perda total de potência ativa e reativa, potência ativa e reativa na barra de referência, potência reativa das barras de geração, e as perdas de potência ativa e reativa nas linhas de transmissão (LT). Além de facilitar a implementação computacional do método de continuação, as técnicas de parametrização apresentadas simplificam a definição matemática e o entendimento do método por parte de engenheiros de potência, visto que os métodos de continuação existentes na literatura sempre utilizam técnicas de parametrização complexas, e de interpretação puramente geométrica. Resultados obtidos com a nova metodologia para os sistemas testes do IEEE (14, 30, 57 e 118 barras) mostram que as características de convergência do método de fluxo de carga convencional são melhoradas na região do PMC. Além disso, durante o traçado das curvas PV, as diversas técnicas de parametrização podem ser comutadas entre si possibilitando o cálculo de todos os pontos da curva com um número reduzido de iterações. Diversos testes são realizados para proporcionar a comparação do desempenho dos esquemas de parametrização propostos.

Determinação do ponto de máximo carregamento de sistemas de potência utilizando o fluxo de carga desacoplado rápido parametrizado

Os métodos de fluxo de carga por Newton-Raphson e fluxo de carga desacoplado rápido convencionais são considerados inadequados para a obtenção do ponto de máximo carregamento de sistemas de potência, devido à problemas de mal-condicionamento neste ponto crítico e na sua vizinhança. Neste ponto a matriz Jacobiana do método de Newton-Raphson torna-se singular e considera-se que não são mais válidas as hipóteses de desacoplamento P-V e Q-teta utilizadas para a formulação do método fluxo de carga desacoplado rápido. No entanto, mostra-se neste trabalho, que com pequenas modificações, as versões XB e BX do fluxo de carga desacoplado rápido tornam-se adequadas para a obtenção do ponto de máximo carregamento. Estas novas versões modificadas são comparadas entre si com o intuito de explicitar suas características, assim como da influência da atuação dos limites de geração de potência reativa e de tap's de transformadores. Os resultados obtidos para os sistemas testes do IEEE (14, 30, 57 e 118 barras) mostram que as características de convergência das versões originais são preservadas. Além disso, durante o traçado das curvas PV, os diversos métodos podem ser comutados entre si possibilitando o cálculo de todos os pontos da curva com um número reduzido de iterações.

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.

An Improved Parameterization Technique for the Continuation Power Flow

Continuation methods have been long used in P-V curve tracing due to their efficiency in the resolution of ill-conditioned cases, with close to singular Jacobian matrices, such as the maximum loading point of power systems. Several parameterization techniques have been proposed to avoid matrix singularity and successfully solve those cases. This paper presents a simple geometric parameterization technique to overcome the singularity of the Jacobian matrix by the addition of a line equations located at the plane determined by a bus voltage magnitude and the loading factor. This technique enlarges the set of voltage variables that can be used to whole P-V curve tracing, without ill-conditioning problems and no need of parameter changes. Simulation results, obtained for large realistic Brazilian and American power systems, show that the robustness and efficiency of the conventional power flow are not only preserved but also improved.

Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Cellular Automaton Approach to Spatial Electric Load Forecasting

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

An effortless hybrid method to solve economic load dispatch problem in power systems

This paper proposes a new approach and coding scheme for solving economic dispatch problems (ED) in power systems through an effortless hybrid method (EHM). This novel coding scheme can effectively prevent futile searching and also prevents obtaining infeasible solutions through the application of stochastic search methods, consequently dramatically improves search efficiency and solution quality. The dominant constraint of an economic dispatch problem is power balance. The operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones (POZ), network loss are considered for practical operation. Firstly, in the EHM procedure, the output of generator is obtained with a lambda iteration method and without considering POZ and later in a genetic based algorithm this constraint is satisfied. To demonstrate its efficiency, feasibility and fastness, the EHM algorithm was applied to solve constrained ED problems of power systems with 6 and 15 units. The simulation results obtained from the EHM were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time. (C) 2011 Elsevier Ltd. All rights reserved.

Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Artificial neural networks for load flow and external equivalents studies

In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg-Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)-(d). (C) 2010 Elsevier B.V. All rights reserved.