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This work approaches relative aspects to the alarm processing problem and fault diagnosis in system level, having as purpose filter the alarms generated during a outage and identify the equipment under fault. A methodology was developed using Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in order to resolve the problem. This procedure had as initiative explore the GA capacity to deal...
In this paper a new neuro-genetic approach for fault section estimation (FSE) in electrical power system is presented. We propose a procedure to obtain objective function (required for fault section estimation) using an artificial neural network (ANN). The genetic algorithm (GA) optimization method is then employed to estimate the fault section making use of the objective function. The Hebb's learning...
In order to overcome the inherent flaws of artificial neural networks (ANN), such as long training time, slow convergence and low diagnosis accuracy, a novel evolutionary ANN combining with rough set (RS), named as RSANN, is suggested, and it's proposed to apply in power grid fault diagnosis. The ANN used is a three-layer back-propagation (BP) neural network. RS can reduce the dimensionality of attributes...
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