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Fault detection of gas unit of Gilan combined cycle power plant using neural network

机译:基于神经网络的吉兰联合循环发电厂燃气机组故障检测

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Fault detection is one of the most important and challenging issues in engineering application. In this article fault detection of Gilan combined cycle power plant is investigated. To do so two neural network structures are applied. The first neural network which is trained by Kalman Filter. The second structure is NARX network which is trained by levenberg-marquardt method. The results obtained show that the neural network has a great capability in fault detection.
机译:故障检测是工程应用中最重要和最具挑战性的问题之一。本文对吉兰联合循环发电厂的故障检测进行了研究。为此,应用了两个神经网络结构。由卡尔曼滤波器训练的第一个神经网络。第二种结构是NAlev网络,它是通过levenberg-marquardt方法训练的。所得结果表明,神经网络具有强大的故障检测能力。

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