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首页> 外文期刊>IEEE transactions on industrial informatics >Integration of Accelerated Deep Neural Network Into Power Transformer Differential Protection
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Integration of Accelerated Deep Neural Network Into Power Transformer Differential Protection

机译：将加速深神经网络集成到电力变压器差动保护中

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Differential protection scheme is the main protection scheme of power transformers, which still holds the risk of sending false trips subject to inrush currents. This article aims to develop a differential protection scheme to discriminate power transformer magnetizing current from internal faults to decrease the risk of false trips. In this article, an accelerated convolutional neural network (CNN) based approach is designed for the discrimination between internal faults and inrush current. The main competitive advantage of the proposed algorithm is its capability in fusing the feature extraction and fault detection blocks into a single deep neural network block by enabling the network to discover important features automatically. The result of this point is that the algorithm is more efficient in terms of speed, hardware usage, and accuracy. The proposed method is applied to a simulated 230-kV network and an experimental prototype. Different cases with various external factors are simulated to calculate reliability indexes. The comparison between the accelerated CNN, conventional CNN, and nine widely used methods demonstrates the faster and more reliable performance of the proposed algorithm.
机译：差分保护方案是电力变压器的主保护方案，其仍然具有发送受涌入电流的错误旅行的风险。本文旨在开发差动保护方案，以区分电力变压器磁化电流从内部故障降低虚假旅行的风险。在本文中，设计了一种加速的基于卷积神经网络（CNN）的方法，用于内部故障和浪涌电流之间的识别。所提出的算法的主要竞争优势是它在通过使网络自动发现重要功能来将特征提取和故障检测块融合到单个深神经网络块中的能力。这一点的结果是，在速度，硬件使用和准确性方面更有效。所提出的方法应用于模拟的230 kV网络和实验原型。模拟具有各种外部因素的不同案例以计算可靠性指标。加速CNN，常规CNN和九种广泛使用的方法之间的比较演示了所提出的算法的速度和更可靠的性能。

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