Evaluation of transfer learning approaches for partial discharge classification in hydrogenerators
Evaluation of transfer learning approaches for partial discharge classification in hydrogenerators
复制标题
水轮发电机局部放电分类迁移学习方法的评估
DOI：
10.1109/wcnps56355.2022.9969682
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Victor Dmitriev
中科院分区：
文献类型：
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作者：
Frederico H. R. Lopes;R. Zampolo;Rodrigo M. S. Oliveira;Victor Dmitriev
绝缘系统的严重恶化会中断高压电机的运行。就水力发电机而言，意外中断会给能源公司和消费者带来重大损失。最近提出的自动局部放电分析是防止高压设备故障的有效方法，主要基于深度学习。然而，它们的性能依赖于庞大且通常昂贵的数据集的可用性。此外，如果要从头开始训练模型，则需要大量的计算资源。这项工作比较了三种微调策略应用于预训练的卷积神经网络进行局部放电分类。我们使用相位分辨的局部放电数据，在Tucuruí（巴西帕拉）发电厂的水轮发电机正常运行期间获得的，重新训练的最后一层的深度分类器最初设想在不同的上下文中识别局部放电。我们的研究结果表明，有效的迁移学习是通过使用交叉验证和数据增强技术。
Severe deterioration in the insulation system can interrupt the operation of high voltage electrical machines. Concerning hydrogenerators, unexpected interruptions result in important losses to both energy companies and consumers. Recent proposals for automatic partial discharge analysis, an effective approach to prevent failure in high voltage equipment, are mainly based on deep learning. Their performance, however, relies upon the availability of huge, and commonly expensive, datasets. Besides, if models are intended to be trained from scratch, significant computational resources are required. This work compares three fine-tuning strategies applied to a pre-trained convolutional neural network for partial discharge classification. We use phase-resolved partial discharge data, obtained during normal operation of hydrogenerators at Tucuruí (Pará, Brazil) power plant, to re-train the last layer of a deep classifier originally conceived to identify partial discharges in a different context. Our results demonstrate that effective transfer learning is achieved by using cross-validation and data augmentation techniques.