Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things
Youcef Djenouri, Asma Belhadi, Gautam Srivastava, Uttam Ghosh, Pushpita Chatterjee, Jerry Chun‐Wei Lin
Abstract
This article introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps: 1) data preparation; 2) object detection; and 3) hyperparameter optimization. Inspired by deep learning and evolutionary computation (EC) techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyperparameters optimization model based on EC that can be used to tune parameters of our deep learning framework. In the validation of the framework’s usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO data sets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.