Evaluation of Transfer Learning Techniques for Fault Classification in Radial Distribution Systems: A Comparative Study

Abstract

Transfer learning has recently had a detectable impact on the state of the art in a wide variety of applications, and this trend is expected to continue in the near future. Both transfer learning and deep learning algorithms make use of a number of network layers, each of which may be intellectually learned and typically represents the data in a hierarchical fashion with increasing levels of abstraction. Convolutional neural networks have been proven to be exceptionally successful machine learning and deep learning techniques for a number of computer vision problems. These networks were developed by companies such as Alexa, Google, and Squeeze. Fault diagnostic strategies that are based on deep learning techniques are currently a topic of intense investigation due to their higher performance. Using transfer learning technology to carry out fault categorization in a power distribution system in a manner that is both accurate and efficient The work at hand employs a fault classification model for a radial power distribution system that is based on transfer learning and deep learning. Images of time series of three-phase fault currents are acquired via simulation with the assistance of PSCAD software as part of the proposed approach for doing so. In the next step, CNN models that are based on Alex Net, Google Net, and Squeeze Net are utilized to extract fault features from defective photos in order to categorize eleven distinct defects (using the MATLAB platform). For the categorization of defects in a radial distribution system, Alex Net, Google Net, and SqueezNet each offer accuracy of approximately 98.92%, 97.48%, and 99.82%, respectively. In this study, the classification of faults in a distribution system is accomplished with the help of AlexNet, GoogleNet, and SqueezNet. According to the findings of the simulations, the test accuracy for SqueezeNet is the highest it can be, coming in at 99.82%. Because of this, selecting it as the solution to the issue of fault classification in the test distribution system is your best option.