Transfer Learning based Condition Assessment of High Voltage Insulator: A Comparative Analysis

Routine inspection of a power line insulator system for early problem detection and maintenance is required for the effective transmission of electrical power to customers. Unlike the traditional manual inspection methods such as foot patrol, helicopter assisted methods, flying and climbing robots are time consuming, dangerous, labor intensive, and expensive. This work primarily focused on the insulator image classification of the Chinese Power Line Insulator Dataset (CPLID). Convolutional Neural Network (CNN) and pre-trained neural networks with different optimizers like Adam, Adamax, Adagrad, Adadelta, Nadam, Ftrl, and RMSprop were used to find out the best combination of CNN and pre-trained neural network with the optimizer to provide robust performance and higher accuracy. Finally, CNN with Adamax optimizer provided overall accuracy of 95.27% and the pre-trained CNN Densenet 201 with Adam optimizer gave overall accuracy of 100%. In the automated inspection of transmission line insulators, the deployment of UAVs and deep learning algorithms can be a useful starting point for many researchers throughout the world.