Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data.

Bushra Jalil,Maria Antonietta Pascali,Massimo Martinelli,Andrea Berton,Davide Moroni,Giuseppe Riccardo Leone

doi:10.3390/s19133014

Abstract

The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.

Highlights

Power transmission lines are the means of electricity distribution, and it is of extreme importance to ensure the continuous supply of electricity and the high performance of these lines
The recent boost of Unmanned Aerial Vehicles (UAVs) technology has increased the need of methods for object tracking and detection from RGB images, supporting the UAV intelligence or improving the functionalities of a real-time monitoring system based on UAV
Regarding the insulator detection task, our method achieved a performance at the state-of-the-art, but in addition, we provide a classification based on their status in order to allow a predictive maintenance of the insulator chains, while most of the research is devoted to the insulator detection and localization, or limiting to shape anomalies

Summary

Introduction

Power transmission lines are the means of electricity distribution, and it is of extreme importance to ensure the continuous supply of electricity and the high performance of these lines. Constant surveillance and inspection of power lines can play a vital role to avoid power shortage: detection of defects in power equipment at an early stage can prevent severe and costly damage, and even used to expect future anomalies. The electrical equipment undergoes a maintenance and repair process, based on their condition, which is termed as preventive maintenance. Geoffrey et al presented the review of some of these methods to identify faults and classify their severity in power equipment using different image analysis approaches [1]. Shawal et al reviewed different methods for classifying the level of faults in electrical equipment [2]

Methods

Results

Conclusion