Aerial Image Analysis using Deep Learning for Electrical Overhead Line Network Asset Management

Electricity networks are critical infrastructure, delivering vital energy services. Due to the significant number, variety and distribution of electrical network overhead line assets, energy network operators spend millions annually on inspection and maintenance programmes. Currently, inspection involves acquiring and manually analysing aerial images. This is labour intensive and subjective. Along with costs associated with helicopter or drone operations, data analysis represents a significant financial burden to network operators. We propose an approach to automating assessment of the condition of electrical towers. Importantly, we train machine learning tower classifiers without using condition labels for individual components of interest. Instead, learning is supervised using only condition labels for towers in their entirety. This enables us to use a real-world industry dataset without needing costly additional human labelling of thousands of individual components. Our prototype first detects instances of components in multiple images of each tower, using Mask R-CNN or RetinaNet. It then predicts tower condition ratings using one of two approaches: (i) component instance classifiers trained using class labels transferred from towers to each of their detected component instances, or (ii) multiple instance learning classifiers based on bags of detected instances. Instance or bag class predictions are aggregated to obtain tower condition ratings. Evaluation used a dataset with representative tower images and associated condition ratings covering a range of component types, scenes, environmental conditions, and viewpoints. We report experiments investigating classification of towers based on the condition of their multiple insulator and U-bolt components. Insulators and their U-bolts were detected with average precision of 96.7 and 97.9, respectively. Tower classification achieved areas under ROC curves of 0.94 and 0.98 for insulator condition and U-bolt condition ratings, respectively. Thus we demonstrate that tower condition classifiers can be trained effectively without labelling the condition of individual components

Icing Effects on Power Lines and Anti-icing and De-icing Methods

Icing on power lines may lead to compromise safety and reliability of electric supply network. Prolong icing can lead to power breakdown and collapse of towers. Since power transmission lines are mostly overhead and could face the direct impact of icing, and it is one of the main challenges faced by power distribution companies in cold regions. When the ice accretion crosses the safety limit then deicing action can be carried out. We can find number of deicing methods that are used in different parts of the world. However, all of these deicing techniques have their own advantages and disadvantages on implementation. It is one of the most difficult as well as dangerous process to perform deicing on power lines. If a fault is detected and that has been occurred due to icing or during routine maintenance, extra care must be taken in order to ensure safety of the personals when performing de-icing of lines. However, as technology evolved, new ways and techniques are adopted with the help of sensors that give quick feedback to control room in the national grid via wireless communication network for real time action. In the thesis we have discussed atmospheric icing impacts on power lines in the cold regions across the world. A literature review has been done for anti-icing and deicing methods that are currently adopted in the power distribution network. Methods that are used against ice buildups have also been analyzed. This work also shows the impacts of icing and deicing techniques presently adopted, and also throws light on their pros and cons during maintenance operations. It provides an overview of the evolving technology trends that are practiced to ensure the availability of existing power transmission system in cold climate regions

Remote Sensing methods for power line corridor surveys

AbstractTo secure uninterrupted distribution of electricity, effective monitoring and maintenance of power lines are needed. This literature review article aims to give a wide overview of the possibilities provided by modern remote sensing sensors in power line corridor surveys and to discuss the potential and limitations of different approaches. Monitoring of both power line components and vegetation around them is included. Remotely sensed data sources discussed in the review include synthetic aperture radar (SAR) images, optical satellite and aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) data. The review shows that most previous studies have concentrated on the mapping and analysis of network components. In particular, automated extraction of power line conductors has achieved much attention, and promising results have been reported. For example, accuracy levels above 90% have been presented for the extraction of conductors from ALS data or aerial images. However, in many studies datasets have been small and numerical quality analyses have been omitted. Mapping of vegetation near power lines has been a less common research topic than mapping of the components, but several studies have also been carried out in this field, especially using optical aerial and satellite images. Based on the review we conclude that in future research more attention should be given to an integrated use of various data sources to benefit from the various techniques in an optimal way. Knowledge in related fields, such as vegetation monitoring from ALS, SAR and optical image data should be better exploited to develop useful monitoring approaches. Special attention should be given to rapidly developing remote sensing techniques such as UAVs and laser scanning from airborne and land-based platforms. To demonstrate and verify the capabilities of automated monitoring approaches, large tests in various environments and practical monitoring conditions are needed. These should include careful quality analyses and comparisons between different data sources, methods and individual algorithms

Importance and applications of robotic and autonomous systems (RAS) in railway maintenance sector: a review

Maintenance, which is critical for safe, reliable, quality, and cost-effective service, plays a dominant role in the railway industry. Therefore, this paper examines the importance and applications of Robotic and Autonomous Systems (RAS) in railway maintenance. More than 70 research publications, which are either in practice or under investigation describing RAS developments in the railway maintenance, are analysed. It has been found that the majority of RAS developed are for rolling-stock maintenance, followed by railway track maintenance. Further, it has been found that there is growing interest and demand for robotics and autonomous systems in the railway maintenance sector, which is largely due to the increased competition, rapid expansion and ever-increasing expense

Automatic vision based fault detection on electricity transmission components using very highresolution

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations. Fault identification is one of the most significant bottlenecks faced by Electricity transmission and distribution utilities in developing countries to deliver credible services to customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial resolution to monitor four major Electric power transmission network (EPTN) components condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks (CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage object detection model on the electric transmission power line imagery to localize, classify and inspect faults present. The components fault considered include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision of 89.61%. All the developed SSD based models achieve a high precision rate and low recall rate in detecting the faulty components, thus achieving acceptable balance levels F1-score and representation. Finally, comparable to other works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood

Aerial identification of flashed over faulty insulator using binary image classification

Flashed over insulator faults are the most significant faults in high voltage line insulators. They are complicated to identify using traditional methods due to their labor-intensive nature. This study proposes a deep learning-based algorithm for detecting flashed over insulator faults in the real time. The algorithm is based on the Resnet 50 architecture, which has been shown to be effective for image classification tasks in the previous studies regarding image analysis. The algorithm is fast, robust and efficient, making it suitable for real-time applications. The algorithm is trained on a dataset of images of flashed over and non-flashed over insulators. This dataset was collected from various transmission lines and National Center of Robotics and Automation, which are located in Pakistan. For validating the effectiveness of the Resnet 50 algorithm, it was compared with the results obtained from the two other widely popular deep learning algorithms, Densenet 121 and VGG 16 (trained and validated on the same dataset). The results showed that the Resnet 50 was able to detect flashed over insulator faults with an accuracy of over 99%. Whereas the Densenet 121 and VGG 16 have achieved an accuracy of less than 51%