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

Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography

Infrared thermographic inspection system is widely being utilized for defect detection in electrical equipment. Conventional inspection based on the temperature data interpretation and evaluation the condition of the equipment is subjective and depends on the human experts. Implementation of an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In this thesis, an automatic features extraction system from thermal image of defects and the intelligent classification of thermal condition based on neural network are proposed. The proposed system extracts first order histogram based features and grey level co-occurrence matrix features from the segmented regions and evaluates the effectiveness of these features for defect characterization. Three feature selection techniques namely principal component analysis, the discriminant analysis and individual feature performance analysis are employed to find out the useful and important statistical features. In this study, multilayered perceptron network is proposed for classifying thermal condition into two classes namely normal and defective. The multilayered perceptron neural networks are trained using various training algorithms. Additionally, the present research introduces a computer aided defect diagnosis system where the defected region is found by manual thresholding and intensity features are extracted from each segmented region. The results prove that the statistical features are capable to classify thermal condition and the neural networks achieve the accuracy around 73~78

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

A Survey on Audio-Video based Defect Detection through Deep Learning in Railway Maintenance

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions

Index to NASA Tech Briefs, 1975

This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

Industrial Problem Solving with Microbeam Analysis

The objective of this paper is to provide an overview of the microbeam analytical techniques and to help select the appropriate one to solve complex problems often arising in today\u27s high - tech industries. case histories are given to show how renowned analytical service laboratories and microbeam experts formulated their strategies to approach a real life problem, what type of conclusions were deduced from the data and, finally, how the problem was solved

Machine Learning for Photovoltaic Systems Condition Monitoring:A Review

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring

High voltage outdoor insulator surface condition evaluation using aerial insulator images

High voltage insulator detection and monitoring via drone-based aerial images is a cost-effective alternative in extreme winter conditions and complex terrains. The authors examine different surface conditions of the outdoor electrical insulator that generally occur under winter condition using image processing techniques and state-of-the-art classification methods. Two different types of classification approaches are compared: one method is based on neural networks (e.g. CNN, InceptionV3, MobileNet, VGG16, and ResNet50) and the other method is based on traditional machine learning classifiers (e.g. Bayes Net, Decision Tree, Lazy, Rules, and Meta classifiers). They are evaluated to discriminate the images of insulator surface exposed to freezing, wet, and snowing conditions. The results indicate that traditional machine learning methods with proper selection of features can show high classification accuracy. The classification of the insulator surfaces will assist in determining the insulator conditions, and take preventive measures for its protection

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

Power distribution grids are typically installed outdoors and are exposed to environmental conditions. When contamination accumulates in the structures of the network, there may be shutdowns caused by electrical arcs. To improve the reliability of the network, visual inspections of the electrical power system can be carried out; these inspections can be automated using computer vision techniques based on deep neural networks. Based on this need, this paper proposes the Semi-ProtoPNet deep learning model to classify defective structures in the power distribution networks. The Semi-ProtoPNet deep neural network does not perform convex optimization of its last dense layer to maintain the impact of the negative reasoning process on image classification. The negative reasoning process rejects the incorrect classes of an input image; for this reason, it is possible to carry out an analysis with a low number of images that have different backgrounds, which is one of the challenges of this type of analysis. Semi-ProtoPNet achieves an accuracy of 97.22%, being superior to VGG-13, VGG-16, VGG-19, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-201, and also models of the same class such as ProtoPNet, NP-ProtoPNet, Gen-ProtoPNet, and Ps-ProtoPNet