159 research outputs found

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

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    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 Deep Learning-based approach for Fault Detection of Power Lines

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    Master's thesis in Information- and communication technology (IKT590)A transmission network is the most crucial part of modern infrastructure. However, it requires an extensive amount of power line inspection each year to maintain, and with an increased interest in replacing large helicopters with drones for this process, the possibility of including AI is equally compelling. This thesis goes into the second part by taking a deep learning-based approach in the interest of fault detection. A literature review illustrates that earlier research has some to none understanding of the complexity re-quired for inspection. Due to the advancement in object detection and classification, this thesis has identified and implemented an applicable model capable of giving state-of-the-art accuracy in electrical pole and component detection by dividing the process into multiple layers. This thesis takes as well and proposes a new method that presented great result in assuring more reliable fault detection and is a way to improve the quality of images taken by drones. The pole detection layer gave 97.7 mAP, the component detection layer reached 95.6mAP, the fault classifier delivered an accuracy of 93%, and the proposed quality classifier had an accuracy of 93% as well. The presented approach illustrates the possibility of phasing the physical inspection out. The amount of component labeled that must be available for algorithmic training to surpass a human expert is not readily available. Nevertheless, the presented approach is a sufficient tool for assisting the inspector

    Inspecção Visual de Isoladores Eléctricos -Abordagem baseada em Deep Learning

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    To supply the electrical population’s demand is necessary to have a good quality power distribution systems. Electrical asset inspection, like electrical towers, dam or power line is a high risk and expensive task. Nowadays it is done with traditional methods like using a helicopter equipped with several sensors or with specialised human labour. In the last years, the Unmanned Aerial Vehicle (UAV) exponential growth (most common called drones) make them very accessible for different applications. They are cheaper and easy to adapt. Adopting this technology will be in the future the next step on electrical asset inspection. It will provide a better service (safer, faster and cheaper), particularly in power line distribution. This thesis brings forward an alternative to traditional methods using a UAV for images processing during the insulator visual inspection. The developed work implement real-time insulators visual detection using na Artificial Neural Network (ANN), You Only Look Once (YOLO) in this case, on medium and high voltage power lines. YOLO was trained with different types and sizes of insulators. Isn’t always possible to see what the UAV is recording so it has a gimbal system which controls the camera orientation/position. It will centre the insulator on the image and this way getting a better view of it. All the training and tests were performed on board Jetson TX2.A inspeção de ativos elétricos, sejam eles torres elétricas, barragens ou linhas elétricas, é realizada com recurso a helicópteros, equipados com sensores para o efeito ou, de uma forma mais minuciosa, com o recurso a mão-de-obra especializada. Ambas as situações são trabalhos de risco elevado. Nos últimos anos temos assistido a um enorme crescimento de veículos aéreos não tripulados, vulgarmente chamados de drones. Estes sistemas estão bastante desenvolvidos e são economicamente acessíveis, o que os torna perfeitos para variadíssimas funções. A inspeção de linhas elétricas não ´e exceção. Esta dissertação, pretende ser uma primeira abordagem `a utilização de drones para uma inspeção autónoma de linhas elétricas, nomeadamente no processamento de imagem para inspeção visual de isoladores. O trabalho desenvolvido, consiste na implementação de um sistema que funciona em tempo real para a deteção visual de isoladores. A deteção ´e feita com recurso a uma rede neuronal, neste caso específico a fico a You Only Look Once (YOLO), que foi treinada com isoladores de diferentes tamanhos e materiais. Uma vez que nem sempre ´e possível acompanhar o que está a ser filmado, o drone consta de um sistema capaz de orientar a câmara, chamado gimbal, para centrar o isolador na imagem e assim conseguir obter um melhor enquadramento do ativo a ser inspecionado. Todos este desenvolvimentos e consequentes testes foram realizados com a utilização de processamento paralelo, que neste caso foi utilizada a placa Jetson TX2

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

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    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

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

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    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

    Artificial neural networks and their applications to intelligent fault diagnosis of power transmission lines

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    Over the past thirty years, the idea of computing based on models inspired by human brains and biological neural networks emerged. Artificial neural networks play an important role in the field of machine learning and hold the key to the success of performing many intelligent tasks by machines. They are used in various applications such as pattern recognition, data classification, stock market prediction, aerospace, weather forecasting, control systems, intelligent automation, robotics, and healthcare. Their architectures generally consist of an input layer, multiple hidden layers, and one output layer. They can be implemented on software or hardware. Nowadays, various structures with various names exist for artificial neural networks, each of which has its own particular applications. Those used types in this study include feedforward neural networks, convolutional neural networks, and general regression neural networks. Increasing the number of layers in artificial neural networks as needed for large datasets, implies increased computational expenses. Therefore, besides these basic structures in deep learning, some advanced techniques are proposed to overcome the drawbacks of original structures in deep learning such as transfer learning, federated learning, and reinforcement learning. Furthermore, implementing artificial neural networks in hardware gives scientists and engineers the chance to perform high-dimensional and big data-related tasks because it removes the constraints of memory access time defined as the von Neuman bottleneck. Accordingly, analog and digital circuits are used for artificial neural network implementations without using general-purpose CPUs. In this study, the problem of fault detection, identification, and location estimation of transmission lines is studied and various deep learning approaches are implemented and designed as solutions. This research work focuses on the transmission lines’ datasets, their faults, and the importance of identification, detection, and location estimation of them. It also includes a comprehensive review of the previous studies to perform these three tasks. The application of various artificial neural networks such as feedforward neural networks, convolutional neural networks, and general regression neural networks for identification, detection, and location estimation of transmission line datasets are also discussed in this study. Some advanced methods based on artificial neural networks are taken into account in this thesis such as the transfer learning technique. These methodologies are designed and applied on transmission line datasets to enable the scientist and engineers with using fewer data points for the training purpose and wasting less time on the training step. This work also proposes a transfer learning-based technique for distinguishing faulty and non-faulty insulators in transmission line images. Besides, an effective design for an activation function of the artificial neural networks is proposed in this thesis. Using hyperbolic tangent as an activation function in artificial neural networks has several benefits including inclusiveness and high accuracy

    Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery

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    This work presents a novel system utilizing previously developed convolutional neural network (CNN) architectures to aid in automating maintenance inspections of the dead-end body component (DEBC) from high-tension power lines. To maximize resolution of inspection images gathered via unmanned aerial systems (UAS), two different CNNs were developed. One to detect and crop the DEBC from an image. The second to classify the likelihood the component in question contains a crack. The DEBC detection CNN utilized a Python implementation of Faster R-CNN fine-tuned for three classes via 270 inspection photos collected during UAS inspection, alongside 111 images from provided simulated imagery. The data was augmented to develop 2,707 training images. The detection was tested with 111 UAS inspections images. The resulting CNN was capable of 97.8% accuracy in detecting and cropping DEBC welds. To train the classification CNN if the DEBC weld region cropped from the DEBC detection CNN was cracked, 1,149 manually cropped images from both the simulated images, as well images collected of components previously replaced both inside and outside a warehouse, were augmented to provide a training set of 4,632 images. The crack detection network was developed using the VGG16 model implemented with the Caffe framework. Training and testing of the crack detection CNNs performance was accomplished using a random 5-fold cross validation strategy resulting in an overall 98.8% accuracy. Testing the combined object detection and crack classification networks on the same 5-fold cross validation test images resulted in an average accuracy of 73.79%
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