INSPECCIÓN DE AISLADORES EN LÍNEAS DE TRANSMISIÓN ELÉCTRICA USANDO INTELIGENCIA ARTIFICIAL

Uno de los procesos más importantes en la inspección de líneas de transmisión eléctrica es la detección de fallas en aisladores eléctricos. El defecto más común encontrado en los aisladores eléctricos es el quiebre de discos dentro de la cadena de aisladores. El uso de métodos tradicionales de segmentación por binarización indican una pobre capacidad para detectar un aislador si hay muchos cambios en el medio en el que se encuentra. Un algoritmo de inteligencia artificial conocido como You Only Look Once (YOLO) se usa para detectar y localizar los aisladores eléctricos a partir de imágenes de torres eléctricas de alta tensión. Posteriormente a la localización de los aisladores eléctricos, se realiza un escalado al doble del tamaño de la imagen original del aislador eléctrico usando un interpolador cúbico. De tal forma que le permita al supervisor de las líneas eléctricas de alta tensión realizar una correcta visualización de los aisladores a inspeccionar. La arquitectura de redes neuronales convolucionales MobileNet empleando el algoritmo YOLO, presentó resultados superiores en precisión y velocidad de ejecución con respecto a las arquitecturas Full YOLO e InceptionV3

Arbitrary-Oriented Detection of Insulators in Thermal Imagery via Rotation Region Network

10.13039/501100004607-Natural Science Foundation of Guangxi Province (Grant Number: 2018JJB160056); Guangxi Science and Technology Base and Talent Project (Grant Number: 2020AC19010); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 51907034)

A Training Framework of Robotic Operation and Image Analysis for Decision-Making in Bridge Inspection and Preservation

This project aims to create a framework of training engineers and policy makers on robotic operation and image analysis for the inspection and preservation of transportation infrastructure. Specifically, it develops the method for collecting camera-based bridge inspection data and the algorithms for data processing and pattern recognitions; and it creates tools for assisting users on visually analyzing the processed image data and recognized patterns for inspection and preservation decision-making. The project first developed a Siamese Neural Network to support bridge engineers in analyzing big video data. The network was initially trained by one-shot learning and is fine-tuned iteratively with human in the loop. Bridge engineers define the region of interest initially, then the algorithm retrieves all related regions in the video, which facilitates the engineers to inspect the bridge rather than exhaustively check every frame of the video. Our neural network was evaluated on three bridge inspection videos with promising performances. Then, the project developed an assistive intelligence system to facilitate inspectors efficiently and accurately detect and segment multiclass bridge elements from inspection videos. A Mask Region-based Convolutional Neural Network was transferred in the studied problem with a small initial training dataset labeled by the inspector. Then, the temporal coherence analysis was used to recover false negative detections of the transferred network. Finally, self-training with a guidance from experienced inspectors was used to iteratively refine the network. Results from a case study have demonstrated that the proposed method uses just a small amount of time and guidance from experienced inspectors to successfully build the assistive intelligence system with an excellent performance

A Review of Graph Neural Networks and Their Applications in Power Systems

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed

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