Online health status monitoring of high voltage insulators using deep learning model

Sarkar, Dipu; Gunturi, Sravan Kumar

doi:10.1007/s00371-021-02308-x

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…In the inspection scenario, real-time detection assumes a pivotal role in assessing power facility components [29,[83][84][85][86][87][88] and detecting surface cracks in bridges and buildings [35,89,90]. These applications are intricately linked to power transmission, building health, and personnel safety.…”

Section: Application Scenarios and Tasks Of Real-time Object Detectionmentioning

confidence: 99%

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Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

Cao,

Kooistra,

Wang

et al. 2023

Drones

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Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection in different fields has become increasingly important. However, since real-time UAV object detection is a comprehensive task involving hardware, algorithms, and other components, the complete implementation of real-time object detection is often overlooked. Although there is a large amount of literature on real-time object detection based on UAV remote sensing, little attention has been given to its workflow. This paper aims to systematically review previous studies about UAV real-time object detection from application scenarios, hardware selection, real-time detection paradigms, detection algorithms and their optimization technologies, and evaluation metrics. Through visual and narrative analyses, the conclusions cover all proposed research questions. Real-time object detection is more in demand in scenarios such as emergency rescue and precision agriculture. Multi-rotor UAVs and RGB images are of more interest in applications, and real-time detection mainly uses edge computing with documented processing strategies. GPU-based edge computing platforms are widely used, and deep learning algorithms is preferred for real-time detection. Meanwhile, optimization algorithms need to be focused on resource-limited computing platform deployment, such as lightweight convolutional layers, etc. In addition to accuracy, speed, latency, and energy are equally important evaluation metrics. Finally, this paper thoroughly discusses the challenges of sensor-, edge computing-, and algorithm-related lightweight technologies in real-time object detection. It also discusses the prospective impact of future developments in autonomous UAVs and communications on UAV real-time target detection.

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Section: Application Scenarios and Tasks Of Real-time Object Detectionmentioning

confidence: 99%

“…Although Raspberry Pi is normally used as a comparison item in benchmark studies, it has also been used as the core computing platform in some studies. For example, an insulator detection study used a lightweight algorithm on a Raspberry Pi 4B to achieve real-time detection [87].…”

Section: Computingmentioning

confidence: 99%

Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

Cao,

Kooistra,

Wang

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…To address the issues of slow detection speeds, the authors of [115] proposed a onestage network using a YOLOv3 deep learning model to recognize and classify images. Moreover, their proposed system combines deep learning with Internet of Things (IoT) through a Raspberry Pi.…”

Section: Physical Defect Detectionmentioning

confidence: 99%

Deep Learning in High Voltage Engineering: A Literature Review

et al. 2022

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Condition monitoring of high voltage apparatus is of much importance for the maintenance of electric power systems. Whether it is detecting faults or partial discharges that take place in high voltage equipment, or detecting contamination and degradation of outdoor insulators, deep learning which is a branch of machine learning has been extensively investigated. Instead of using hand-crafted manual features as an input for the traditional machine learning algorithms, deep learning algorithms use raw data as the input where the feature extraction stage is integrated in the learning stage, resulting in a more automated process. This is the main advantage of using deep learning instead of traditional machine learning techniques. This paper presents a review of the recent literature on the application of deep learning techniques in monitoring high voltage apparatus such as GIS, transformers, cables, rotating machines, and outdoor insulators.

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“…Blynk supports a large number of controllers such as Arduino, ESP8266, ESP32, Raspberry Pi, Onion Omega, SparkFun, etc., which are widely used in IoT applications. Using this IoT platform, without the need to write codes, an iOS/Android mobile interface can be developed for IoT projects in a very short time using only Widgets [39,44,49,53,55].…”

Section: Architecture Of the Iaq Monitoring Systemmentioning

confidence: 99%

A low-cost air quality monitoring system based on Internet of Things for smart homes

Taştan

2022

AIS

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Global climate change and COVID-19 have changed our social and business life. People spend most of their daily lives indoors. Low-cost devices can monitor indoor air quality (IAQ) and reduce health problems caused by air pollutants. This study proposes a real-time and low-cost air quality monitoring system for smart homes based on Internet of Things (IoT). The developed IoT-based monitoring system is portable and provides users with real-time data transfer about IAQ. During the COVID-19 period, air quality data were collected from the kitchen, bedroom and balcony of their home, where a family of 5 spend most of their time. As a result of the analyzes, it has been determined that indoor particulate matter is mainly caused by outdoor infiltration and cooking emissions, and the CO2 value can rise well above the permissible health limits in case of insufficient ventilation due to night sleep activity. The obtained results show that the developed measuring devices may be suitable for measurement-based indoor air quality management. In addition, the proposed low-cost measurement system compared to existing systems; It has advantages such as modularity, scalability, low cost, portability, easy installation and open-source technologies.

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Online health status monitoring of high voltage insulators using deep learning model

Cited by 8 publications

References 29 publications

Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

Deep Learning in High Voltage Engineering: A Literature Review

A low-cost air quality monitoring system based on Internet of Things for smart homes

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