UV‐flashover evaluation of porcelain insulators based on deep learning

Pei, Shaotong; Liu, Yunpeng; Ji, Xinxin; Geng, Jianghai; Zhou, Guangyang; Wang, Shenghui

doi:10.1049/iet-smt.2017.0465

Cited by 16 publications

(8 citation statements)

References 17 publications

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“…The distribution network is an integral and extremely important part of the power grid, which shoulders the important functions of voltage conversion, power conversion, and power distribution. Ensuring that the substation can operate in a stable and safe state for a long time, which can undoubtedly ensure the safety and reliability of the power grid [1]. At present, there are more studies on the condition monitoring and maintenance of primary and secondary equipment in China, but fewer on the monitoring and control of auxiliary equipment such as environmental monitoring systems and fire protection systems, which leads to the lag of the level.…”

Section: Introductionmentioning

confidence: 99%

Intelligent monitoring method of electronic equipment operation status based on Internet of Things technology

Shen

2023

International Conference on Internet of Things and Machine Learning (IoTML 2022)

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The new power system can strengthen the resource allocation ability of the distributed Internet of Things, and can also make the intelligent coordinated control of a large number of decentralized power generation and supply objects. It is an urgent task to realize the two-way interaction of various market players and establish a corresponding power communication network for the realization of a new power system. Aiming at the quality problem of communication data transmission of power Internet of Things (IOT) equipment, this paper proposes a unified data model of IOT agent. It can encapsulate the collected data into a user-defined binary data packet format for transparent transmission. This avoids complex data parsing and format conversion and improves the efficiency of data transmission. To better reflect the feasibility and effectiveness of the IOT agent information bearing model, this paper applies different network scenarios for simulation analysis. The experimental results indicate that the model can reduce the data transmission delay and the packet loss rate.

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Section: Introductionmentioning

confidence: 99%

Intelligent monitoring method of electronic equipment operation status based on Internet of Things technology

Shen

2023

International Conference on Internet of Things and Machine Learning (IoTML 2022)

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“…The solar-blind ultraviolet (UV) discharge detection based on UV signals of the solid-blind band from 240 to 280 nm has many advantages, such as resistance to interference from solar background noise, a long detection distance, with non-contact, accurate, discharge location. For this reason, in recent years, it has been widely used in detection of discharge from electrical equipment [1][2][3][4]. At present, the number of photons at the location of corona discharge is intuitive data output in the detection results of UV imagers.…”

Section: Introductionmentioning

confidence: 99%

A consistency method of discharge detection results at solar‐blind ultraviolet based on a radiation transmission model

Liu

Geng

et al. 2022

IET Science Measure & Tech

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Solar‐blind ultraviolet (UV) discharge detection based on UV signals in the solar‐blind band from 240 to 280 nm is an effective non‐contact discharge detection method for electrical equipment, but there is still a problem that the detection results of different types of UV imagers are not comparable. To this end, by using standard light source and MODTRAN model, the relationship between the facular area and the radiant flux received by UV imagers was investigated by establishing a radiation transmission model for UV detection under different environmental conditions. Through analysis, it was found that the influence of humidity on the transmittance of the solar‐blind band in atmosphere can be neglected, and the transmittance is slightly affected by atmospheric pressure, temperature, and detection distance under conditions suitable for UV detection. Moreover, a consistency formula for UV detection results was proposed, thus forming a consistency method for detection results suitable for various types of UV imager. Finally, the feasibility of the consistency method was verified by UV discharge detection tests with a rod‐plate model and the absolute irradiance at discharge points of the rod‐plate model measured by a grating spectrometer. The consistency method is conducive to the promotion of UV discharge detection technology.

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“…Many scholars focus on the PD monitoring technologies for non‐ceramic or ceramic insulators, such as leakage current detection [9, 10], ultrahigh frequency (UHF) [11, 12] and UV image recognition [13]. Among them, leakage current detection is the most extensively adopted and includes analysis of the root‐mean‐square (RMS) value, peak value or frequency features of leakage current.…”

Section: Introductionmentioning

confidence: 99%

Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses

Jin

et al. 2021

High Voltage

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Inspection of partial discharge before contamination flashover is of great importance for preventing exterior insulation accidents. In this study, a new method for identification of discharge severities is proposed. Specifically, a low-cost ultraviolet (UV) sensor detection system was combined with time-frequency method, texture analysis and support vector machine (SVM) classifier to classify partial discharge severities for ceramic insulators. The visible images and the root-mean-square value of leakage currents detected simultaneously are used to classify the UV signals into different discharge faults. The frequency and amplitude integration of UV pulses are minimum in corona discharge and larger in arc discharge. The images of UV signal spectrograms differ significantly at different discharge stages. The density and brightness of image textures are minimal in corona discharge and larger in arc discharge. Valid and reliable features selected by two texture feature extraction methods with SVM classifier have a reliable classification accuracy of 90.6% for ceramic insulators, and outperform a single time feature or other texture features. SVM outperforms k-Nearest Neighbour, Naive Bayes and Decision Tree. Our new method with computational effectiveness and high practicality can solve the problem of high randomness and low accuracy of UV sensor detection. It can be further applied to the deterioration diagnosis of power facilities.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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UV‐flashover evaluation of porcelain insulators based on deep learning

Cited by 16 publications

References 17 publications

Intelligent monitoring method of electronic equipment operation status based on Internet of Things technology

Intelligent monitoring method of electronic equipment operation status based on Internet of Things technology

A consistency method of discharge detection results at solar‐blind ultraviolet based on a radiation transmission model

Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses

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