The purpose of electrical insulation

Moses, G. L.

doi:10.1109/encaei.1960.7466616

Cited by 3 publications

(3 citation statements)

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“…Here, equation (1) states that if the grayscale intensity 𝐺(𝑖, 𝑗) of a pixel is greater than the threshold value 𝛼, it is assigned a value of 1 (foreground), representing a faulty region. Conversely, Equation 2 states that if the grayscale intensity 𝐺(𝑖, 𝑗) is less than or equal to α, the pixel is assigned a value of 0 (background), representing the background noise.…”

Section: Image Segmentationmentioning

confidence: 99%

“…Electrical protectors are a crucial part of all electrical structures. Insulators serve a crucial purpose in electrical systems by impeding or minimizing the flow of electric current between conductive materials exhibiting varying electrical potentials [1]. They function as protective barriers for electrical safety, provide structural support for electric components, and play a vital role in power transmission and distribution systems by curbing energy losses.…”

Section: Introductionmentioning

confidence: 99%

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Infrared Thermography Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning

Shafique,

Alam,

Rashid

et al. 2024

IEEE Access

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Power substations play a crucial role in ensuring the reliable transmission of electricity to residential and commercial establishments. This paper addresses the critical issue of insulator fault detection in electric substations, emphasizing the importance of timely identification to prevent accidents. Conventional inspection methods relying on manual intervention pose high risk, especially in high-voltage substations. To overcome this, infra-red (IR) thermal cameras are employed, but human analysis of IR thermal images is subjective and time-consuming. The current paper develops a computer vision system uses advanced segmentation algorithms to automatically detect and categorize defects in insulators based on Infrared Thermography (IRT) images. The proposed system combines unsupervised clustering for efficient training with supervised learning for accurate classification. In particular, it first employs clustering techniques for unsupervised learning to reduce human labeling needs. Then, in the supervised learning phase, a Gaussian kernel support vector machine (SVM) algorithm classifies various insulator defects using extracted features. The trained algorithm utilizes the extracted characteristics to effectively identify and classify several fault types in thermal images of insulators. The system's efficiency in quickly identifying faults enables timely intervention, preventing potential power outages and safety hazards.

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Section: Image Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Infrared Thermography Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning

Shafique,

Alam,

Rashid

et al. 2024

IEEE Access

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show abstract

“…Insulation systems play a pivotal role in the proper functioning and long-term operation of highvoltage electrical equipment such as transformers, cables, switchgear, motors and generators [1,2]. Insulation systems in transformers are divided into liquid and solid insulation [3].…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review

Thobejane,

Thango

2024

Preprint

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Electric Transformers make up some of the most critical components of a power system. Their reliable operation is critical for an efficient power grid as well as economic viability for the transformer operators and owners. Power transformers, like other High Voltage (HV) electrical equipment experience aging and insulation degradation due to chemical, mechanical and electrical forces during their operation. Partial Dischargers (PD) are localized electrical discharges that develop within insulation systems of HV electrical equipment such as transformers. In transformers, PD occurs in different forms and various locations and insulation both internal and external of the transformer. PD originates as small pulses but tends to increase in size and intensity which can result in complete insulation failure. Monitoring partial discharges has proven to provide valuable information on the state of the insulation systems of the power transformer, allowing transformer operators to make calculated decisions for the maintenance and life of plant plans. This systematic literature review aims to systematically examine the use of machine learning techniques in classifying PD in transformers to present a complete indicator to allow for future research in the field. The systematic review surveyed a total of 81 research literature published from 2010 to 2023 that fulfilled a specific methodology which was developed as part of this study. The results revealed that supervised learning has been the most widely used Artificial Intelligence (AI) algorithm utilized, primarily in the form of Support Vector Machine (SVM). Regarding PD, the survey revealed that most researchers tend to use numerous types of PD and compare them to one another. Furthermore, the use of artificial PD defect models to simulate the occurrence of PD is widely used versus the use of actual power transformers. Most of the literature tends to not specify the physical characteristics of PD, such as the magnitude of PD, PD inception voltage and PD extinction voltage.

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Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review

Thobejane,

Thango

2024

Applied Sciences

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Power transformers, like other High-Voltage (HV) electrical equipment, experience aging and insulation degradation due to chemical, mechanical and electrical forces during their operation. Partial discharges (PD) are among the most predominant insulation breakdown mechanisms. Monitoring partial discharges has proven to provide valuable information on the state of the insulation systems of power transformer, allowing transformer operators to make calculated decisions for maintenance, major interventions and plan for replacement. This systematic literature review aims to systematically examine the use of machine learning techniques in classifying PD in transformers to present a complete indicator of the available literature as well as potential literature gaps which will allow for future research in the field. The systematic review surveyed a total of 81 research literatures published from 2010 to 2023 that fulfilled a specific methodology which was developed as part of this study. The results revealed that supervised learning has been the most widely used Artificial Intelligence (AI) algorithm, primarily in the form of Support Vector Machine (SVM). The collected research indicated 20 countries represented in the publications, with China carrying out 32% of the research, followed by India with 10%. Regarding PD, the survey revealed that most researchers tend to investigate numerous types of PD and compare them to one another. Furthermore, the use of artificial PD defect models to simulate the occurrence of PD is widely used versus the use of actual power transformers. Most of the literature tends to not specify the physical characteristics of PD, such as the magnitude of PD, PD inception voltage and PD extinction voltage.

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The purpose of electrical insulation

Cited by 3 publications

References 0 publications

Infrared Thermography Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning

Infrared Thermography Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning

Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review

Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review

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