Data-driven methodology to predict distribution lines failure location

Oliveira, Amanda Suellenn da Silva Santos; Leitão, Armando; Carvalho, Luís; Dias, L. F. S.; Guimarães, Lucas Rebouças; Ribeiro, Moisés V.

doi:10.1049/icp.2021.1830

Cited by 1 publication

(2 citation statements)

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“…Regarding transformers, many research studies have concentrated on dissolved gas analysis (DGA), which aids in estimating a transformer's health index (indicated by the dark gray box in Figure 5). [20,[22][23][24][25][26];…”

Section: Discussionmentioning

confidence: 99%

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Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review

Imam,

Musilek,

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2024

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Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing techniques and methods to minimize downtime in power networks and reduce maintenance costs. In addition to traditional statistical methods, modern technologies such as machine learning have become increasingly common for enhancing system reliability and customer satisfaction. The primary objective of this study is to review parametric and nonparametric machine learning techniques and their applications in relation to maintenance-related aspects of power distribution system assets, including (1) distribution lines, (2) transformers, and (3) insulators. Compared to other reviews, this study offers a unique perspective on machine learning algorithms and their predictive capabilities in relation to the critical components of power distribution systems.

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Section: Discussionmentioning

confidence: 99%

“…Oliveira et al [26] employ the extreme gradient boosting (XGBoost) algorithm to predict future failures and their location in high-voltage (HV) and medium-voltage (MV) distribution lines. The distribution line dataset is segmented into two groups corresponding to the HV and MV lines.…”

Section: Short Circuit Faultsmentioning

confidence: 99%