Enhanced partial discharge location determination for transformer insulating oils considering allocations and uncertainties of acoustic measurements

Taha, Ibrahim B. M.; Dessouky, Sobhy S.; Ghaly, Ramy N. R.; Ghoneim, Sherif S. M.

doi:10.1016/j.aej.2020.08.041

Cited by 16 publications

(2 citation statements)

References 16 publications

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“…Проведенный анализ отечественной [5,6] и зарубежной [7][8][9] литературы выявил наличие большого количества работ, посвященных методам и способам диагностирования высоковольтного оборудования.…”

Section: постановка задачиunclassified

Increasing the Reliability of Power Transformers Diagnosing under Seasonal Temperature Changes

Volchanina¹,

Kuznetsov²,

Горлов³

2021

ESC

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Section: постановка задачиunclassified

Increasing the Reliability of Power Transformers Diagnosing under Seasonal Temperature Changes

Volchanina¹,

Kuznetsov²,

Горлов³

2021

ESC

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“…With the advent of artificial intelligence (AI), there is the possibility of improving and automating PD localization and differentiating between different PD types [ 7 ]. Localization is a type of regression problem where the TDOA information obtained from four sensors can be given as input to an artificial neural network (ANN) to predict the x, y, and z coordinates of the PD source [ 8 , 9 ]. Additionally, localization errors from TDOA can also be corrected by using multiple radial basis function (RBF) neural networks [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Pathfinding Algorithms in Partial Discharge Localization in Power Transformers

Beura,

Wolters,

Tenbohlen

2024

Sensors

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The introduction of artificial intelligence (AI) to ultra-high-frequency (UHF) partial discharge (PD) monitoring systems in power transformers for the localization of PD sources can help create a robust and reliable system with high usability and precision. However, training the AI with experimental data or data from electromagnetic simulation is costly and time-consuming. Furthermore, electromagnetic simulations often calculate more data than needed, whereas, for localization, the signal time-of-flight information is the most important. A tailored pathfinding algorithm can bypass the time-consuming and computationally expensive process of simulating or collecting data from experiments and be used to create the necessary training data for an AI-based monitoring system of partial discharges in power transformers. In this contribution, Dijkstra’s algorithm is used with additional line-of-sight propagation algorithms to determine the paths of the electromagnetic waves generated by PD sources in a three-dimensional (3D) computer-aided design (CAD) model of a 300 MVA power transformer. The time-of-flight information is compared with results from experiments and electromagnetic simulations, and it is found that the algorithm maintains accuracy similar to that of the electromagnetic simulation software, with some under/overestimations in specific scenarios, while being much faster at calculations.

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Increasing the Reliability of Diagnosing Power Transformers by Using a Defect Simulator

Kuznetsov

Harlamov

Volchanina

et al. 2022

Lecture Notes in Networks and Systems

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Enhanced partial discharge location determination for transformer insulating oils considering allocations and uncertainties of acoustic measurements

Cited by 16 publications

References 16 publications

Increasing the Reliability of Power Transformers Diagnosing under Seasonal Temperature Changes

Increasing the Reliability of Power Transformers Diagnosing under Seasonal Temperature Changes

Application of Pathfinding Algorithms in Partial Discharge Localization in Power Transformers

Increasing the Reliability of Diagnosing Power Transformers by Using a Defect Simulator

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