Localization of Incipient Discharge in Power Transformer Using UHF Sensor

Chakravarthi, M. Kalyan; Giridhar, A; Sarma, Dhiman

doi:10.1109/ichvet.2019.8724280

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chakravarthi et al [17] carried out PD tests on the electrode test cell using UHF sensors to measure the TDOA between captured signals and to evaluate the PD source localization using the particle swarm optimization (PSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Defect Localization Inside Simulated MV Switchgear Based on Cumulative Energy Curve Using Transient Earth Voltage Sensors

et al. 2021

View full text Add to dashboard Cite

The partial discharge (PD) leads to catastrophic failure of the medium voltage (MV) switchgear insulation. Determination of the PD source (defect) location in high voltage (HV) equipment is very important in the maintenance procedures and in isolating the root cause of PD generation. In this paper, the transient earth voltage (TEV) detection method was used to acquire defect-initiated PD signals in a simulated MV switchgear model. An array of four TEV sensors were placed on the surface walls outside an MV switchgear tank to acquire the PD signals generated from the known location(s)/coordinates of sharp needle type defect inside the tank. The time difference of arrival (TDOA) between signals that are captured by the TEV sensors array was critically analyzed. Estimating the TDOA between PD signals generated by PD source at a known location with high accuracy is of great importance for accurate defect localization. The cumulative energy method (CEM) is used to estimate the onset time point of each TEV signal. The estimated TDOA by the cumulative energy method is compared with actual and expected TDOA based on known coordinates of PD source and TEV sensors. Experimental data are used as a basis for determining the TEV method accuracy for PD source localization. Experimental results show the average error of time difference is about 1.34 ns, which is equivalent to the propagation distance of 0.4 m.

show abstract

Section: Introductionmentioning

confidence: 99%

Defect Localization Inside Simulated MV Switchgear Based on Cumulative Energy Curve Using Transient Earth Voltage Sensors

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To maintain the power system's healthiness by avoiding any sort of malfunction, accurate PD localisation is needed [6,7]. For this purpose, ultra-high frequency (UHF)-based methods have been acclaimed by researchers [8] despite being sensitive to time delay estimation and timeconsuming. It was due to the anti-interference attribute and stable transmitting speed of the electromagnetic waves, which made these methods a subject of research interest [9].…”

Section: Introductionmentioning

confidence: 99%

Accurate partial discharge localisation using a multi‐deep neural network model trained with a novel virtual measurement method

Zaki

Jiang

2021

IET Science Measure & Tech

View full text Add to dashboard Cite

Time difference of arrival-based localisation method has been extensively used by researchers for the partial discharge (PD) diagnosis despite being time-inefficient and sensitive to time delay estimation. Most of the contemporary work focuses on overcoming these problems by using data-driven approaches and/or statistical simulation methods. When used simultaneously, statistical simulation-based methods facilitate the data-driven approaches in terms of providing them with large amounts of data during the testing phase. However, the present work introduces a novel training phase strategy for a multi-deep neural network model (MDNNM). In this method, 'N' number of randomly generated PD sources in 3-D space are obtained statistically through virtual measurement method (VMM). Time delays amongst sensors, for the received ultra-high frequency signals from these 'N' PDs, are used in training of the MDNNM. This enhances the model's PD detection ability, as the obtained time delays realise the measurement error beforehand; and consequently, the model learns to predict the PD coordinates accurately. After applying this MDNNM trained with a novel VMM practically, the experimental results show that a location accuracy of 1 • can be obtained for a system error value of time difference up to 10 ns. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

MATLAB GUI-Based Partial Discharge Localization in Power Transformers Using UHF Acoustic Method

Welankiwar

Kudkelwar

2022

Sustainable Technology and Advanced Computing in Electrical Engineering

View full text Add to dashboard Cite

Localization of Incipient Discharge in Power Transformer Using UHF Sensor

Cited by 6 publications

References 7 publications

Defect Localization Inside Simulated MV Switchgear Based on Cumulative Energy Curve Using Transient Earth Voltage Sensors

Defect Localization Inside Simulated MV Switchgear Based on Cumulative Energy Curve Using Transient Earth Voltage Sensors

Accurate partial discharge localisation using a multi‐deep neural network model trained with a novel virtual measurement method

MATLAB GUI-Based Partial Discharge Localization in Power Transformers Using UHF Acoustic Method

Contact Info

Product

Resources

About