Assessment of insulation condition of large power transformers by on-site electrical diagnostic methods

Bräunlich, Reinhold; Hässig, Michael; Fuhr, J.; Aschwanden, T.

doi:10.1109/elinsl.2000.845527

Cited by 25 publications

(8 citation statements)

References 4 publications

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“…Convolution neural networks (CNNs) are used to enhance the accuracy of PD diagnoses in power transformers by utilising acoustic signals [37]. A CNN is a type of artificial neural network that is intended to process data with a grid-like structure, such as images or acoustic signals [38]. To use a CNN for PD diagnosis in power transformers, the acoustic signal is first pre-processed by applying filters to remove noise and enhance features that are relevant to PD activity [37][38][39].…”

Section: Convolution Neural Network (Cnns)mentioning

confidence: 99%

“…A CNN is a type of artificial neural network that is intended to process data with a grid-like structure, such as images or acoustic signals [38]. To use a CNN for PD diagnosis in power transformers, the acoustic signal is first pre-processed by applying filters to remove noise and enhance features that are relevant to PD activity [37][38][39]. The pre-processed signal is then fed into the CNN as input, and the network learns to automatically extract features that indicate PD activity.…”

Section: Convolution Neural Network (Cnns)mentioning

confidence: 99%

See 1 more Smart Citation

Time Reversal vs. Integration of Time Reversal with Convolution Neural Network in Diagnosing Partial Discharge in Power Transformer

Sekatane,

Bokoro

2023

Energies

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Partial discharge (PD) is a common issue in power transformers that can lead to catastrophic failures if left undetected. Time reversal (TR) is a well-known technique in signal processing that can reconstruct signals by reversing the direction of time. The paper investigates the use of time reversal and the integration of time reversal with convolution neural networks (CNNs) for diagnosing PD in power transformers. We compare the performance of these techniques on a dataset of PD signals collected from power transformers. We propose a novel method of using time reversal as a pre-processing step to improve the accuracy of CNNs on noisy or distorted signals. Our experimental results demonstrate that this approach can significantly enhance the performance of CNNs on various datasets, including speech, audio, and image datasets. This paper provides a novel approach to signal processing and demonstrates the potential of time reversal as a pre-processing step in CNNs.

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Section: Convolution Neural Network (Cnns)mentioning

confidence: 99%

Section: Convolution Neural Network (Cnns)mentioning

confidence: 99%

Time Reversal vs. Integration of Time Reversal with Convolution Neural Network in Diagnosing Partial Discharge in Power Transformer

Sekatane,

Bokoro

2023

Energies

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“…Selection of suitable diagnostic methods is the first important objective during the design of monitoring system. As the aging of transformer insulation system is affected mainly by changes of temperature and electrical operation characteristics [2], [3], [4], the observation of these characteristics is included in our monitoring system. The list of these characteristics is presented bellow.…”

Section: Selection Of Monitored Characteristicsmentioning

confidence: 99%

Instruments for on-line monitoring of transformers

Mentlík¹,

Prosr²,

Polanský³

et al. 2009

REPQJ

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The main objective of the paper is to describe the experience with newly developed monitoring system for transformers with oil-paper insulation system. From the view of operation reliability, it is necessary for operators to provide a monitoring of transformer's failure free operation. The application of monitoring system enables to warn timely of the rising failure and also decrease of degradation of single subsystems of machine. When failure is detected, it is important to support this detection by other measurements that would lead to exact determination of the failure. SFRA analysis (Sweep Frequency Response Analysis) is such additional method in our monitoring.

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“…At present, the acoustic emission method (AE), electromagnetic methods (high-frequency (HF), very-high frequency (VHF), and ultra-high frequency (UHF)), and dissolved gas analysis (DGA) are some of the most important PD detection methods adjusted to operating in online monitoring mode [16][17][18][19][20][21][22][23][24][25]. The last technique is indirect and because of its limitations, such as long time intervals between successive measurements, the sensitivity depending on the dimensions of transformer tank and distance of the gas sensor from the PD source as well as complex and non-standardized methods of DGA results interpretation, it is not capable to detect very quickly developing defects.…”

Section: Introductionmentioning

confidence: 99%

On-Line Partial Discharge Monitoring System for Power Transformers Based on the Simultaneous Detection of High Frequency, Ultra-High Frequency, and Acoustic Emission Signals

et al. 2020

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The article presents a novel on-line partial discharge (PD) monitoring system for power transformers, whose functioning is based on the simultaneous use of three unconventional methods of PD detection: high-frequency (HF), ultra-high frequency (UHF), and acoustic emission (AE). It is the first monitoring system equipped in an active dielectric window (ADW), which is a combined ultrasonic and electromagnetic PD sensor. The article discusses in detail the process of designing and building individual modules of hardware and software layers of the system, wherein the most attention was paid to the PD sensors, i.e., meandered planar inverted-F antenna (MPIFA), high-frequency current transformer (HFCT), and active dielectric window with ultrasonic transducer, which were optimized for detection of PDs occurring in oil-paper insulation. The prototype of the hybrid monitoring system was first checked on a 330 MVA large power transformer during the induced voltage test with partial discharge measurement (IVPD). Next, it was installed on a 31.5 MVA substation power transformer and integrated according to the standard IEC 61850 with SCADA (Supervisory Control and Data Acquisition) system registering voltage, active power, and oil temperature of the monitored unit. The obtained results showed high sensitivity of the manufactured PD sensors as well as the advantages of the simultaneous use of three techniques of PD detection and the possibility of discharge parameter correlation with other power transformer parameters.

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Assessment of insulation condition of large power transformers by on-site electrical diagnostic methods

Cited by 25 publications

References 4 publications

Time Reversal vs. Integration of Time Reversal with Convolution Neural Network in Diagnosing Partial Discharge in Power Transformer

Time Reversal vs. Integration of Time Reversal with Convolution Neural Network in Diagnosing Partial Discharge in Power Transformer

Instruments for on-line monitoring of transformers

On-Line Partial Discharge Monitoring System for Power Transformers Based on the Simultaneous Detection of High Frequency, Ultra-High Frequency, and Acoustic Emission Signals

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