A Collective Condition Monitoring Algorithm for On-Load Tap-Changers

Feizifar, Behnam; Müller, Zdeněk; Fandi, Ghaeth; Usta, Ömer

doi:10.1109/eeeic.2019.8783320

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…The measurements are obtained whenever required. In real-time methods, the measurements also must be continuously acquired from an energized or in-service device [7][8][9]. In addition, real-time systems must respond to events quickly and accurately.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features

Dabaghi-Zarandi,

Behjat,

Gauvin

et al. 2024

Energies

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An On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages. In recent years, Hydro Quebec initiated a project to monitor the OLTC’s condition in power transformers using vibro-acoustic signals. A data acquisition system has been installed on real OLTCs, which has been continuously measuring their generated vibration signal envelopes over the past few years. In this work, the multivariate deep autoencoder, a reconstruction-based method for unsupervised anomaly detection, is employed to analyze the vibration signal envelopes generated by the OLTC and detect abnormal behaviors. The model is trained using a dataset obtained from the normal operating conditions of the transformer to learn patterns. Subsequently, kernel density estimation (KDE), a nonparametric method, is used to fit the reconstruction errors (regarding normal data) obtained from the trained model and to calculate the anomaly scores, along with the static threshold. Finally, anomalies are detected using a deep autoencoder, KDE, and a dynamic threshold. It should be noted that the input variables responsible for anomalies are also identified based on the value of the reconstruction error and standard deviation. The proposed method is applied to six different real datasets to detect anomalies using two distinct approaches: individually on each dataset and by comparing all six datasets. The results indicate that the proposed method can detect anomalies at an early stage. Also, three alarms, including ignorable anomalies, long-term changes, and significant alterations, were introduced to quantify the OLTC’s condition.

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Section: Introductionmentioning

confidence: 99%

Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features

Dabaghi-Zarandi,

Behjat,

Gauvin

et al. 2024

Energies

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“…There are several techniques adopted for the condition monitoring of an OLTC, such as dynamic resistance measurement (DRM), dissolved gas-in-oil analysis (DGA), vibro-acoustic signals analysis, motor-drive current analysis, etc. [ 10 ]. In this research work, vibro-acoustic signals analysis is used to evaluate the OLTC condition.…”

Section: Introductionmentioning

confidence: 99%

Power Transformers OLTC Condition Monitoring Based on Feature Extraction from Vibro-Acoustic Signals: Main Peaks and Euclidean Distance

Dabaghi-Zarandi

Behjat

Gauvin

et al. 2023

Sensors

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The detection of On-Load Tap-Changer (OLTC) faults at an early stage plays a significant role in the maintenance of power transformers, which is the most strategic component of the power network substations. Among the OLTC fault detection methods, vibro-acoustic signal analysis is known as a performant approach with the ability to detect many faults of different types. Extracting the characteristic features from the measured vibro-acoustic signal envelopes is a promising approach to precisely diagnose OLTC faults. The present research work is focused on developing a methodology to detect, locate, and track changes in on-line monitored vibro-acoustic signal envelopes based on the main peaks extraction and Euclidean distance analysis. OLTC monitoring systems have been installed on power transformers in services which allowed the recording of a rich dataset of vibro-acoustic signal envelopes in real time. The proposed approach was applied on six different datasets and a detailed analysis is reported. The results demonstrate the capability of the proposed approach in recognizing, following, and localizing the faults that cause changes in the vibro-acoustic signal envelopes over time.

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“…Voltage regulator switching contacts are among the most vulnerable components to failure, as they are the only moving parts of the equipment. Regulators are currently maintained through regular inspections or in the event of failures, however this repair procedure is time consuming, leaving the equipment out of service for a long time [12]. Preventive techniques for maintaining this equipment have been developed over the years to allow the early interpretation of an incipient failure and to define correct diagnoses, thus avoiding an unnecessary stop in the production process to carry out unscheduled maintenance [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“In Situ” Evaluation of Mechanical Wear of Mobile Contacts of Electricity Voltage Regulator

et al. 2020

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This in situ study presents an analysis of the wear of mobile contacts of switching in an automatic line voltage regulator, regarding the surface wear of the contacts. It is interesting to analyze the contacts of this equipment due to the friction and wear generated during the contact change, in order to predict and contain mechanical operation failures. The contacts were evaluated for mass loss, profilometry, and analysis in a scanning electron microscope, after remaining in operation in the regulator for about one year and switching the contacts almost 2500 times. This study aims to evaluate the performance of the contacts, in terms of surface wear, correlating the number of switching and the effects generated on the contacts in terms of loss of mass and surface damage. With the results obtained, it is possible to conclude that with each switching maneuver the mobile contact loses about 0.002895% of its initial mass. In addition, there was a change in the topography of the sample, associated with its wear, suggesting that the wear is different along the surface of the regulator contacts studied.

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A Collective Condition Monitoring Algorithm for On-Load Tap-Changers

Cited by 7 publications

References 42 publications

Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features

Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features

Power Transformers OLTC Condition Monitoring Based on Feature Extraction from Vibro-Acoustic Signals: Main Peaks and Euclidean Distance

“In Situ” Evaluation of Mechanical Wear of Mobile Contacts of Electricity Voltage Regulator

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