An Offline and Online Approach to the OLTC Condition Monitoring: A Review

Alnaimi, Firas Basim Ismail; Mazwan, Maisarah; Al-Faiz, Hussein; Marsadek, Marayati; Hasini, Hasril; Al-Bazi, Ammar; Ghazali, Young Zaidey Yang

doi:10.3390/en15176435

Cited by 14 publications

(6 citation statements)

References 45 publications

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“…Among the power transformer components, the On-Load Tap Changer (OLTC), which is used to regulate the transformer's output voltage, is identified as one of the most vulnerable parts. Its failure is among the main cause of major power transformers' catastrophic failures [3][4][5][6]. In this regard, it is necessary to detect OLTC faults in their incipient stages.…”

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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“…Consequently, if the voltage difference between the nodes becomes sufficiently large, there is a risk of overvoltage at bus 2. All voltage regulation methods involve changing one or more of the variables included in Equation (6). The basic and conventional method of voltage regulation in distribution networks is based on influencing the voltage at the main bus of the feeder, i.e., 𝑉 .…”

Section: Introductionmentioning

confidence: 99%

“…It is a cost-effective method, because most HV/MV transformers are equipped with an OLTC. Its disadvantages undoubtedly include a slow response time, including a lack of ability to regulate the voltage during transients, together with restricted tap changes each day due to the limited mechanical endurance of the OLTC [6].…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy OLTC Controller: Applicability in the Transition Stage of the Energy System Transformation

Wróblewski,

Kowalik,

Januszewski

et al. 2024

Energies

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This paper introduces a Fuzzy Logic Controller designed for an on-load tap changer within medium voltage distribution systems with bulk penetration of Distributed Energy Resources. As the on-load tap changer remains one of the most essential forms of voltage regulation in medium voltage distribution networks, improving its operation is a cost-effective response to the emerging voltage violations caused by intermittent generation during the early stages of the energy system transformation. Software-in-the-loop simulations were conducted to validate the effectiveness of the proposed algorithm compared to the conventional methods. A modified CIGRE Medium Voltage Distribution Network Benchmark in European Configuration was modelled while the controller code developed in Python 3.12 was running on a PC, both coupled in a real-time closed-loop environment. The analyses showed that the proposed algorithm managed to reduce overvoltage from 7.02% to 4.85% in the benchmark network, thus demonstrating that the algorithm is efficient and ready for on-field implementation.

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“…An On-Load Tap-Changer (OLTC) is one of the most important components of the power transformer and it is used to regulate the transformer voltage by changing the number of turns in one winding of the transformer (turns ratio). It is reported that around 30% of transformer faults are due to OLTC failures, hence the requirement for a timely condition assessment of this component [ 4 , 5 , 6 ].…”

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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An Offline and Online Approach to the OLTC Condition Monitoring: A Review

Cited by 14 publications

References 45 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

A Fuzzy OLTC Controller: Applicability in the Transition Stage of the Energy System Transformation

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

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