Fault Diagnosis of OLTC Based on Time-Frequency Image Analysis of Vibration Signal

Liu, Zhiyuan; Xia, Xinqian; Ji, Shengchang; Shi, Yuhang; Zhang, Fan; Fu, Yu; Jiang, Zhitong

doi:10.1109/cmd.2018.8535918

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…Another technique for the analysis of nonstationary signals is the Wavelet transform (WT), which decomposes a nonstationary signal in a set of frequency bands overtime. In [18][19][20], the WT is used to decompose vibrations signals, and thus, extract features that allow the fault detection in transformers. Some variations of the WT, i.e., wavelet package transform [21] and empirical wavelet transform [22], are also reported to extract features and diagnose different fault conditions in transformers.…”

Section: Related Literaturementioning

confidence: 99%

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

et al. 2020

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Transformers are vital and indispensable elements in electrical systems, and therefore, their correct operation is fundamental; despite being robust electrical machines, they are susceptible to present different types of faults during their service life. Although there are different faults, the fault of short-circuited turns (SCTs) has attracted the interest of many researchers around the world since the windings in a transformer are one of the most vulnerable parts. In this regard, several works in literature have analyzed the vibration signals that generate a transformer as a source of information to carry out fault diagnosis; however this analysis is not an easy task since the information associated with the fault is embedded in high level noise. This problem becomes more difficult when low levels of fault severity are considered. In this work, as the main contribution, the nonlinear mode decomposition (NMD) method is investigated as a potential signal processing technique to extract features from vibration signals, and thus, detect SCTs in transformers, even in early stages, i.e., low levels of fault severity. Also, the instantaneous root mean square (RMS) value computed using the Hilbert transform is proposed as a fault indicator, demonstrating to be sensitive to fault severity. Finally, a fuzzy logic system is developed for automatic fault diagnosis. To test the proposal, a modified transformer representing diverse levels of SCTs is used. These levels consist of 0 (healthy condition), 5, 10, 15, 20, and 25 SCTs. Results demonstrate the capability of the proposal to extract features from vibration signals and perform automatic fault diagnosis.

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Section: Related Literaturementioning

confidence: 99%

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

et al. 2020

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“…Concerning the main faults coming from the OLTC, OLTC faults can be categorized into three types: the oil fault (due to the generation of gasses), mechanical faults (mainly coming from tap changers contacts, mechanical wear of the springs, and loosed fasteners) and electrical faults (decrease of dielectric strength due to degradation of insulation oil and paper) [2,9,10]. In the following sub-section, different types of OLTC faults will be explained.…”

Section: Types Of Oltc Faultmentioning

confidence: 99%

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

et al. 2022

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Transformer failures have a significant cost impact on the operation of an electrical network. In many utilities, transformers have been operating for many years past their expected usable life. As power demand has surged, transformers in some areas are being loaded beyond their rated capacity to meet the demand. One of the vital components in a transformer is the on-load tap changer (OLTC), which regulates the voltage in the distribution network. This study aims to review several condition-monitoring techniques (online and offline) that can monitor the health of the OLTC and assure the safety of the transformer’s OLTC from irreparable damage by detecting the defect at an earlier stage, which is preceded by the specification of typical faults. This paper also discussed the common faults of the OLTC and the root causes of these faults. The OLTC is prone to mechanical faults due to its frequently changing mechanism in the tap operation. The OLTC are also prone to oil as well as thermal faults. As a result, it is critical to monitor OLTC conditions while they are in use. Proper management of condition monitoring (CM) for the OLTC is useful and necessary to increase availability and achieve optimised operating. Condition monitoring (CM) and diagnostics methods (DM) have been developing since the 1950s. CM and DM have been implemented to diagnose and detect an incipient fault, especially for the OLTC. Many techniques, online and offline, are being used to monitor the condition of the OLTC to prevent failure and minimize outages. These DM and CM will prolong the operational cycle and avoid a major disaster for the OLTC, which is an unfavorable scenario.

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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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Fault Diagnosis of OLTC Based on Time-Frequency Image Analysis of Vibration Signal

Cited by 7 publications

References 6 publications

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

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

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

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