Junhyuck Seo

Probabilistic wavelet transform for partial discharge measurement of transformer

Partial discharge (PD) measurement provides a means for online monitoring and diagnosis of transformers. However, extensive interferences and noise can significantly jeopardize the measured PD signals and cause ambiguity in PD measurement interpretation. Necessary PD signal de-noising techniques need to be adopted and wavelet transform is one of such techniques. Mother wavelet selection, decomposition level determination and thresholding are important processes for effective PD extraction using wavelet transform. Various methods have been proposed in the literature to improve the above processes of wavelet transform. In these methods a single threshold is normally adopted at each decomposition level and a binary decision is made to indicate whether an extracted signal is PD signal or noise. However, in online PD measurements it is difficult to find a threshold, which can be used for extracting only PD signals without including any noise. As such, the signals determined by a single threshold cannot be assured as PD signals with certainty. To address the limitations caused by the single thresholding method in wavelet transform for PD signals extraction, this paper proposes quantile based multi-scale thresholding method at each decomposition level, which can thus provide probability indexes for the extracted signals evaluating the likelihood of these signals to be PD signals. To evaluate the proposed method, PD measurements have been conducted on both experimental PD models and inservice transformers at substation. The results are provided in the paper.

A Joint Vibration and Arcing Measurement System for Online Condition Monitoring of Onload Tap Changer of the Power Transformer

Vibration measurement has been adopted in some utilities in Australia, Canada, and several European countries for online condition monitoring of the power transformers onload tap changer (OLTC). By comparing measured vibration signals at different stages, it might be possible to assess changes of OLTCs condition. However, there are still considerable difficulties in correlating vibration signals to the events of OLTC operation, which may impair the capability of vibration measurement for OLTC condition monitoring. Therefore, arcing measurement is proposed in this paper to complement the vibration measurement. Arcing is provoked when the OLTC switching contact closes at a fixed tap position and can lead to electromagnetic signals flowing through transformer windings, finally reaching earth. The arcing measurement is achieved by using a high-frequency current transducer (HFCT) clamping on the transformers grounding cable. The joint vibration and arcing measurement can provide a better means for interpreting events involved in OLTC operation and facilitating improved OLTC condition monitoring. Since HFCT measured arcing signals can be coupled with noise, a probabilistic wavelet transform is thus employed in this paper to extract arcing signals from noise. Field measurements on two different types of OLTCs are performed using the joint vibration and arcing measurement system to validate the proposed methodology.

Partial discharge sources classification of power transformer using pattern recognition techniques

Continuous Partial discharge (PD) monitoring can help assess the integrity of transformer insulation system. Over the past few decades, various aspects of PD techniques have been investigated. Current research of PD focuses on multiple PD sources classification, which aims to identify the types of several defects that may coexist in a transformer and cause discharge. This paper develops a hybrid discrete wavelet transform (DWT) and support vector machine (SVM) algorithm targeting multiple PD sources classification. To evaluate the performance of this algorithm, experiments on a number of artificial PD models and transformers are conducted in the paper.

Signal extraction methods for online partial discharge (PD) measurement of power transformers

Wavelet transform-based Partial discharge (PD) signal extraction methods have been widely adopted in the past ten years. However, these methods may still encounter some difficulties in online PD measurements, in which PD signals can be overwhelmed by noise. To overcome the limitations of wavelet transform-based methods, the authors of this paper have developed two novel signal extraction methods. One is a multiscale thresholding based wavelet transform, which adopts multiscale thresholds and provides probability indices for extracted signals to indicate their likelihood of being PD signals. Another is a differential PD signal extraction method, which is based on evaluation of changing rates of acquired signals. It can be applied to online PD measurements even when multiple PD sources occur simultaneously in a transformer. This paper demonstrates the applicability of the two methods on the extraction of PD signals from online PD measurements of field transformers. Comparisons of the two methods are also provided in this paper.

Differential partial discharge extraction technique for online power transformer insulation assessment

Partial discharge (PD) measurement by using high frequency current transducer (HFCT) provides a means for online monitoring of power transformers. However, extensive interferences and noise can cause difficulties in PD signals interpretation and consequently lead to ambiguity in transformer insulation condition assessment. Therefore, necessary signal processing techniques need to be adopted for PD signals extraction. Wavelet transform is one of such techniques. However, wavelet transform has some limitations when applied to online PD measurement of power transformer. This paper proposes a differential PD signal extraction technique for online PD measurement on transformers. The performance of the proposed technique will be verified through case studies on data obtained from PD measurements on experimental PD models and in-service transformers.

Practical issues of online partial discharge measurement for in-service substation transformer

Partial discharge (PD) measurement provides a means for online monitoring and assessing the insulation condition of a substation transformer. However, the characteristics of PD pulses may vary depending on various factors such as geometry of insulation system, types of PD sources, properties of PD sensors and measurement systems, and sampling rate. Among these factors, sampling rate of PD measurement system has significant effect on the acquisition of PD waveform and the extraction of PD signals. This paper evaluated different sampling rates for online PD measurement of in-service substation transformers. The results of different sampling rates are compared in terms of the waveforms of the acquired PD signals, properties of the extracted PD signals, formation of PD patterns, and dataset sizes for proper PD evaluation, which will be presented in the paper. Other important issues such as online PD measurement system setup, filtration of power frequency harmonics, and PD signal extraction using wavelet transform are also discussed in this paper.

On Savitzky–Golay Filtering for Online Condition Monitoring of Transformer On-Load Tap Changer

Vibro-acoustic measurement on a transformers on-load tap changer (OLTC) can provide indications on its mechanical condition. Recently, a joint vibro-acoustic and arcing measurement system has been proposed, which can correlate the vibro-acoustic signal to mechanical events of OLTCs operation. However, there are still considerable difficulties in extracting useful information from both vibro-acoustic signal and arcing signal in a synchronized manner without any distortions in the extracted signals. In this paper, Savitzky–Golay filter is introduced to process the signals acquired from a joint vibro-acoustic and arcing measurement system installed on in-service OLTCs. It proves that the Savitzky–Golay filter can process both vibro-acoustic and arcing signals induced by OLTC, extract essential information without any time delay from both types of signals, and retrieve voltage phase information from the arcing signal. The methodologies developed in this paper can improve the visibility of OLTCs mechanical operation for an effective online condition monitoring.

Evolutionary Bayesian fusion for transformers fault detection

This paper presents an evolutionary Bayesian fusion method for transformer fault detection. It adopts Bayesian Network (BN) to explore the causal relationship between different potential faults inside transformer and fault symptoms; and then such knowledge is used to identify the fault types of transformer. Since Bayesian network acquires fault evidence gradually and transformer fault diagnosis is also an evolutionary process, the proposed method determines an optimal set of measurements, which need to be performed at each diagnostic step. This methodology can improve the accuracy of transformer fault identification while improve the efficiency of diagnostic process since the number of required measurements is minimized and only meaningful fault evidences are used in fault identification. Case studies are presented to verify the proposed method.

Sinusoidal noise suppression technique for partial discharge measurement of transformers

Partial discharge (PD) measurement system based on high frequency current transducer (HFCT) provides a convenient and cost-effective means for online insulation condition monitoring of transformers. However, it is a difficult task to extract PD signals from the measured signals overwhelmed by noise, which consequently leads to ambiguities in the condition assessment of transformers. This paper develops a novel noise cancellation technique for PD signals extraction during online PD measurement of transformers. This technique is based on an iterative multiplication process, which can suppress sinusoidal noise while maintains PD signals. The proposed technique will be verified through case studies on PD measurements performed on experimental models in laboratory and transformers at substations.

Vibration measurement and signal processing for condition assessment of OLTC of transformer

Vibration measurement is a non-invasive technique for online condition monitoring and assessment of power transformers On-Load Tap Changer (OLTC). In this paper novel signal processing techniques are proposed for evaluating conditions of tap positions of OLTC. Firstly, a signal pre-processing procedure is performed for obtaining a simplified vibration signal waveform of tap positions. Then, a correlation analysis is conducted for comparing waveforms of different tap positions and evaluating their conditions. The above techniques have been implemented on an OLTC condition monitoring system and verified through vibration measurement on the OLTC of a power transformer.