Comparative study of feature extraction methods applied to partial discharge signals

Liao, Ruijin; Taylor, Gareth; Tavernier, K.; Khan, Ozair Idris

doi:10.1109/upec.2012.6398585

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…Online PD monitoring techniques have drawn a lot of attention from academia and industry in the past ten years. Online CM systems allow for the detection and recording of PDs, creating a comprehensive database that can be utilized for additional analysis [9].…”

Section: Condition Monitoring and Pd Signalsmentioning

confidence: 99%

“…In both the positive and negative half cycles, PD activity appears at various phase angles of the voltage. Rapid charge movement and a high-frequency and low-amplitude transient current pulse (PD pulse), with a duration in the nanosecond-to-microsecond range and amplitude in mA, are the results of PD events [9]. These PD pulses' polarity is determined by the applied voltage's polarity [17].…”

Section: Condition Monitoring and Pd Signalsmentioning

confidence: 99%

“…It is commonly known that the wavelet transform (WT) is a useful tool for analyzing signals in the frequency and time domains. The WT's primary benefit is its variable time resolution, which results in longer effective time windows for slower processes at lower frequencies and shorter time windows for faster processes at higher frequencies [9]. The WT is a small waveform with zero average magnitudes and a very short period, and is used to break down the signals.…”

Section: Wavelet Transform-based Feature Extractionmentioning

confidence: 99%

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A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Kumar,

Shafiq,

Kauhaniemi

et al. 2024

Energies

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Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.

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Section: Condition Monitoring and Pd Signalsmentioning

confidence: 99%

Section: Condition Monitoring and Pd Signalsmentioning

confidence: 99%

Section: Wavelet Transform-based Feature Extractionmentioning

confidence: 99%

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A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Kumar,

Shafiq,

Kauhaniemi

et al. 2024

Energies

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“…To avoid the misinterpretation of signals, separation of defects is needed. The process of separation consists of features extraction and classification [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of PD Faults Using Features Extraction and K-Means Clustering Techniques

Kumar

Shafiq

Hussain

et al. 2020

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

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Partial discharge (PD) diagnostic is a crucial tool for condition monitoring of power system equipment (e.g. switchgear, cable) in the medium voltage (MV) network, which is degraded by the gradual deterioration of insulation elements, ageing, and various operational and environmental stresses. In the MV network, different types of PD faults are generated from different sources and to know the impact of an individual PD fault on the health of MV equipment, classification plays an important role. This paper aims to provide suitable techniques for classifying PD faults. The data is collected from an experimental investigation of three different types of PD faults from MV switchgear and classified using features extraction, dimensionality reduction and clustering techniques. To identify the best classification technique, dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbour embedding) are used, and their results are compared using the confusion matrix after applying k-means clustering technique.

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“…A number of other commonly used methods in extracting a PD signal are available for UHF pulse signal to recognize PD in power equipment. Examples of such methods are the time-frequency method, wavelet analysis method, empirical mode decomposition, and frequency domain analysis [3][4][5][6]. Information extracted by one method only reflects the signal characteristics from a point of view.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Identification Using Multiple Features Fusion Based on Evidence Theory and Support Vector Machine

2013

AMM

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The time resolved partial discharge (TRPD) signals collected by Ultra High Frequency (UHF) sensors of transformer contain important informations of the properties of faults. Wealth of information can be utilized for fault identification by different signal analyzing techniques, such as time domain analysis, frequency domain analysis and the wavelet analysis. An integrated characteristic information fusion system based on the Platt posterior probability model and support vector machine (SVM) one-against-one method is proposed. From the recognition result of the four typical insulation defects, the recognition system shows strong advantages in accuracy and reliability.

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Comparative study of feature extraction methods applied to partial discharge signals

Cited by 3 publications

References 7 publications

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Classification of PD Faults Using Features Extraction and K-Means Clustering Techniques

Partial Discharge Identification Using Multiple Features Fusion Based on Evidence Theory and Support Vector Machine

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