Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

Carvalho, Itaiara Felix; da Costa, Edson Guedes; Nobrega, Luiz Augusto Medeiros Martins; Silva, Allan David da Costa

doi:10.3390/s24072226

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…Moreover, Refs. [22,23] differentiated between sources of partial discharges using envelope information from various sources .…”

Section: Methodsmentioning

confidence: 99%

Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning

Santos Júnior,

Serres,

Xavier

et al. 2024

Electronics

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This work presents a methodology for the generation and classification of phase-resolved partial discharge (PRPD) patterns based on the use of a printed UHF monopole antenna and signal conditioning circuit to reduce hardware requirements. For this purpose, the envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric discs with internal cavities in an oil cell, a potential transformer and tip–tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as a reference. After the acquisition of conditioned UHF signals, a digital signal filtering threshold technique was used, and peaks of partial discharge envelope pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector machine and decision tree algorithms. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work.

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“…Moreover, Refs. [22,23] differentiated between sources of partial discharges using envelope information from various sources .…”

Section: Methodsmentioning

confidence: 99%

Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning

Santos Júnior,

Serres,

Xavier

et al. 2024

Electronics

Self Cite

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“…Such tiny discharges can erode the insulation structure, leading to a localized increase in the temperature of the power equipment insulation and causing fires. Partial discharges in power equipment, if not resolved or detected in time, will continue to expand the deterioration area and eventually lead to overall insulation breakdown [ 4 , 5 , 6 , 7 ]. Partial discharges pose a serious threat to the safe operation of power equipment, and the ability to accurately detect localized discharge signals promptly is a critical step.…”

Section: Introductionmentioning

confidence: 99%

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

Deng,

Liu,

Zhu

et al. 2024

Sensors

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Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction–attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects.

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“…The results showed that the proposed SVM algorithm had a higher recognition accuracy and a faster convergence speed. Carvalho et al compared three clustering algorithms (K-means, Gaussian mixture model, and mean-shift) and the SVM method for PD classifications; the supervised SVM demonstrated a notably high average accuracy [5]. Furthermore, global optimization algorithms have been used to optimize the hyperparameters of SVM models in some studies.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Pattern Recognition Based on an Ensembled Simple Convolutional Neural Network and a Quadratic Support Vector Machine

Fei,

Li,

Yang

2024

Energies

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Partial discharge (PD) is a crucial and intricate electrical occurrence observed in various types of electrical equipment. Identifying and characterizing PDs is essential for upholding the integrity and reliability of electrical assets. This paper proposes an ensemble methodology aiming to strike a balance between the model complexity and the predictive performance in PD pattern recognition. A simple convolutional neural network (SCNN) was constructed to efficiently decrease the model parameters (quantities). A quadratic support vector machine (QSVM) was established and ensembled with the SCNN model to effectively improve the PD recognition accuracy. The input for QSVM consisted of the circular local binary pattern (CLBP) extracted from the enhanced image. A testing prototype with three types of PD was constructed and 3D phase-resolved pulse sequence (PRPS) spectrograms were measured and recorded by ultra-high frequency (UHF) sensors. The proposed methodology was compared with three existing lightweight CNNs. The experiment results from the collected dataset emphasize the benefits of the proposed method, showcasing its advantages in high recognition accuracy and relatively few mode parameters, thereby rendering it more suitable for PD pattern recognition on resource-constrained devices.

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Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

Cited by 4 publications

References 39 publications

Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning

Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

Partial Discharge Pattern Recognition Based on an Ensembled Simple Convolutional Neural Network and a Quadratic Support Vector Machine

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