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

Kumar, Haresh; Shafiq, Muhammad; Kauhaniemi, Kimmo; Elmusrati, Mohammed

doi:10.3390/en17051142

Cited by 5 publications

(1 citation statement)

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“…Zhang proposed a partial-discharge recognition method based on CNN that extracts the spatial features of partial-discharge signals through state–space reconstruction and improves the recognition efficiency of partial-discharge signals [ 23 ]. The CNN-based method incorporated with the long short-term memory (LSTM) network to recognize partial-discharge signals has also achieved success [ 24 ]; it uses CNN and the LSTM network to extract the spatial and temporal features of PD signals, respectively, and performs PD signal classification through the interaction of the two features. CNN extracts the PD signal features through layer-by-layer convolution, pooling, and other operations.…”

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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Section: Introductionmentioning

confidence: 99%