A Denoising Method for Mining Cable PD Signal Based on Genetic Algorithm Optimization of VMD and Wavelet Threshold

Wang, Yanwen; Chen, Peng; Zhao, Yaohua; Sun, Ying

doi:10.3390/s22239386

Cited by 16 publications

(16 citation statements)

References 20 publications

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“…A denoising method that is based on WT and hard thresholding to eliminate remaining noise from the obtained PD signals was presented by Han et al [62]. Wang et al [63] suggested a denoising method based on wavelet threshold and VMD optimization with a genetic algorithm (GA) for mining cable PD signals. Additionally, thorough comparative analysis has been published in [64,65] to evaluate the effectiveness of WT, SVD, and VMD, the three denoising approaches, for acoustic signals produced by PD sources.…”

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: Wavelet Transform-based Feature Extractionmentioning

confidence: 99%

“…It is an iterative process, ending inheritance after a specified number of generations. The algorithm involves six steps: encoding, initial population generation, fitness value evaluation, selection, crossover, and mutation [63]. This feature selection method, the GA, is part of the wrapper approach [117].…”

Section: Genetic Algorithmmentioning

confidence: 99%

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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“…Considering this, noise rejection tools must operate in two steps. The first step should be focused on rejecting the non-pulsating noise signals by means of band stop or wavelet filters [ 2 , 15 , 16 , 17 , 18 , 19 , 20 ] and the second step should address rejecting pulsating noises by means of clustering tools [ 21 , 22 , 23 ]. The metrological tests are focused on analysing the error caused by the filtering tools when non-pulsating noise signals are rejected.…”

Section: Reference Pd Pulse Trains and Noise Signalsmentioning

confidence: 99%

Metrological Qualification of PD Analysers for Insulation Diagnosis of HVDC and HVAC Grids

Garnacho

Álvarez

Elg

et al. 2023

Sensors

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On-site partial discharge (PD) measurements have turned out to be a very efficient technique for determining the insulation condition in high-voltage electrical grids (AIS, cable systems, GIS, HVDC converters, etc.); however, there is not any standardised procedure for determining the performances of PD measuring systems. In on-line and on-site PD measurements, high-frequency current transformers (HFCTs) are commonly used as sensors as they allow for monitoring over long distances in high-voltage installations. To ensure the required performances, a metrological qualification of the PD analysers by applying an evaluation procedure is necessary. A novel evaluation procedure was established to specify the quantities to be measured (electrical charge and PD repetition rate) and to describe the evaluation tests considering the measured influence parameters: noise, charge amplitude, pulse width and time interval between consecutive pulses. This procedure was applied to different types of PD analysers used for off-line measurements, sporadic on-line measurements and continuous PD monitoring. The procedure was validated in a round-robin test involving two metrological institutes (RISE from Sweden and FFII from Spain) and three universities (TUDelft from the Netherlands, TAU from Finland and UPM from Spain). With this round-robin test, the effectiveness of the proposed qualification procedure for discriminating between efficient and inappropriate PD analysers was demonstrated. Furthermore, it was shown that the PD charge quantity can be properly determined for on-line measurements and continuous monitoring by integrating the pulse signals acquired with HFCT sensors. In this case, these sensors must have a flat frequency spectrum in the range between several tens of kHz and at least two tens of MHz, where the frequency pulse content is more significant. The proposed qualification procedure can be useful for improving the future versions of the technical specification TS IEC 62478 and the standard IEC 60270.

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“…To appropriately detect PD activity and perform accurate diagnosis, various measuring applications based on electromagnetic methods have been and continue to be developed [ 8 , 9 , 10 , 11 , 12 ]. These applications usually have specific functionalities, such as background noise filters to improve the sensitivity and PD source discrimination, as well as location and defect identification tools [ 3 , 4 , 13 , 14 ]. They also have the capacity to generate assisted or automatic alarms when a critical defect is identified [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Scale Modular Test Platform for the Characterization of PD Measuring Systems Using HFCT Sensors

Arcones,

Álvarez,

Khamlichi

et al. 2024

Sensors

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Today, online partial discharge (PD) measurements are common practice to assess the condition status of dielectrics in high-voltage (HV) electrical grids. However, when online PD measurements are carried out in electrical facilities, several disadvantages must be considered. Among the most important are high levels of changing electrical noise and interferences, signal phase couplings (cross-talk phenomena), and the simultaneous presence of various defects and difficulties in localizing and identifying them. In the last few decades, various PD-measuring systems have been developed to deal with these inconveniences and try to achieve the adequate supervision of electrical installations. In the state of the art, one of the main problems that electrical companies and technology developers face is the difficulty in characterizing the measuring system’s functionalities in laboratory setups or in real-world facilities, where simulated or real defects must be detected. This is mainly due to the complexity and costs that the laboratory setups entail and the fact that the facilities are permanently in service. Furthermore, in the latter scenario, owners cannot assign facilities to carry out the tests, which could cause irreversible damage. Additionally, with the aforementioned installations, a comparison of results over time in various locations is not possible, and noise conditions cannot be controlled to perform the characterizations in a correct way. To deal with the problems indicated, in this article, an affordable scale modular test platform that simulates an HV installation is presented, where real on-site PD measuring conditions are simulated and controlled. In this first development, the HV installation comprises a cable system connected at both ends to a gas-insulated substation (GIS). As the most common acquisition technique in online applications is based on the placement of high-frequency current transformer (HFCT) sensors in the grounding cables of facilities, the test platform is mainly adapted to carry out measurements with this type of sensor. The designed and developed test platform was validated to assess its features and the degree of convergence with a real installation, showing the convenience of its use for the appropriate and standardized characterization of PD-measuring systems.

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A Denoising Method for Mining Cable PD Signal Based on Genetic Algorithm Optimization of VMD and Wavelet Threshold

Cited by 16 publications

References 20 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

Metrological Qualification of PD Analysers for Insulation Diagnosis of HVDC and HVAC Grids

Scale Modular Test Platform for the Characterization of PD Measuring Systems Using HFCT Sensors

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