Investigation of electrical tree growth characteristics and partial discharge pattern analysis using deep neural network

Sahoo, Rakesh K.; Karmakar, Subrata

doi:10.1016/j.epsr.2023.109287

Cited by 16 publications

(3 citation statements)

References 27 publications

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“…Detecting PD is crucial for assessing the insulation condition of cable terminals. By identifying local discharges, the extent of insulation deterioration can be determined, allowing for timely maintenance or replacement measures [ 5 , 6 , 7 ]. Currently, technologies for the PD detection encompass a variety of methods, including the pulse current method, the high-frequency pulse current method, the ultra-high frequency detection method, the ultrasonic detection method, the optical measurement method, and the infrared imaging technology [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit Cable Termination

Sun,

Wu,

Pan

et al. 2024

Sensors

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Partial discharge detection is considered a crucial technique for evaluating insulation performance and identifying defect types in cable terminals of high-speed electric multiple units (EMUs). In this study, terminal samples exhibiting four typical defects were prepared from high-speed EMUs. A cable discharge testing system, utilizing high-frequency current sensing, was developed to collect discharge signals, and datasets corresponding to these defects were established. This study proposes the use of the convolutional neural network (CNN) for the classification of discharge signals associated with specific defects, comparing this method with two existing neural network (NN)-based classification models that employ the back-propagation NN and the radial basis function NN, respectively. The comparative results demonstrate that the CNN-based model excels in accurately identifying signals from various defect types in the cable terminals of high-speed EMUs, surpassing the two existing NN-based classification models.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit Cable Termination

Sun,

Wu,

Pan

et al. 2024

Sensors

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“…When PD occurs, repetitive pulsating signals of very short duration are generated. Several studies have demonstrated that PD activity is closely related to the degradation of the insulation materials of HV electrical grids [ 1 , 2 , 3 ]. PD measurement makes it possible to detect insulation defects before dielectric breakdown due to a short circuit.…”

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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“…The presence of PDs in HV assets is a symptom of the existence of insulation defects. The electrical tree growth and phase-resolved PD (PRPD) patterns have been studied by different authors to prevent failures in the power grids [1][2][3]. Over time, these defects can lead to short circuits and corresponding blackouts in HV networks.…”

Section: Introductionmentioning

confidence: 99%

Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks

Vera,

Garnacho,

Klüss

et al. 2023

Applied Sciences

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The insulation condition of HVDC grids consisting of cable systems, GIS, and converters should be monitored by partial discharge (PD) analysers using artificial intelligence (AI) tools for efficient insulation diagnosis. Although there are many experiences of PD monitoring solutions developed for the supervision of the insulation condition of HVAC grids using PD analysers, there are no standardised requirements for their qualification available yet. The international technical specification TS IEC 62478 provides general rules for PD measurements using electromagnetic methods but does not define performance requirements for qualification tests. HVDC and HVAC PD analysers must be tested by unambiguous test procedures. This paper compiles experiences of using PD analysers with HFCT sensors in HVAC grids (cable systems, GIS, and AIS) to define a qualification procedure for HVAC systems. This procedure is applicable to HVDC grids (cable systems, GIS, AIS, and converters) because the particularities related to the insulation behaviour under HVDC voltage are also considered. Representative PD sources are discussed in HVAC and HVDC positive and negative polarity. The PD pulse trend of representative insulation defects in HVDC cable systems is quite different from that of HVAC grids. Special attention should be paid to the acquisition of PD signals in HVDC grids since few pulses appear in solid insulations, mainly during voltage changes (polarity reversals or surges), but rarely in continuous operation with constant direct voltage. A synthetic PD simulator has been developed to reproduce trains of PD pulses or noise signals, similar to those that can appear in the power network. A set of three functionality tests has been developed for qualification of the diagnostic capabilities of PD analysers working up to 30 MHz addressed to HVDC or HVAC grids: (1) PD recognition test, (2) PD clustering test, and (3) PD location test. This qualification procedure has been validated by means of a round-robin test performed by five research institutes (RISE, FFII, TUDelft, TAU, and UPM) using commercial and in-development AI PD recognition and clustering tools to demonstrate its robustness and applicability. Applying this qualification procedure, two PD methods for electrical detection and prevention of insulation defects have been approved, one for HVAC and the other for HVDC grids.

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Investigation of electrical tree growth characteristics and partial discharge pattern analysis using deep neural network

Cited by 16 publications

References 27 publications

Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit Cable Termination

Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit Cable Termination

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

Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks

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