Low-Pass Filter for HV Partial Discharge Testing

Kindl, Vladimir; Skala, Bohumil; Pechánek, Roman; Kus, Vaclav; Hornak, Jaroslav

doi:10.3390/s18020482

Cited by 9 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is usually built as a large inductor because the tested object insulation system, C x , exhibits a predominantly capacitive character. The coupling capacitor, when connected in series with the impedance Z m , creates a capacitive divider, converting the high-frequency current into a voltage signal detectable by the measuring instrument [57]. When a discharge occurs in the object under test, the coupling capacitor transfers a charge to it to compensate for the momentary collapse of the voltage across it [18].…”

Section: Conventional Detection Method: Iec 60270mentioning

confidence: 99%

Radiometric Partial Discharge Detection: A Review

et al. 2023

View full text Add to dashboard Cite

One of the most common failures or breakdowns that can occur in high-voltage (HV) equipment is due to partial discharges (PDs). This occurs as a result of inadequate insulation, aging, harsh environmental effects, or manufacturing flaws. PD detection and recognition methods have gained growing attention and have seen great progress in the past decades. Radiometric methods are one of the most investigated detection approaches due to their immunity to electromagnetic interference (EMI) and their capabilities to detect and locate PD activities in different applications such as transformers, cables, etc. Several review articles have been published to classify and categorize these works. Nonetheless, some concepts are missing, and some improvement techniques, such as PD detection at high-frequency (HF) and very high-frequency (VHF), have been overlooked. We present in this paper an exhaustive review study of state-of-the-art PD detection based on radiometric methods at different usable radiofrequency bands (i.e., HF, VHF, and UHF). Accordingly, we propose a new generic categorization approach based on the detected electromagnetic wave component (magnetic or electric fields) and pick-up location, either from free space or ground cable.

show abstract

Section: Conventional Detection Method: Iec 60270mentioning

confidence: 99%

Radiometric Partial Discharge Detection: A Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…. , y k 􏼈 􏼉 is a set of label results for input D. Te work in [22][23][24] tries to fnd outliers in the data and edit to identify them, while the work in [25,26] is for the classifcation and labeling of the target sequence, whether it is abnormal or normal. Te former is a regression method that regresses the input data into an exact value.…”

Section: Problem Defnitionmentioning

confidence: 99%

An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

Zhang

et al. 2023

Security and Communication Networks

View full text Add to dashboard Cite

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. Cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today’s network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model’s validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall.

show abstract

“…Several studies [1][2][3][4][5][6] dealt with the PD issues in general. Some of the recent studies [2,[7][8][9][10] were focused on the PD under DC from a specific point of view-pulse sequence analysis (PSA). The analysis seems to be a method to distinguish the PD type at DC voltage instead of the phase-resolved characteristics used at AC voltage.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Analysis and Simulation Using the Consecutive Pulses Correlation Method

Kozak

Pihera

2021

Energies

View full text Add to dashboard Cite

The behaviour of partial discharge as consequences of an alternating current (AC) is already well defined. AC partial discharges have completely different behaviour, background physics and parameters than partial discharges (PD) under direct current (DC) stress. This paper focuses on the most used and promising evaluation method of the PD DC stress—pulse sequence analysis (PSA). The first step is understanding and verifying the mechanisms and principles of this method. It is provided by well-known fundamentals of AC PD and by comparison with the other diagnostic and fault-locating methods such as phase-resolved partial discharge (PRPD) and pulse diagrams. The paper shows the PSA simulations and PD analyses performed at AC and partly at DC test conditions on typical PD test arrangements such as corona, surface and internal discharges. It is shown that the simulations performed, compared and validated with data obtained from measurements on different PD arrangements are a good match. This fact opens the way for the PD source recognition in DC, especially the time-resolved pulse sequence analysis described in detail in the paper.

show abstract

Low-Pass Filter for HV Partial Discharge Testing

Cited by 9 publications

References 23 publications

Radiometric Partial Discharge Detection: A Review

Radiometric Partial Discharge Detection: A Review

An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

Partial Discharge Analysis and Simulation Using the Consecutive Pulses Correlation Method

Contact Info

Product

Resources

About