Deep neural networks for understanding and diagnosing partial discharge data

Catterson, Victoria M.; Sheng, Bojie

doi:10.1109/icacact.2014.7223616

Cited by 33 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different 2D convolution topologies were tested (usually with 64-128 filter channels); each stage was followed by the MaxPooling layer. Applying a lower number of filters (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) resulted in the rapid downgrading of the accuracy by 30%. Two types of kernel sizes were compared: 3 × 3 and 5 × 5 (input image size of 128 × 128 pixels; stride was equal to 2).…”

Section: Discussionmentioning

confidence: 99%

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Florkowski

2020

Energies

View full text Add to dashboard Cite

Artificial intelligence-based solutions and applications have great potential in various fields of electrical power engineering. The problem of the electrical reliability of power equipment directly refers to the immunity of high-voltage (HV) insulation systems to operating stresses, overvoltages and other stresses—in particular, those involving strong electric fields. Therefore, tracing material degradation processes in insulation systems requires dedicated diagnostics; one of the most reliable quality indicators of high-voltage insulation systems is partial discharge (PD) measurement. In this paper, an example of the application of a neural network to partial discharge images is presented, which is based on the convolutional neural network (CNN) architecture, and used to recognize the stages of the aging of high-voltage electrical insulation based on PD images. Partial discharge images refer to phase-resolved patterns revealing various discharge stages and forms. The test specimens were aged under high electric stress, and the measurement results were saved continuously within a predefined time period. The four distinguishable classes of the electrical insulation degradation process were defined, mimicking the changes that occurred within the electrical insulation in the specimens (i.e., start, middle, end and noise/disturbance), with the goal of properly recognizing these stages in the untrained image samples. The results reflect the exemplary performance of the CNN and its resilience to manipulations of the network architecture and values of the hyperparameters. Convolutional neural networks seem to be a promising component of future autonomous PD expert systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Florkowski

2020

Energies

View full text Add to dashboard Cite

show abstract

“…The first DNN applied for PD diagnosis was proposed by Catterson and Sheng in 2015 [14]. Their objective was to classify six different PD defects constructed in oil: (1) bad electrical contact, (2) floating potential, (3) and (4) metallic protrusion in two different configurations, (5) free particle and (6) surface discharge.…”

Section: Prpd Datamentioning

confidence: 99%

Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Barrios¹,

Buldain

Comech

et al. 2019

Energies

View full text Add to dashboard Cite

This paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems. There are several techniques for on-line PD measurements, but the typical classification and recognition method is made off-line and involves an expert manually extracting appropriate features from raw data and then using these to diagnose PD type and severity. Many methods have been developed over the years, so that the appropriate features expertly extracted are used as input for Machine Learning (ML) algorithms. More recently, with the developments in computation and data storage, DL methods have been used for automated features extraction and classification. Several contributions have demonstrated that Deep Neural Networks (DNN) have better accuracy than the typical ML methods providing more efficient automated identification techniques. However, improvements could be made regarding the general applicability of the method, the data acquisition, and the optimal DNN structure.

show abstract

“…Insulation condition monitoring in substations through partial discharge (PD) detection has drawn great attention since the last decade [1], [2], as PD signals contain an ample amount of information regarding the insulation status of electrical equipment [3], [4]. PD is not merely an indicator to degrading insulation level, rather it warns about an alarming condition that could henceforth lead to a complete breakdown.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Localization in 3-D With a Multi-DNN Model Based on a Virtual Measurement Method

Zaki

Jiang

2020

IEEE Access

View full text Add to dashboard Cite

Time difference of Arrival (TDOA)-based localization method, although used widely, calls for a fast and accurate solution owing to its time inefficiency and sensitivity to time delay estimation. In order to speed up the solution for nonlinear TDOA equations, while guaranteeing the location accuracy, this paper presents a hybrid approach namely multi-deep neural network model based on a virtual measurement method (MDNNM-VMM). Data consisting of multiple time difference values, resulting from a virtual measurement method (VMM), are fed to a pretrained multi-deep neural network model (MDNNM). These 'n' number of virtually generated sequences of time delays are obtained from a single set of TDOA equations, while conforming with a uniform distribution. The multi-DNN model using these data, outputs the required partial discharge (PD) coordinates that help determine accurate PD location. While applying a measurement error of 24 ns, the average error values r, θ , , and d for the proposed method, compared to a multi-DNN method, see a significant percentage decrease of 32 %, 24 %, 39 %, and 44 %, respectively. Additionally, varying simulated error, different array designs, and certain other parameters are studied to make the PD localization process more efficient and multifaceted. INDEX TERMS Deep neural network (DNN), partial discharge (PD) localization, time difference of arrival (TDOA), ultra-high frequency (UHF), virtual measurement method (VMM).

show abstract

Deep neural networks for understanding and diagnosing partial discharge data

Cited by 33 publications

References 13 publications

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Partial Discharge Localization in 3-D With a Multi-DNN Model Based on a Virtual Measurement Method

Contact Info

Product

Resources

About