Research

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12 pages, 632 KiB

Open AccessArticle

Partial Discharge Diagnostics: Data Cleaning and Feature Extraction

by Donny Soh, Sivaneasan Bala Krishnan, Jacob Abraham, Lai Kai Xian, Tseng King Jet and Jimmy Fu Yongyi

Energies 2022, 15(2), 508; https://0-doi-org.brum.beds.ac.uk/10.3390/en15020508 - 11 Jan 2022

Cited by 13 | Viewed by 2255

Abstract

Detection of partial discharge (PD) in switchgears requires extensive data collection and time-consuming analyses. Data from real live operational environments pose great challenges in the development of robust and efficient detection algorithms due to overlapping PDs and the strong presence of random white [...] Read more.

Detection of partial discharge (PD) in switchgears requires extensive data collection and time-consuming analyses. Data from real live operational environments pose great challenges in the development of robust and efficient detection algorithms due to overlapping PDs and the strong presence of random white noise. This paper presents a novel approach using clustering for data cleaning and feature extraction of phase-resolved partial discharge (PRPD) plots derived from live operational data. A total of 452 PRPD 2D plots collected from distribution substations over a six-month period were used to test the proposed technique. The output of the clustering technique is evaluated on different types of machine learning classification techniques and the accuracy is compared using balanced accuracy score. The proposed technique extends the measurement abilities of a portable PD measurement tool for diagnostics of switchgear condition, helping utilities to quickly detect potential PD activities with minimal human manual analysis and higher accuracy. Full article

(This article belongs to the Special Issue New Trends in Condition Monitoring and Diagnostics of Power System Assets)

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26 pages, 7536 KiB

Open AccessArticle

Automatic PRPD Image Recognition of Multiple Simultaneous Partial Discharge Sources in On-Line Hydro-Generator Stator Bars

by Ramon C. F. Araújo, Rodrigo M. S. de Oliveira and Fabrício J. B. Barros

Energies 2022, 15(1), 326; https://0-doi-org.brum.beds.ac.uk/10.3390/en15010326 - 04 Jan 2022

Cited by 9 | Viewed by 2677

Abstract

In this study, a methodology for automatic recognition of multiple simultaneous types of partial discharges (PDs) in hydro-generator stator windings was proposed. All the seven PD sources typical in rotating machines were considered, and up to three simultaneous sources could be identified. The [...] Read more.

In this study, a methodology for automatic recognition of multiple simultaneous types of partial discharges (PDs) in hydro-generator stator windings was proposed. All the seven PD sources typical in rotating machines were considered, and up to three simultaneous sources could be identified. The functionality of identifying samples with no valid PDs was also incorporated using a new technique. The data set was composed of phase-resolved partial discharge (PRPD) patterns obtained from on-line measurements of hydro-generators. From an input PRPD, noise and interference were removed with an improved version of an image-based denoising algorithm previously proposed by the authors. Then, a novel image-based algorithm that separates partially superposed PD clouds was proposed, by decomposing the input pattern into two sub-PRPDs containing discharges of different natures. From the sub-PRPDs, one extracts features quantifying the PD distribution over amplitudes and the contour of PD clouds. Those features are fed as inputs to several artificial neural networks (ANNs), each of which solves a part of the classification problem and acts as a block of a larger system. Once trained, ANNs work collaboratively to identify an unknown sample. Good results were obtained, with overall accuracies ranging from 88% to 94.8% for all the considered PD sources. Full article

(This article belongs to the Special Issue New Trends in Condition Monitoring and Diagnostics of Power System Assets)

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33 pages, 6865 KiB

Open AccessArticle

Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

by Ramon C. F. Araújo, Rodrigo M. S. de Oliveira, Fernando S. Brasil and Fabrício J. B. Barros

Energies 2021, 14(11), 3267; https://0-doi-org.brum.beds.ac.uk/10.3390/en14113267 - 03 Jun 2021

Cited by 10 | Viewed by 2526

Abstract

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate [...] Read more.

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected. Full article

(This article belongs to the Special Issue New Trends in Condition Monitoring and Diagnostics of Power System Assets)

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17 pages, 3192 KiB

Open AccessArticle

Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier

by Youcef Benmahamed, Omar Kherif, Madjid Teguar, Ahmed Boubakeur and Sherif S. M. Ghoneim

Energies 2021, 14(10), 2970; https://0-doi-org.brum.beds.ac.uk/10.3390/en14102970 - 20 May 2021

Cited by 43 | Viewed by 2866

Abstract

The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical and thermal fault classes [...] Read more.

The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical and thermal fault classes were categorized based on the IEC and IEEE standard rules. The concentration of five main combustible gases (hydrogen, methane, ethane, ethylene, and acetylene) was utilized as an input vector of the two classifiers. Two types of input vectors have been tested; the first input type considered the five gases in ppm, and the second input type considered the gases introduced in the percentage of the sum of the five gases. An extensive database of 481 had been used for training and testing phases (321 data samples for training and 160 data samples for testing). The SVM model conditioning parameter “λ” and penalty margin parameter “C” were adjusted through the bat algorithm to develop a maximum accuracy rate. The SVM-BA and Gaussian classifiers’ accuracy was evaluated and compared with several DGA techniques in the literature. Full article

(This article belongs to the Special Issue New Trends in Condition Monitoring and Diagnostics of Power System Assets)

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15 pages, 2436 KiB

Open AccessArticle

Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

by Suganya Govindarajan, Venkateshwar Ragavan, Ayman El-Hag, Kannan Krithivasan and Jayalalitha Subbaiah

Energies 2021, 14(6), 1564; https://0-doi-org.brum.beds.ac.uk/10.3390/en14061564 - 12 Mar 2021

Cited by 4 | Viewed by 1451

Abstract

Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted [...] Read more.

Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted features. Recent research posits that features extracted by singular value decomposition (SVD) can exhibit the natural characteristics and energy contained in the signal. Though the technique by itself is not novel, in this paper, SVD is employed for PD classification in a revised way starting from data arrangement in Hankel form, to embedding the hypergraph-based features and finally to extracting the required set of optimal features. The algorithm is tested for various measurement conditions that include the influences of various PD locations and oil temperatures. The robustness of the algorithm is also tested using noisy PD signals. Experimental results show the proposed feature extraction method supremacy. Full article

(This article belongs to the Special Issue New Trends in Condition Monitoring and Diagnostics of Power System Assets)

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16 pages, 5736 KiB

Open AccessArticle

A Convolutional Neural Network-Based Model for Multi-Source and Single-Source Partial Discharge Pattern Classification Using Only Single-Source Training Set

by Sara Mantach, Ahmed Ashraf, Hamed Janani and Behzad Kordi

Energies 2021, 14(5), 1355; https://0-doi-org.brum.beds.ac.uk/10.3390/en14051355 - 02 Mar 2021

Cited by 13 | Viewed by 2309

Abstract

Classification of the sources of partial discharges has been a standard procedure to assess the status of insulation in high voltage systems. One of the challenges while classifying these sources is the decision on the distinct properties of each one, often requiring the [...] Read more.

Classification of the sources of partial discharges has been a standard procedure to assess the status of insulation in high voltage systems. One of the challenges while classifying these sources is the decision on the distinct properties of each one, often requiring the skills of trained human experts. Machine learning offers a solution to this problem by allowing to train models based on extracted features. The performance of such algorithms heavily depends on the choice of features. This can be overcome by using deep learning where feature extraction is done automatically by the algorithm, and the input to such an algorithm is the raw input data. In this work, an enhanced convolutional neural network is proposed that is capable of classifying single sources as well as multiple sources of partial discharges without introducing multiple sources in the training phase. The training is done by using only single-source phase-resolved partial discharge (PRPD) patterns, while testing is performed on both single and multi-source PRPD patterns. The proposed model is compared with single-branch CNN architecture. The average percentage improvements of the proposed architecture for single-source PDs and multi-source PDs are 99.6% and 96.7% respectively, compared to 96.2% and 77.3% for that of the traditional single-branch CNN architecture. Full article

(This article belongs to the Special Issue New Trends in Condition Monitoring and Diagnostics of Power System Assets)

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18 pages, 4015 KiB

Open AccessArticle

Combined Approach Using Clustering-Random Forest to Evaluate Partial Discharge Patterns in Hydro Generators

by Ana C. N. Pardauil, Thiago P. Nascimento, Marcelo R. S. Siqueira, Ubiratan H. Bezerra and Werbeston D. Oliveira

Energies 2020, 13(22), 5992; https://0-doi-org.brum.beds.ac.uk/10.3390/en13225992 - 17 Nov 2020

Cited by 6 | Viewed by 2280

Abstract

The measurement and analysis of partial discharges (PD) are like medical examinations, such as Electrocardiogram (ECG), in which there are preestablished criteria. However, each patient will present his particularities that will not necessarily imply his condemnation. The consolidated method for PD processing has [...] Read more.

The measurement and analysis of partial discharges (PD) are like medical examinations, such as Electrocardiogram (ECG), in which there are preestablished criteria. However, each patient will present his particularities that will not necessarily imply his condemnation. The consolidated method for PD processing has high qualifications in the statistical analysis of insulation status of electric generators. However, although the IEEE 1434 standard has well-established standards, it will not always be simple to classify signals obtained in the measurement of the hydro generator coupler due to variations in the same type of PD incidence that may occur as a result of the uniqueness of each machine subject to staff evaluation. In order to streamline the machine diagnostic process, a tool is suggested in this article that will provide this signal classification feature. These measurements will be established in groups that represent each known form of partial discharge established by the literature. It was combined with supervised and unsupervised techniques to create a hybrid method that identified the patterns and classified the measurement signals, with a high degree of precision. This paper proposes the use of data-mining techniques based on clustering to group the characteristic patterns of PD in hydro generators, defined in standards. Then, random forest decision trees were trained to classify cases from new measurements. A comparative analysis was performed among eight clustering algorithms and random forest for choosing which is the superior combination to make a better classification of the equipment diagnosis. R² was used for assessing the data trend. Full article

(This article belongs to the Special Issue New Trends in Condition Monitoring and Diagnostics of Power System Assets)

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Review

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36 pages, 2324 KiB

Open AccessReview

Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review

by Arturo Y. Jaen-Cuellar, David A. Elvira-Ortiz, Roque A. Osornio-Rios and Jose A. Antonino-Daviu

Energies 2022, 15(15), 5404; https://0-doi-org.brum.beds.ac.uk/10.3390/en15155404 - 26 Jul 2022

Cited by 15 | Viewed by 2588

Abstract

Renewable energy-based power generation technologies are becoming more and more popular since they represent alternative solutions to the recent economic and environmental problems that modern society is facing. In this sense, the most widely spread applications for renewable energy generation are the solar [...] Read more.

Renewable energy-based power generation technologies are becoming more and more popular since they represent alternative solutions to the recent economic and environmental problems that modern society is facing. In this sense, the most widely spread applications for renewable energy generation are the solar photovoltaic and wind generation. Once installed, typically outside, the wind generators and photovoltaic panels suffer the environmental effects due to the weather conditions in the geographical location where they are placed. This situation, along with the normal operation of the systems, cause failures in their components, and on some occasions such problems could be difficult to identify and hence to fix. Thus, there are generated energy production stops bringing as consequence economical losses for investors. Therefore, it is important to develop strategies, schemes, and techniques that allow to perform a proper identification of faults in systems that introduce renewable generation, keeping energy production. In this work, an analysis of the most common faults that appear in wind and photovoltaic generation systems is presented. Moreover, the main techniques and strategies developed for the identification of such faults are discussed in order to address the advantages, drawbacks, and trends in the field of detection and classification of specific and combined faults. Due to the role played by wind and photovoltaic generation, this work aims to serve as a guide to properly select a monitoring strategy for a more reliable and efficient power grid. Additionally, this work will propose some prospective with views toward the existing areas of opportunity, e.g., system improvements, lacks in the fault detection, and tendency techniques that could be useful in solving them. Full article

(This article belongs to the Special Issue New Trends in Condition Monitoring and Diagnostics of Power System Assets)

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