Energies

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12 pages, 296 KiB

Open AccessArticle

Modelling the Effect of Thermal Aging on Transformer Oil Electrical Characteristics Using a Regression Approach

by Sifeddine Abdi, Abderrahmane Manu Haddad, Noureddine Harid and Ahmed Boubakeur

Energies 2023, 16(1), 381; https://doi.org/10.3390/en16010381 - 29 Dec 2022

Cited by 5 | Viewed by 2231

The effect of thermal aging on the electrical properties of the insulating oil used for transformers has been explored in this experimental work. In particular the dielectric dissipation factor, the resistivity and the breakdown voltage have been measured and correlated. The numerical results [...] Read more.

The effect of thermal aging on the electrical properties of the insulating oil used for transformers has been explored in this experimental work. In particular the dielectric dissipation factor, the resistivity and the breakdown voltage have been measured and correlated. The numerical results predicted by mathematical model and those measured in the laboratory have been compared by using a regression analysis. Experiments on thermal aging were performed on insulating oil (Borak 22, Nynas, Austria) during a period of time of 5000-h at three different temperatures. First, the transformer oil’s dielectric dissipation factor, the resistivity and the breakdown voltage are measured after every 500 h of aging. Then, polynomial and exponential regression expressions are proposed for modelling the oil’s electrical parameters variations with thermal ageing at different aging temperatures and periods. The results show that after thermal aging, the resistivity and the breakdown voltage decrease with thermal aging, however, the dielectric dissipation factor which increases. This trend is similar for all different aging temperatures. The numerical results show close agreement with the measured results for all the samples and all studied properties. The regression model presents strong correlation with high coefficients (>94%). Full article

(This article belongs to the Special Issue Advances in Oil Power Transformers)

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17 pages, 1612 KiB

Open AccessArticle

FEM-Based Power Transformer Model for Superconducting and Conventional Power Transformer Optimization

by Tamás Orosz

Energies 2022, 15(17), 6177; https://doi.org/10.3390/en15176177 - 25 Aug 2022

Cited by 7 | Viewed by 1900

Abstract

There were many promising superconducting materials discovered in the last decades that can significantly increase the efficiency of large power transformers. However, these large machines are generally custom-made and tailored to the given application. During the design process the most economical design should [...] Read more.

There were many promising superconducting materials discovered in the last decades that can significantly increase the efficiency of large power transformers. However, these large machines are generally custom-made and tailored to the given application. During the design process the most economical design should be selected from thousands of applicable solutions in a short design period. Due to the nonlinearity of the task, the cost-optimal transformer design, which has the smallest costs during the transformers’ planned lifetime, is usually not the design with the highest efficiency. Due to the topic’s importance, many simplified transformer models were published in the literature to resolve this problem. However, only a few papers considered this preliminary design optimization problem in the case of superconducting transformers and none of them made a comparison with a validated conventional transformer optimization model. This paper proposes a novel FEM-based two-winding transformer model, which can be used to calculate the main dimension of conventional and superconducting transformer designs. The models are stored in a unified JSON-file format, which can be easily integrated into an evolutionary or genetic algorithm-based optimization. The paper shows the used methods and their accuracy on conventional 10 MVA and superconducting 1.2 MVA transformer designs. Moreover, a simple cost optimization with the 10 MVA transformer was performed for two realistic economic scenarios. The results show that in some cases the cheaper, but less efficient, transformer can be the more economic. Full article

(This article belongs to the Special Issue Advances in Oil Power Transformers)

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18 pages, 4001 KiB

Open AccessArticle

Eco-Friendly Ester Fluid for Power Transformers versus Mineral Oil: Design Considerations

by Teresa Nogueira, José Carvalho and José Magano

Energies 2022, 15(15), 5418; https://doi.org/10.3390/en15155418 - 27 Jul 2022

Cited by 4 | Viewed by 2760

Abstract

Mineral oil has long been used as an adequate coolant and dielectric medium in power transformer design. However, it is flammable and environmentally risky as it may be leaked or spilled. Therefore, ester fluids, which have been increasingly used in the last two [...] Read more.

Mineral oil has long been used as an adequate coolant and dielectric medium in power transformer design. However, it is flammable and environmentally risky as it may be leaked or spilled. Therefore, ester fluids, which have been increasingly used in the last two decades, look promising as an ideal dielectric option. This research aims to better understand how using ester fluid insulation in power transformers impacts their physical and electrical dimensions, including their load-losses, impedance, masses, and equipment dimensions. Three case studies were carried out in a Portuguese electrical equipment manufacturer’s facility, with varying electrical parameters and physical properties of the mineral oil and ester-filled power transformers. The main results enhanced the known good electrical behavior of ester fluids, namely creating a lower electric field around winding wedges, yet the use of ester fluids led to higher load-losses, larger masses, additional radiators and, consequently, higher manufacturing costs. Nevertheless, the contribution of ester-filled power transformers to the improved environmental safety (reducing spillage and fire risks), among other advantages, makes ester fluids a truly eco-friendly option for power transformer design. Full article

(This article belongs to the Special Issue Advances in Oil Power Transformers)

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13 pages, 3072 KiB

Open AccessArticle

Application of Selected Machine Learning Techniques for Identification of Basic Classes of Partial Discharges Occurring in Paper-Oil Insulation Measured by Acoustic Emission Technique

by Tomasz Boczar, Sebastian Borucki, Daniel Jancarczyk, Marcin Bernas and Pawel Kurtasz

Energies 2022, 15(14), 5013; https://doi.org/10.3390/en15145013 - 8 Jul 2022

Cited by 6 | Viewed by 1280

Abstract

The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, [...] Read more.

The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established. Full article

(This article belongs to the Special Issue Advances in Oil Power Transformers)

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20 pages, 15077 KiB

Open AccessArticle

Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning

by Daria Wotzka, Wojciech Sikorski and Cyprian Szymczak

Energies 2022, 15(9), 3167; https://doi.org/10.3390/en15093167 - 26 Apr 2022

Cited by 6 | Viewed by 1960

Abstract

The article presents research on the influence of the type of UHF antenna and the type of machine learning algorithm on the effectiveness of classification of partial discharges (PD) occurring in the insulation system of a power transformer. For this purpose, four antennas [...] Read more.

The article presents research on the influence of the type of UHF antenna and the type of machine learning algorithm on the effectiveness of classification of partial discharges (PD) occurring in the insulation system of a power transformer. For this purpose, four antennas specially adapted to be installed in the transformer tank (UHF disk sensor, UHF drain valve sensor, planar inverted F-type antenna, Hilbert curve fractal antenna) and a reference log-periodic antenna were used in laboratory tests. During the research, the main types of PD, typical for oil-paper insulation, were generated, i.e., PD in oil, PD in oil wedge, PD in gas bubbles, surface discharges, and creeping sparks. For the registered UHF PD pulses, nine features in the frequency domain and four features in the wavelet domain were extracted. Then, the PD classification process was carried out with the use of selected methods of supervised machine learning. The study investigated the influence of the number and type of feature on the obtained classification results gained with the following machine-learning methods: decision tree, support vector machine, Bayes method, k-nearest neighbor, linear discriminant, and ensemble machine. As a result of the works carried out, it was found that the highest accuracies are gathered for the feature representing peak frequency using a decision tree, reaching values, depending on the type of antenna, from 89.7% to 100%, with an average of 96.8%. In addition, it was found that the MRMR method reduces the number of features from 13 to 1 while maintaining very high effectiveness. The broadband log-periodic antenna ensured the highest average efficiency (100%) in the PD classification. In the case of the tested antennas adapted to work in an energy transformer tank, the highest defect-recognition efficiency is provided by the UHF disk sensor (99.3%), and the lowest (89.7%) is by the UHF drain valve sensor. Full article

(This article belongs to the Special Issue Advances in Oil Power Transformers)

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20 pages, 6798 KiB

Open AccessArticle

Multichannel Detection of Acoustic Emissions and Localization of the Source with External and Internal Sensors for Partial Discharge Monitoring of Power Transformers

by Iago Búa-Núñez, Julio E. Posada-Román and José A. García-Souto

Energies 2021, 14(23), 7873; https://doi.org/10.3390/en14237873 - 24 Nov 2021

Cited by 4 | Viewed by 2274

Abstract

The detection of acoustic emissions with multiple channels and different kinds of sensors (external ultrasound electronic sensors and internal optical fiber sensors) for monitoring power transformers is presented. The source localization based on the times of arrival was previously studied, comparing different strategies [...] Read more.

The detection of acoustic emissions with multiple channels and different kinds of sensors (external ultrasound electronic sensors and internal optical fiber sensors) for monitoring power transformers is presented. The source localization based on the times of arrival was previously studied, comparing different strategies for solving the location equations and the most efficient strategy in terms of computational and complexity costs versus performance was selected for analyzing the error propagation. The errors of the acoustic emission source location (localization process) are evaluated from the errors of the times of arrival (detection process). A hybrid programming architecture is proposed to optimize both stages of detection and location. It is formed by a virtual instrumentation system for the acquisition, detection and noise reduction of multiple acoustic channels and an algorithms-oriented programming system for the implementation of the localization techniques (back-propagation and multiple-source separation algorithms could also be implemented in this system). The communication between both systems is performed by a packet transfer protocol that allows continuous operation (e.g., on-line monitoring) and remote operation (e.g., a local monitoring and a remote analysis and diagnosis). For the first time, delay errors are modeled and error propagation is applied with this error source and localization algorithms. The 1% mean delay error propagation gives an accuracy of 9.5 mm (dispersion) and a maximum offset of 4 mm (<1% in both cases) in the AE source localization process. This increases proportionally for more severe errors (up to 5% reported). In the case of a multi-channel internal fiber-optic detection system, the resulting location error with a delay error of 2% is negligible when selecting the most repeated calculated position. These aim at determining the PD area of activity with a precision of better than 1% (<10 mm in 110 cm). Full article

(This article belongs to the Special Issue Advances in Oil Power Transformers)

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22 pages, 4131 KiB

Open AccessArticle

Lightning Impulse Breakdown Voltage of Rice Bran Oil for Transformer Application

by Mardhiah Hayati Abdul Hamid, Mohd Taufiq Ishak, Nur Sabrina Suhaimi, Jaafar Adnan, Nazrul Fariq Makmor, Nurul Izzatul Akma Katim and Rahisham Abd Rahman

Energies 2021, 14(16), 5084; https://doi.org/10.3390/en14165084 - 18 Aug 2021

Cited by 2 | Viewed by 2184

Abstract

Transformer oil does not only serve as an insulating liquid, but also in removing heat from the windings and cores. Mineral oil (MO) has been widely used in transformers for more than 150 years. Recently, researchers have attempted to search for alternative insulating [...] Read more.

Transformer oil does not only serve as an insulating liquid, but also in removing heat from the windings and cores. Mineral oil (MO) has been widely used in transformers for more than 150 years. Recently, researchers have attempted to search for alternative insulating oils due to the possibility that MO will run out in the future together with the concern on fire safety and environmental pollution. Among the potential oils is rice bran oil (RBO). This work presents the studies of the lightning impulse (LI) of RBO behavior under various electric fields, gap distances and testing methods. The electrical performances of LI tests show that RBO and Palm Oil (PO) have lower LI breakdown voltage than MO under both uniform and non-uniform electric fields. However, the difference in LI breakdown voltages between RBO, PO and MO are slightly small which is less than 20%. In addition, there is no significant effect in the various testing methods under both uniform field and non-uniform field where the percentages of difference are less than 12% and 8% respectively. The data of LI breakdown voltage were statistically analysed to predict the withstand voltage and 50% breakdown voltage of oil samples by using Weibull distribution. The Weibull distribution of MO, PO and RBO has well fit with the fitting line. Finally, the relationship between LI voltages under a non-uniform field with various parameters of PO and RBO was obtained and proposed. From this work, it can be concluded that RBO shows promising results to be considered as an alternative to MO in transformer applications. Full article

(This article belongs to the Special Issue Advances in Oil Power Transformers)

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14 pages, 1209 KiB

Open AccessArticle

The Correlation of Transformer Oil Electrical Properties with Water Content Using a Regression Approach

by Sifeddine Abdi, Noureddine Harid, Leila Safiddine, Ahmed Boubakeur and Abderrahmane (Manu) Haddad

Energies 2021, 14(8), 2089; https://doi.org/10.3390/en14082089 (registering DOI) - 9 Apr 2021

Cited by 15 | Viewed by 3451

Abstract

An experimental investigation is conducted to measure and correlate the impact of the water content on the electrical characteristics of the mineral oil for transformers, particularly the breakdown voltage, the resistivity, and the dielectric dissipation factor. Regression method is carried out to compare [...] Read more.

An experimental investigation is conducted to measure and correlate the impact of the water content on the electrical characteristics of the mineral oil for transformers, particularly the breakdown voltage, the resistivity, and the dielectric dissipation factor. Regression method is carried out to compare the results obtained through laboratory experiments with those predicted using an analytical model. A treatment to reduce water content in oil involving filtration, degassing and dehydration using a SESCO mobile station was applied to the new, regenerated, and used oil samples in service. The breakdown voltage, the resistivity, and the dielectric dissipation factor of the samples were measured. Regression analysis using an exponential model was applied to examine the samples electrical properties. The results show that, after treatment, the breakdown voltage and resistivity increase as the water content decreases, unlike the dielectric dissipation factor which exhibits a decreasing trend. This trend is found to be similar for the three oil samples: new, regenerated, and used. The results of the regression analysis give close agreement with the experimental results for all the samples and all studied characteristics. The model shows strong correlation with high coefficients (>90%). Full article

(This article belongs to the Special Issue Advances in Oil Power Transformers)

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