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Adaptive neuro-fuzzy inference system (ANFIS) flowchart for modelling transformer paper condition estimations.
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This article presents an algorithm for modelling an Adaptive Neuro Fuzzy Inference System (ANFIS) for power transformer paper conditions in order to estimate the transformer’s expected life. The dielectric characteristics, dissolved gasses, and furfural of 108 running transformers were collected, which were divided into 76 training datasets and ano...
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... proposed algorithm for building an ANFIS model for transformer paper condition estimation is shown in Figure 3 Energies 2017, 10, 1135 6 of 18 Dissolved Combustible Gas) CO and CO2 are a result of the thermal aging of paper insulation through an oxidation process [2,3,20]. The amount of dissolved carbon monoxide and carbon dioxide in oil could be correlated with the degree of polymerization and the tensile strength of the paper. ...
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Accurate diagnoses of faults in power transformers for life-long maintenance is ever-demanding. Transformers insulation excellence is known to deteriorate over time because of temperature fluctuationsand moisture contents, which significantly affects its duurability and functionality.developing an intelligent program for precise and efficient deter...
Citations
... Transformator mengalami penurunan kondisi seiring dengan bertambahnya waktu operasi karena berbagai proses penuaan dan hal yang mempercepat penuaan seperti kandungan air yang meningkat, suhu, dan proses oksidasi [1], [2]. Beberapa penelitian terdahulu telah melakukan penuaan thermal dipercepat terhadap sistem isolasi transformator untuk mengevaluasi penurunan kinerjanya. ...
The transformer insulation system in the form of oil-immersed paper, is the most important part of a transformer. This isolation system needs to be ensured that it has the appropriate characteristics, and is resistant to stress due to transformer operation. The most commonly used method for evaluating insulation life is the accelerated thermal aging test. Insulating oil-immersed paper is sealed in a vessel with a ratio in the Accelerated Thermal Aging Chamber with a volume capacity of 3600 ml. It takes about 3000 ml of oil to be poured into a tube with a ratio of insulating oil to insulating paper rolled on copper which is 10:1 and then heated in a thermal oven to accelerate aging. This study aims to develop an Accelerated Aging Chamber for evaluating the performance of transformer isolation systems. The developed test cell is able to reach a set-point of 150°C in 16 minutes with on/off control while PID control in 37 minutes with a Human Machine Interface for monitoring and collecting temperature data in real time, and maintaining that temperature until the end of the experimental period. Theresults of the sample will later be tested for the characteristics of the transformer insulation, namely tensile strength, color scale, andbreakdown voltage due to thermal stress.
... This algorithm consist of five layers, [28] which the circle indicate the fixed nodes in the layer and the square indicate an adaptive node. The first layers are full of adaptive terminals, and this layer output is indicated, ...
In this work, the transformer oil reclamation experimental test has been created utilising the physical-chemical reclamation technique and oil lifespan analysis using ANFIS algorithm. The significant of work is that it develops an ANFIS algorithm for estimating transformer life and analyzing transformer oil reliability. Rubber seed oil (mineral oil) is used in transformers to cool the substantial portion of the power transformer and decrease electrical ageing issues. These mineral oils interact chemically with the windings, suffering electrical and mechanical pressure owing to high temperatures over its power balance which leads to moisture and oxidation. In order to improve the performance of ageing oil, a physical and chemical reclamation approach with two primary steps, Coagulation and Adsorption, is used. Breakdown voltage, flash point, viscosity, and fire point are the important dielectric qualities of oil reclamation that will differentiate the performance between before and after reclamation when compared to diverse oil samples. The results of the work revealed that the physical-chemical reclamation process is enhanced the dielectric characteristics of the ageing oil, and the parameters of the reclaimed oil are utilised to predict the projected lifespan of the transformer service.
... Salah satu dampak yang timbul akibat pemasangan PV ini adalah kepada kinerja dari isolasi transformator. Isolasi transformator sendiri dapat menjadi salah satu faktor penentu masa pemakaian transformator [3], [4]. ...
ABSTRAK
Salah satu peralatan yang penting dalam sistem distribusi tenaga listrik adalah transformator. Penuaan transformator disebabkan oleh kerusakan isolasi yang diakibatkan dari proses degradasi kimia yang terakselerasi oleh oksidasi dan peningkatan suhu. Maka dari itu, untuk menilai loss of life transformator yang paling umum digunakan adalah menggunakan karakteristik thermal transformator. Peningkatan penggunaan photovoltaic (PV) yang mulai menyebar di berbagai daerah adalah salah satu bukti mulai diandalkannya energi terbarukan. Hal ini membawa dampak positif maupun negatif, baik bagi sistem tenaga secara keseluruhan, maupun bagi peralatan listrik. Artikel ini membahas berbagai penelitian terdahulu yang meneliti tentang pengaruh penggunaan PV terhadap penurunan kondisi transformator distribusi. Artikel review ini dibagi menjadi tiga bagian. Yang pertama membahas tentang akibat pemasangan PV yang mempengaruhi kinerja dari isolasi transformator berdasarkan pembebanan yang dialami saat operasi. Yang kedua membahas tentang pengaruh harmonisa yang dihasilkan oleh sistem PV terhadap penuaan transformator. Yang terakhir membahas tentang beberapa penelitian terdahulu yang membahas pendekatan untuk mengatasi harmonisa pada sistem.
ABSTRACT
One of the most important equipment in an electric power distribution system is a transformer. Transformer aging is caused by insulation breakdown resulted from chemical degradation processes that are accelerated by oxidation and increasing temperature. To assess the loss of life of the transformer, the most commonly used is to use the thermal characteristics of the transformer. The increasing use of photovoltaic which is starting to spread in various regions is one proof that renewable energy is starting to be relied on. This has both positive and negative impacts, both for the power system as a whole and for electrical equipment. This article discusses various previous studies that examined the effect of the use of photovoltaic on the deterioration of the distribution transformer condition. This review article is divided into three parts. The first is the effect of PV penetration which affects the performance of the transformer insulation based on the loading during operation. The second is the effect of harmonics generated by the PV system on the transformer aging. The last is to discuss some of the previous studies that proposed methods in reducing harmonics in the system.
... ey further articulated that if electrical and thermal insulation withstanding thresholds are exceeded, fault stresses occur which are noticeable through partial discharge, arcing, and localized overheating. Currently, in many utility organizations, transformer incipient faults are routinely diagnosed by dissolved gas analysis (DGA) approaches [5][6][7][8][9][10][16][17][18][19][20][21][22]. On the other hand, insulation degradation leads to aging stresses that also reduce transformer lifespan. ...
... is degradation is evidenced through oxidative, hydrolytic, and pyrolytic byproducts of oil-paper insulation system. ese stresses are assessed by measuring insulation variables such as moisture content, interfacial tension, dielectric strength, acidity, furanics, degree of polymerization (DP), dielectric dissipation factor (DDF), aldehydes, alcohols, and ketones [12,[16][17][18][19][20][21][22]. ...
Power transformers are essential assets in power system networks that must be meticulously monitored throughout their operational life cycle. Given that a substantial percentage of in-service power transformers around the world have reached the end of their projected technical lifespan, utilities are adopting various transformer condition-based maintenance strategies to minimize potentially devastating equipment failure. Thus, further usage of aging fleet of power system assets can be improved by gaining a better understanding of life threatening factors. In-service power transformers and their auxiliary components are susceptible to a variety of operational risks, which can compromise their performance and efficiency, thus resulting in devastating failures, financial losses, and power outages. Hence, it is necessary to investigate transformer life time issues so as to fully grasp the operational and performance status in order to reduce failures and operating costs. This paper presents a unified framework of attributes pertaining to life time issues for transformer operation evaluation together with a summary of recent findings in order to explore the current status and progress of this rapidly progressing field. The failure statistics analysis is presented first, followed by an examination of the transformer’s insulation degradation and aging mechanism. Furthermore, emphasis was on detailing the commonly adopted models and strategies in transformer condition evaluation, fault diagnostics, and remnant life estimation. Despite significant advancements in ascertaining transformer health diagnosis and prognosis technologies, including test accuracy, quick, precise fault localization, and fault type classification, there are still several shortcomings that require additional research, and thus future research perspectives are also discussed.
... Transformator mengalami penurunan kondisi seiring dengan bertambahnya waktu operasi karena berbagai proses penuaan dan hal yang mempercepat penuaan seperti kandungan air yang meningkat, suhu, dan proses oksidasi [1], [2]. Beberapa penelitian terdahulu telah melakukan penuaan thermal dipercepat terhadap sistem isolasi transformator untuk mengevaluasi penurunan kinerjanya. ...
Sistem isolasi transformator berupa kertas terendam minyak, adalah bagian yang paling penting dari suatu transformator. Sistem isolasi ini perlu dipastikan memiliki karakteristik yang sesuai, dan tahan terhadap stress karena operasi transformator. Metode yang paling umum digunakan untuk evaluasi masa pakai isolasi adalah uji penuaan termal dipercepat. Kertas terendam minyak isolasi disegel dalam bejana dengan rasio tertentu lalu dipanaskan dalam oven termal untuk mempercepat penuaan. Penelitian ini bertujuan mengembangkan Accelerated Ageing Chamber untuk evaluasi kinerja sistem isolasi transformator. Sel uji yang dikembangkan mampu mencapai set-point 150°C dalam 16 menit, dan menjaga pada suhu tersebut hingga akhir masa eksperimen.
... In [14], DGA and oil quality indices, along with paper insulation quality index, are classified and normalized in five groups, and a combination of fuzzy logic and support vector machine methods is used to determine the transformer health index. e DGA index is used to determine the faults that occurred in the transformer [15,16] and the oil quality index is obtained by the electrical, physical, and chemical oil parameters [12,13,15]. One of the common methods for calculating the DGA index for fault detection in transformers is artificial neural networks [16]. ...
... In [14], DGA and oil quality indices, along with paper insulation quality index, are classified and normalized in five groups, and a combination of fuzzy logic and support vector machine methods is used to determine the transformer health index. e DGA index is used to determine the faults that occurred in the transformer [15,16] and the oil quality index is obtained by the electrical, physical, and chemical oil parameters [12,13,15]. One of the common methods for calculating the DGA index for fault detection in transformers is artificial neural networks [16]. ...
Power transformers are one of the most significant and expensive equipment in power systems that are exposed to electrical, thermal, and chemical tensions. The transformer health index is a measure that uses test data and field inspections to assess the condition and determine the remaining life of the transformer. The purpose of this article as a new idea is to determine the relationships between electrical, physical, and chemical parameters of transformer oil, dissolved gases, and the transformer health index. One of the advantages of using the regression method in analyzing transformer data compared to the other methods to evaluate the transformer health index is determining the influence of the parameters that have the most impact on each other. Some achievements of this article are as follows: (1) introducing moisture content as the parameter that plays an effective role in reducing dielectric oil breakdown voltage and improving the transformer health index; (2) determining the inverse relationship between acidity and furfural components; (3) determining furfural as a parameter with the greatest role in reducing the Interfacial tension (IFT) of oil (molecular interconnection); (4) determining CO gas as the parameter with the most role in the production of furfural component; (5) determining C2H2 gas as the parameter with the most role in producing the acid component. For example, with a 1 ppm increase in the moisture component, the oil breakdown voltage decreases by 0.583 kV in the compound, growth, exponential, and logistic regressions, or with a 1 ppm increase in the furfural component, the oil interfacial tension decreases by 0.644 mN/m in power regression. In this article, the curve estimation regression method is used and the results are plotted by SPSS statistical software to analyze the interaction between different transformer parameters. To perform the simulations, test data related to 120 transformers have been considered.
... The expected lifetime estimation of power transformers[16] ...
Increasing the demand for EV charging has increased the burden and accretion of the power quality issues. Harmonic voltages and currents have a significant negative influence on power system components, specifically power transformers. The voltage and current harmonics created by EV chargers and their impacts on power transformers have been discussed in this paper, and an approach is proposed to reduce such harmonics in the system. For this purpose, firstly, the total harmonic distortion (THD) of a typical EV charger is evaluated. Then an analysis is performed utilizing Fast Fourier Transform (FTT) to extract individual harmonics. To this end, this paper addresses the power quality issues on the power transformers by implementing a passive filter. The harmonic voltages and currents were measured on different levels of charging loads. The simulation results show that more than 30% of total harmonic distortions were reduced to 1.16% using a passive filter. As a result, the approach has acceptable harmonic suppression performance in variable cases.
... The expected lifetime estimation of power transformers[16] ...
... The use of ML to assist power transformer condition assessment has been reported in several literatures [17][18][19]. This study investigates six different machine learning algorithms to support the graphical dissolved gas analysis fault identification method. ...
Dissolved gas analysis (DGA) is a powerful tool to monitor the condition of a power transformer. Several interpretation methods have been proposed, one of the most reliable of which is the graphical Duval triangle method (DTM). The method consists of several triangles, which still requires expertise for fault identification. The use of computer-based technology has been implemented in recent years to support transformer fault identification. However, no study has done thorough investigation on the use of suitable machine learning algorithm for the ML-based implementation of this matter. This study examines six commonly used machine learning algorithms to support DGA fault identification of power transformer: decision tree, support vector machine, random forest (RF), neural network, Naïve Bayes, and AdaBoost. Three DGA fault identification methods for mineral oil insulated transformer were studied, namely DTM1, DTM4, and DTM5. The training and testing datasets were generated for each DGA method, and trained to each ML algorithm. The tenfold cross validation was used to evaluate the results using five criteria, namely classification accuracy, area under curve, F1, Precision, and Recall. RF models demonstrated the best performance in classifying fault codes of most DGA methods. A validation was carried out using the validation dataset, comparing the selected RF-based models to the graphical DGA fault identification. The combination method was also implemented in the developed model. The results show that the proposed model is reliable, and especially useful to be used for fault identification of a large number of transformer populations.
... One of the most important elements of the power system is the power transformer. This is evidenced by both its function in the system and the cost of its replacement [7]. The reliable operation of the transformer is conditioned by meeting many requirements. ...
The article presents a method of determining dielectric losses that occur in insulating materials in a power transformer. These losses depend mainly on the electric field stress, pulsation, dielectric loss coefficient, and electrical permittivity of insulating materials. These losses were determined by integrating an expression describing unit losses. The determined dielectric losses were compared with the total losses of the transformer. It turned out that dielectric losses are a fraction of a percent of the total losses. The influence of the electrical permittivity of the insulating liquid and paper insulation on the value of dielectric losses was investigated. This influence was ambiguous, which is characteristic of stratified systems made of materials with different permittivity. An analysis of the influence of the dielectric loss coefficient tan(delta) on the value of dielectric losses in the transformer was carried out. The impact of this coefficient on the amount of dielectric losses turned out to be directly proportional.
