Transformer Insulation Oil Research Articles

Impact of sol-gel synthesized α-aluminium oxide nanoparticles for the enhancement of transformer oil insulation properties

Transformers require advanced insulating materials that outperform mineral oil in terms of cooling, insulation effectiveness, eco-friendliness, and operational efficiency in extreme operational and weather conditions. As a result, emphasis has shifted to renewable and ecologically friendly materials. This change in emphasis is motivated by worries about the environment, fuel shortages, and the disposal of waste oil. Transformers have historically employed solid insulators like paper and pressboard as well as mineral or synthetic oil. However, the development of ecologically friendly insulating materials has been spurred by new environmental regulations as well as economic benefits. This research investigates the use of sol-gel synthesized aluminium oxide (Al2O3) nanoparticles to improve the insulation properties of host transformer oil. The synthesized aluminium nanoparticles were characterized with X-ray diffractometer (XRD) and found rhombohedral structure, whose chemical composition was studied by energy dispersive X-ray spectrum (EDAX). Investigation using Fourier transform infrared spectroscopy (FTIR) also revealed the production of AlOOH and hydroxyl vibrational bands at wavenumbers of 690 and 2976 cm−1, respectively. Studies using field emission scanning electron microscopy (FESEM) showed that the nanoparticles are spherical in form and have a diameter of around 38 nm. Additionally, the incorporation of α-aluminium oxide nanoparticles into the host oil showed an improvement in the breakdown voltage and suggested a possible use for better transformer oil insulation.

A Study on the Motion Behavior of Metallic Contaminant Particles in Transformer Insulation Oil under Multiphysical Fields.

When using transformer insulation oil as a liquid dielectric, the oil is easily polluted by the solid particles generated in the operation of the transformer, and these metallic impurity particles have a significant impact on the insulation performance inside the power transformer. The force of the metal particles suspended in the flow insulation oil is multidimensional, which will lead to a change in the movement characteristics of the metal particles. Based on this, this study explored the motion rules of suspended metallic impurity particles in mobile insulating oil in different electric field environments and the influencing factors. A multiphysical field model of the solid-liquid two-phase flow of single-particle metallic impurity particles in mobile insulating oil was constructed using the dynamic analysis method, and the particles' motion characteristics in the oil in different electric field environments were simulated. The motion characteristics of metallic impurity particles under conditions of different particle sizes, oil flow velocities, and insulation oil qualities and influencing factors were analyzed to provide theoretical support for the detection of impurity particles in transformer insulation oil and enable accurate estimations of the location of equipment faults. Our results show that there are obvious differences in the trajectory of metallic impurity particles under different electric field distributions. The particles will move towards the region of high field intensity under an electric field, and the metallic impurity particles will not collide with the electrode under an AC field. When the electric field intensity and particle size increase, the trajectory of the metallic impurity particles between electrodes becomes denser, and the number of collisions between particles and electrodes and the motion speed both increase. Under the condition of a higher oil flow velocity, the number of collisions between metal particles and electrodes is reduced, which reduces the possibility of particle agglomeration. When the temperature of the insulation oil changes and the quality deteriorates, its dynamic viscosity changes. With a decrease in the dynamic viscosity of the insulation oil, the movement of the metallic impurity particles between the electrodes becomes denser, the collision times between the particles and electrodes increase, and the maximum motion speed of the particles increases.

Transformer fault diagnosis based on DBO-BiLSTM algorithm and LIF technology

Abstract In response to the deficiencies of traditional power transformer fault detection techniques, such as low sensitivity and the inability for online monitoring, a novel transformer fault diagnosis model combining Laser-Induced Fluorescence (LIF) technology with deep learning is proposed. Initially, the spectral data of transformer insulation oil is acquired using LIF technology, yielding spectral data for various fault types. Subsequently, MinMaxScaler (MMS) and Standard Normalized Variate (SNV) methods are employed for denoising and preprocessing the spectral data. The preprocessed data is then subjected to dimensionality reduction using Linear Discriminant Analysis (LDA) and T-distributed Stochastic Neighbor Embedding (T-SNE) to ensure that the spectral data retains maximal feature information while minimizing its dimensionality. Following this, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), Dung Beetle Optimizer-Bi-directional Long Short Term Memory (DBO-BiLSTM), Convolutional Neural Network (CNN), and Support Vector Machine (SVM) models are constructed. The reduced-dimensional data is fed into each of the five models for training to facilitate transformer fault diagnosis. Through comparative analysis among the five models, the optimal model is selected. Experimental results indicate that the DBO-BiLSTM model is the most suitable for transformer fault diagnosis in this experiment, underscoring its significant implications for ensuring the safety of power systems.

KARAKTERISTIK TEGANGAN TEMBUS MINYAK TRAFO SHELL DIALA B MENGGUNAKAN MATERIAL ELEKTRODA BERBAHAN STAINLES STEEL, KUNINGAN, TEMBAGA DAN ALUMUNIUM

Transformer oil insulation breakdown voltage testing is usually carried out at high AC voltage using the SPLN 49-1 1982 standard. The electrodes used are hemispherical electrodes with a distance between the electrodes of 2.5mm. The breakdown voltage value of transformer oil insulation that meets the standards according to SPLN 49-1 of 1982 is 30 kV/2.5mm. In this research, a new Shell Diala B brand transformer oil insulation breakdown voltage test was carried out using the SPLN 49-1 1982 testing standard. The novelty of this research is using various electrode shapes and different electrode materials to test the breakdown voltage of the transformer oil's liquid insulation. The method used in this research is an experimental method where the test is adapted to the SPLN 49-1 standard of 1982. The electrodes used are ball, needle, and plate electrodes. Based on test results at a distance between electrodes of 2.5 mm, it was found that the breakdown voltage characteristics of stainless steel, brass, copper, and aluminum electrode materials varied and did not meet the SPLN 49-1 1982 standard, namely 30 kV/2.5 mm.

Pemeliharaan dan Perhitungan Umur Travo Jaringan Distribusi 20 KV

The distribution transformer is one of the main components in an electric power distribution system. Without a distribution transformer, consumers cannot use electrical energy directly considering that the operating voltage in the distribution system is 20 KV or what is called a medium voltage network. Disruptions that occur in distribution transformers will result in blackouts and obstruction of the distribution of electricity to consumers so that services for electricity needs will be disrupted. For this reason, routine and scheduled maintenance of distribution transformers is required which aims to prevent sudden equipment damage, as well as maintaining optimum equipment work according to its technical age, and safe for humans and the environment, as well as reliable in the electric power distribution system. One of the causes of disruption and damage to transformers include overvoltage due to lightning, overload and unbalanced loads, loss of contact at bushing terminals, broken insulators and failure of transformer oil insulation. These disturbances cause damage to distribution transformers and stop the flow of electricity. to consumers. In calculating the usage time of a transformer, we can predict the usage time of the transformer from calculating the voltage and current used every day, so if we know the usage time of a transformer, we can prevent damage to the distribution network by replacing the transformer before the transformer is damaged.

The Method of the Probability Analysis of Area with Dissolved Gases in Power Transformer Insulating Oil

Transformer insulating oil plays an extremely important role in power transformers and is used for insulation, arc suppression, cooling and other purposes. However, its other focus is on dissolved gases analysis in the oil to monitor internal operating conditions, which is the responsibility of preventive maintenance. This paper uses the normal distribution theory and the American National Standard Specification (hereinafter referred to as ANSI/IEEE C57.104) to reorganize as a diagnostic tool. The range of each gas from three stages of the specification - normal, caution, and abnormal are incorporated to a stage of abnormal, which divide into equal different values of 1000 as the maternal body of a qualitative normal distribution. Then the benchmark is been calculated from those parameters of a qualitative normal distribution. The data of detection has to pass through "Gas chromatography” to generate dissolved gases. At last, the benchmark value compares with dissolved gases to find the abnormal probability value as a diagnostic tool in maintenance evaluation of equipment. This method was developed using EXCEL application software, and repeatedly tested and verified with examples to confirm its feasibility and high accuracy. In addition, 10 cases accident data were extracted from the literature [11], and compared with the actual maintenance results. The accuracy was 80% for ANSI/IEEE C57.104 and 90% for this probability method. Therefore, this diagnostic method can replace the traditional multi-stage judgment and be represented by a single probability value to show. Based on technology sharing, the development process is specially written into a technical article as reference by scholars and electrical maintenance personnel in the field of power engineering.
 Design Methods: Making a qualitative normal distribution employs EXCEL software and the discriminative rule of ANSI/IEEE C57.104. Design Purpose: As a diagnostic tool with dissolved gases in insulating oil. Design Effectiveness: Probability Method is better than traditional; it has those efficacies - the accuracy and feasibly and compete with tried and tested actual case.

Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis

This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover, Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%, and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches.

Multi-expert GMM decision support system with AHP method for determining weight of transformer oil insulation quality index

The transformer is important and quite central in the power system for distributing energy from generators to consumers through electrical substations. Good transformer oil isolation is needed to protect the transformer to function optimally and prevent fatal interference. Over time, insulating oil experiences aging which can cause transformer damage and disruption in energy distribution to consumers. There are three things that affect the condition of transformer oil insulation, namely chemically testing for acidity, sediment (SED) and water content, from an electrical point of view testing for breakdown voltage (BDV) and testing dielectric dissipation factor (DDF), and from a physical point of view, color, and interfacial tension (IFT) tests are carried out. In dealing with this problem, a Decision Support System (DSS) is needed to calculate the oil quality index based on the given parameters. The method used is Analytical Hierarchy Process (AHP) with multi-expert GMM. The results show that the system is running well, and the calculation method is appropriate. Tests show 100% accuracy in comparing manual calculations with the Confusion Matrix system. Users give an average satisfaction of 92.94%. In conclusion, this system effectively meets the need and helps to determine the quality index of insulating oil in transformers.

Analysis of Microphone Coupling and Zero-Point Calibration Model for Non-Resonant Photoacoustic Spectroscopy

Dissolved Gas Analysis (DGA) of transformer insulation oil is an effective method for monitoring transformer operating conditions and diagnosing faults. Photoacoustic spectroscopy (PAS) is widely employed for online dissolved gas analysis in transformer oil. The positioning of the pressure equalization hole within the sound field significantly impacts the microphone’s sensitivity when coupled with the photoacoustic cell in non-resonant photoacoustic spectroscopy. The static temperature and humidity characteristics inside the photoacoustic cell also have a notable influence on the microphone’s output signal. Consequently, an analysis is performed to explore the relationship between the sensitivity of the photoacoustic spectrometer and temperature/humidity, leading to the optimization of the system configuration based on the microphone parameters. Moreover, the photoacoustic zero-point signal is influenced by the temperature and humidity levels within the photoacoustic cell. To address this problem, air mixtures at different humilities are measured, resulting in the establishment of a zero-point model for the background signal in Photoacoustic Spectroscopy (PAS). Subsequently, the experimental detection of acetylene under various humidity mixtures is conducted, successfully extracting effective signals for the measurement of 5 ppm acetylene gas. These results serve as evidence showcasing the efficacy of both the proposed structural optimization and zero-point model put forth in this study.

Nanoscale Investigation of Water Dynamics in Transformer Insulating Oil: Mechanisms of Droplet Nucleation and Growth

Nanoscale Investigation of Water Dynamics in Transformer Insulating Oil: Mechanisms of Droplet Nucleation and Growth

Scaled experimental study on the initial oil thickness effect on the combustion characteristic of the typical transformer insulating oil

Compared with mineral oil, plant insulation oil has a higher flash point, excellent natural degradation ability, and higher electrical insulation. It is expected to become the main component of transformer insulation oil, but the fire safety of this oil needs to be further analyzed and improved. In this paper, a cone calorimeter was used to test the combustion of typical plant insulating oil (camellia seed oil, soybean oil) and mineral oil (25# mineral oil) under different radiant heat flows and initial oil thickness. The combustion parameters such as ignition time, heat release rate, and carbon generation rate were measured. The combustion characteristics of 25# mineral oil were greatly affected by radiative heat flow and oil thickness. The peak heat release rate and carbon generation rate of mineral oil under high heat flow (50 kW/m2) were significantly higher than those under low heat flow (25 kW/m2) and increased with the increase of initial oil thickness. Plant insulating oil (camellia seed oil and soybean oil) had similar laws, but the peak value of the heat release rate and carbon generation rate of soybean oil were relatively small. Petrella fire risk assessment method showed that the fire risk ranking in descending order was mineral oil, camellia seed oil, and soybean oil, which provided a reference for the further popularization and application of plant insulation oil transformer.

Analysis of interference methods on transformers based on the results of dissolved gas analysis tests

<span lang="EN-US">In the operation of the power transformer, several maintenance efforts must be made to ensure the condition of the transformer is in good condition. The problems that usually arise are a thermal failure and electrical failure. The use of insulating media such as transformer oil and transformer insulation paper can be disrupted by this failure. Dissolved gas analysis, which identifies the types and concentrations of dissolved gas in transformer oil, can reveal details on fault indicators in power transformers (DGA). In this study, we used the interpretation of the IEEE std 2008-C57.104 (total dissolved combustible gas (TDCG), key gas, Rogers ratio method), the interpretation of IEC 2015-60599 (Duval triangle and basic gas ratio method), and the IEEE Std 2019-C57.104 interpretation (Duval pentagon method). The outcome of the DGA test is used to determine the conditions and indications of disturbances in the transformer for power. Using various gas analysis techniques also impacts the outcome of the fault indication. This variation has affected the types of gas used in the computation and the gas concentration limit value estimation. After the gas analysis, it was found that the oil purification process was also proven to reduce the concentration of combustible gases.</span>

The effect of electric field on the pyrolysis of transformer insulation oil–paper based on molecular dynamics

The regular operation of transformers is significantly impacted by the insulation effectiveness of the transformer insulation oil–paper. In order to explore the mechanism of the influence of an electric field on the thermal decomposition performance of insulating oil–paper, this paper simulated the process of electrothermal coupling decomposition of insulating oil–paper from the micro-level based on molecular dynamics. It was determined that the insulating oil is made up of three 16-carbon hydrocarbon molecules, while the insulating paper is made up of 30 fibrous disaccharide molecules. Using the molecular dynamics simulation approach, the pyrolysis of the insulating oil and insulating paper under various electric field strengths was simulated, and the lysis of reactants and the distribution of products were statistically examined. This paper also studied how the electric field affected the microscopic process of the insulating oil–paper pyrolysis. The findings demonstrate that under the influence of electrothermal coupling, the big molecules of the insulating oil and insulating paper are pyrolyzed to produce a variety of tiny molecules. For the insulating oil, it is easily subject to electron displacement polarization under the influence of an external electric field since it contains non-polar molecules, especially impacted by an electric field of 100 V/m. For the insulating paper, its polar nature, on the other hand, makes itself a good candidate for guiding polarization when exposed to an external electric field. So, the greater the electric field strength is, the greater the impact on the thermal decomposition of the insulating paper is.

Potential Characterization of Bush Mango Oil for Application in Transformer Insulation

In recent times, the suitability of some oils from edible fruits as a replacement for mineral oils used in transformers has been proven in different literature. The use of edible oils as transformer insulation fluid gives rise to the concept of green insulation in transformers. Research in the use of edible oil in the production of transformer oil has been one of the major researches in high voltage engineering. The existence of paucity of knowledge on the use of bush mango oil as dielectric fluid in transformers led to the idea of this research work. Therefore, this study focuses on the investigation of the suitability of using Irvingia Gabonensis Oil (IGO) for insulation in power transformers. From the series of analyses conducted, it has been established that the Irvingia Gabonensis (Bush Mango) oil is not suitable for transformer insulation oil. This is because the biobased green oil produced presented characteristics that are not suitable to match the standards recommended by the International Electrochemical Commission (IEC) and the American Society for Testing and Materials (ASTM).

Enhancement of the Characteristics of Natural Ester in Transformer Oil Insulation using Nanofluids

Oil is utilized in large transformers for its insulation properties and cooling. Mineral oil (MO) is not recyclable and it poses environment risks as it serves as insulation in transformer. It can be avoided by using a liquid coolant that has all the key qualities of transformer oil and seems to be biodegradable. Natural ester, made from plants as a substitute for mineral oil, has several profits. More investigations were conducted to improve the insulating liquid properties after the advent of nanofluids. In this study, a new hybrid method is employed to explore the properties by mixing different volume-percentage of hybrid nanomaterials with various natural esters. Evaluation of breakdown voltage and various transformer oil characteristics and results achieved showed that the hybrid nanofluid was essential in identifying a feasible alternative for mineral oil.

Research the Instrument of Karl Fischer Titration How to Detect the Moisture Content from Transformer Insulating Oil

This article focuses on the monitoring and control of the moisture content from the transformer insulating oil. When its moisture content exceeds the standard value, it will endanger the normal operation of the equipment and cause accidents. Research on how the moisture content penetrates into the insulating oil of the transformer. How to diagnose the moisture content is an important issue under the premise of affecting hidden worries about the quality of power supply？ Currently, there are three types of moisture content detecting: "visual", "calcium carbide method" and "liquid chromatography." This article will use the most accurate and effective "liquid chromatography", also referred to as "Karl Fischer coulometric titration" or "ASTM D6304-20", as the vertical axis and "monitoring and control" as the horizontal axis to analyze and discuss the moisture of generation in transformer insulating oil and detection methods to compose throughout them for an article. When the detecting oil sample has measured without contamination under normal procedures which has 30 to 35 ppm of moisture content, it is judged as a passing standard by ASTM D6304-20. If it exceeds standard level, the transformer equipment must be shut down for being deal with. To pay more attention on the item for detection of moisture content in insulating oil, so be doing that the transformer - related electrical accident failure which will be what It will not happen again similar accidents caused by moisture content. In addition to being a reference for maintenance technicians of substation equipment, this article can effectively monitor the moisture content inside the transformer to ensure the safe operation of the equipment.
 Detection Methods: the principle of the Karl Fisher Titration detection method is to employ the reaction of chemical between iodine (I2) and sulfur dioxide (SO2), and then absorb all the water (2H2O) from the insulating oil, while the chemical reaction produces sulfuric acid (H2SO4) and hydrogen iodide (2HI), so by doing to diagnose the moisture content in parts per million (ppm) of the oil sample.
 Detection Purpose: To explain "the Instrument of Karl Fischer Titration How to Detect the Moisture Content from Transformer Insulating Oil.”
 Detection Effectiveness: The most accurate and effective method of liquid chromatography, also known as Karl Fisher Titration which is employed for power companies.

Implementasi Metode Scoring Matrix dalam Menentukan Indeks Kondisi Minyak Isolasi pada Transformator Daya 150 kV

Transformers are critical equipment in electrical power system which assist in transmitting electrical energy from generators to consumers. Oil insulation is one of the key component in power transformer. Poor quality of oil insulation can be fatal to the transformer, especially when a disturbance occurs. Oil insulation can affect the aging of power transformers and the failure can disturb the continuity of electrical energy supplied to consumers. There are three things that affect the condition of transformer oil insulation, namely chemical, electrical, and physical. The test results of oil insulation parameters that were collected was assessed individually, such as Interfacial Tension (IFT), colour, breakdown voltage (BDV), acidity, and water content. Then, the condition assessment of power transformer oil insulation using a scoring matrix method proposed in CIGRE TB 761 was performed to determine the assessment index of oil insulation conditions of two 150 kV power transformers based on the results of various oil testing. Lastly, recommended action is provided based on the analysis.

Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks

Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method’s performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a R2 value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet.

Testing of Palm Oil-Based Electric Power Transformer Insulation Oil as a Renewable Energy Source

Reducing petroleum resources and environmental problems, alternative insulating oils with biodegradable characteristics have become an alternative. Esterification of palm-based oil is expected to provide better and optimum electrical properties. The esterification of palm oil will be carried out as a test sample and observed for its electrical, physical and chemical properties such as PD characteristics, breakdown voltage, dissipation factor, viscosity, flash point, etc. The effect of thermal aging on oil samples will also be carried out to simulate real situations in power transformer applications. Then after obtaining the best formulation from the sample it will be used as insulating oil in electric power transformers (100kVA). The purpose of this research is to apply esterified palm oil as an electric power transformer oil. The method used in this research includes field research. Field research includes taking oil samples, measuring the breakdown voltage of transformer oil, and the color of transformer oil. The results obtained are insulating oil for electric power transformers based on palm oil. Palm oil has the advantage of being an environmentally friendly, biodegradable and renewable oil compared to the transformer oil used today which is based on petroleum. With the realization of transformer oil based on palm oil, it is hoped that it can provide great economic opportunities for Indonesia as one of the largest palm oil producers in the world and can also provide added value and increase the competitiveness of the palm oil industry in the future.

Water Content in Transformer Insulation System: A Review on the Detection and Quantification Methods

Water can be an irritant to a power transformer, as it is recognized as a major hazard to the operation of transformers. The water content of a transformer insulation system comprises the water in the transformer insulation oil and in the cellulose paper. The increase in the water content in the insulation system leads to reduced breakdown voltage, accelerated aging of the oil–paper insulation system, and the possibility of producing bubbles at high temperatures. Therefore, various techniques have been applied to measure the water content in both oil and paper insulation. This article comprehensively reviews and analyzes the methods (technically or nontechnically) that have been used to monitor the water content in transformer insulation systems. Apart from discussing the advantages and major drawbacks of these methods, the accuracy, measurement time, and cost of each technique are also elucidated in this review. This review can be extremely useful to the utility in monitoring and maintaining the good condition of transformers. Based on the reviewed methods and their challenges, a few future research directions and prospects for determining the water content in transformer insulation systems are outlined, such as utilizing artificial intelligence and enhancing current techniques.