Oil-filled Power Transformers Research Articles

Investigating the effects of corrosion parameters on the surface resistivity of transformer’s insulating paper using a two-level factorial design

The integrity of the insulation in oil-filled power transformers, shunt reactors, and high voltage bushings can be affected when copper dissolves in the insulating oil and then deposits onto the paper insulation. The presence of dissolved copper in the oil increases dielectric losses, while copper deposition significantly improves the conductivity of the paper insulation. Various factors, including temperature, oxygen, sulfur groups, passivators, and ageing time, have been found to contribute to the acceleration of corrosion activity in transformer insulating oils. Unfortunately, there is a lack of extensive research focused on systematically analysing and measuring the impact of corrosion-related factors on the dissolution of copper in transformer insulating oils and the deposition of copper onto solid insulation surfaces (Kraft paper). Therefore, this study aims to thoroughly examine the effects of corrosion factors on copper and sulfur deposition on Kraft paper insulation when it is submerged in transformer mineral oil (TMO). Using a two-level (2k) factorial design, we investigated three crucial factors: i) oil temperature, ii) elemental sulfur concentration, and iii) ageing time. It is worth mentioning that the results obtained from the two-level factorial design indicate that the surface resistivity is primarily affected by the temperature of the oil. This factor alone explains a significant 38.68% of the observed variation. In order to improve predictability, a regression model was created to estimate the surface resistivity of TMO-impregnated paper insulation. This model takes into account factors such as oil temperature, elemental sulfur concentration, and ageing time.

Simulation Study on Acetylene Gas Diffusion Process in Large Transformers

To discover the influence of the diffusion process of acetylene gas in large oil filled power transformers on the dissolved gases analysis results, the diffusion process of acetylene gas over long distances and the influencing factors have been investigated by simulation. In this paper, based on the convection-diffusion equation of acetylene gas in oil, a one-dimensional model for the diffusion of acetylene gas in a 10 m long oil duct is established and numerically solved by the backward difference method. The simulation found that in right-angle coordinates, the concentration of acetylene gas in the oil is uniformly diffused outward, and the arrival time of the gas is related to the flow rate; in polar coordinates, the acetylene gas is mainly concentrated in the area near the defects and the tank wall, and the distribution of the gas content is extremely inhomogeneous, and the homogeneous state can not be reached for a long time. The results show that the simulation of gas production from local defects in large transformers is closer to reality by using polar coordinate form and boundary flux conditions; because the diffusion coefficient is very small in magnitude, the slow flow of transformer oil leads to the predominant convective transfer of acetylene gas.

Transformer hot spot temperature estimation through adaptive neuro fuzzy inference system approach

Transformer performance and efficiency can be enhanced by effectively address the properties of its insulation system. The power transformer insulation system weakens as a result of operational thermal stresses brought on by dynamic loading and shifting environmental patterns. Winding hot spot temperature is a crucial metric that must be maintained below the prescribed limit while power transformers are operating so as to maintained power system reliability. This is due to the fact that, among other variables, the time-dependent aging effect of insulation depends on transitions in hot spot temperatures. Due to the non‐linear nature of the conventional mathematical models used to determine these temperatures, and complexity of thermal phenomena, investigations still need to be exercised to fully understand the variables that associate with hot spot temperature computation with minimum error. This paper explores the possibilities of enhancing top oil and hot spot temperature estimation accuracy through the use of an adaptive neuro-fuzzy inference (ANFIS) technique. The paper presents an adaptive neuro fuzzy model to approximate the hot spot temperature of a mineral oil-filled power transformer based on loading, and established top oil temperature. Initially, a sub-ANFIS top oil temperature estimation model based on loading and ambient temperature as inputs is established. Using a hybrid optimization technique, the ANFIS membership functions were fine-tuned throughout the training process to reduce the difference between the actual and anticipated outcomes. The correctness and reliability of the created adaptive neural fuzzy model have been verified using real-world field data from a 60/90MVA, 132 kV power transformers under dynamic operating regimes. The ANFIS model results were validated against field measured values and literature-based electrical-thermal analogous models, establishing a precise input-output correlation. The developed ANFIS model achieves the highest coefficient of determination for both TOT and HST (0.98 and 0.96) and the lowest mean square error (7.8 and 10.3) among the compared thermal models. Correct determination of HST can help asset managers in thermal analysis trending of the in-service transformers, helping them to make proper loading recommendations for safeguarding the asset.

Development of a simulation model for assessing the technical condition of the magnetic circuit of oil power transformers by measuring the temperature of the tank and the external environment

In the article, the authors analyzed how oil-filled power transformers ensure reliable and stable operation of power transmission systems by providing efficient transmission of electricity of different voltages. In the article, the authors found that the main elements of oil-filled power transformers - the magnetic circuit, the coil and the technical condition of the oil - directly determine the performance of oil-filled power transformers. In this work, a simulation model was developed to evaluate the magnetic circuit of power oil transformers based on the measured temperature of the tank and the external environment during express diagnosis. A simulation model has been developed that allows to calculate the technical condition of oil power transformers operating in city power networks. It is shown that the developed model allows to calculate the value of the technical condition of the oil power transformer with the help of diagnostic parameters. In order to represent the technical state of the magnetic circuit of the oil power transformer, the functions of changing the state value of the output variable in the Mamdani fuzzy logic method are shown and the block diagrams are drawn up. Author’s using the Mamdani fuzzy logic method, express diagnosis of the technical condition of the “Magnetic core of the oil power transformer” was carried out in the Matlab simulink program. During the evaluation, the value of the output parameter is taken from 0 to 100, and each step is divided by 10, the smallest value is 0, and the largest is 100.

Effect of Fullerene and Graphene Nanoparticles on the AC Dielectric Strength of Natural Ester

The current study addresses the impact of the addition of fullerene and graphene nanoparticles on the AC breakdown voltage (AC BdV) of natural ester (FR3). The nanofluids (NFs) were prepared following the two-step process, and the AC BdV was performed in compliance with IEC 60156 standards. Five different concentrations of NPs were considered (0.1 g/L, 0.2 g/L, 0.3 g/L, 0.4 g/L, and 0.5 g/L). A Student’s t-test was performed to compare the base liquid’s AC BdV data with different nanofluids. The experimental data were checked to see if they obeyed the Weibull distribution fitting curve, and the AC BdV at 1%, 10%, and 50% risk levels were then calculated. The performed t-test provides evidence that AC BdV data from the base liquid were different from those of different NFs (except 0.2 g/L fullerene, and 0.1 g/L and 0.4 g/L graphene NFs). It is also shown that the Weibull distribution fit the BdV data of all liquids (except 0.5 g/L fullerene NF), and remarkable improvements of AC BdVs at 1%, 10%, and 50% were observed. The best improvement was obtained with 0.4 g/L fullerene and 0.3 g/L graphene NFs. These results show the importance of using nanofluids as substitutes for the existing insulating liquids for current oil-filled power transformers.

Detection and Analysis of Partial Discharges in Oil-Immersed Power Transformers Using Low-Cost Acoustic Sensors

Partial Discharge (PD) is one of the symptoms of an electrical insulation problem, and its permanence can lead to the complete deterioration of the electrical insulation in high-voltage equipment such as power transformers. The acoustic emission (AE) method is a well-known technique used to detect and localize PD activity inside oil-filled transformers. However, the commercially available monitoring systems based on acoustic sensors still have a high cost. This paper analyses the ability of low-cost piezoelectric sensors to identify PDs within oil-filled power transformers. To this end, two types of low-cost piezoelectric sensors were fully investigated using time-domain, frequency-domain, and time-frequency analysis, separately. Thereafter, the effectiveness of these sensors for PD detection and monitoring was studied. A three-phase distribution transformer filled with oil was examined. PDs were produced inside an oil-immersed transformer by applying a high voltage over two copper electrodes, and the AE sensors were coupled to the housing of the transformer. By extracting typical features from the AE signals, the PD signals were differentiated from on-site noise and interference. The AE signals were analyzed using acoustic signal metrics such as peak value, energy criterion, and other statistical parameters. The obtained results indicated that the used low-cost piezoelectric sensors have the capability of PD monitoring within power transformers.

Design of a compact ultra-high frequency antenna for partial discharge detection in oil immersed power transformers

Oil-immersed Power transformers are considered to be one of the most essential parts of the electrical system and partial discharges (PD) are one of the dangerous problems that face transformer insulation. PD is localized dielectric breakdown of an insulation degradation or defect in an oil power transformer. Ultra-high frequency (UHF) detection technique can be utilized in PD detection inside oil-filled power transformers, by capturing the electromagnetic waves generated by PD using an antenna. This research presents a design for a compact double layer printed UHF antenna to detect and discriminate various types of PD activity in a power transformer. PD activity in the transformer has been proposed in this paper by using four different artificial insulation defects, metal protrusion defect in air medium, metal protrusion defect in an oil medium, surface discharge defect, and oil-paper void defect. The presented design guarantees a small-size wideband antenna for detection. The designed antenna has small/simple size and ultra-wide frequency passband from 1.4 to 1.85 GHz, 2.2 to 2.65 GHz, and 3.2 to 3.4 GHz. The normalized power frequency spectra of the captured UHF-PD signals of the four defective models showed that every PD type has its signature, and this could help in the discrimination among different types on site.

Partial Discharge Detection in an Oil-Filled Power Transformer Using Fiber Bragg Grating Sensors: A Review

Power transformer is one of the most important high voltage apparatus in any power system network. The life span of such apparatus commonly relies upon its insulation condition. The primary reason for an insulation breakdown is partial discharge. Partial discharges (PDs) are electrical discharges that happen in the insulation system of the transformers. Occurrence of PDs results in deterioration of the transformer insulation system. Hence, measuring and detecting the PD at an initial stage is necessary to avoid complete breakdown of the transformer. Currently, various PD detection and measurement methods, such as, electrical, chemical, acoustic, UHF and optical methods are being used for PD monitoring in power transformers. The conventional PD monitoring techniques like electrical or chemical have some limitations. The optical Fiber Bragg Grating (FBG) Sensor has been used as a PD sensor for PD monitoring in the power transformer to overcome these issues. This paper presents a review of FBG sensors’ application as a sensor for PD monitoring in an oil-filled power transformer. The challenges and future prospects in this area are also discussed.

Experimental Results of Partial Discharge Localization in Bounded Domains.

This work presents a novel diagnostic method to localize Partial Discharges (PDs) inside Medium Voltage (MV) and High Voltage (HV) equipment. The method is well suited for that equipment presenting a bounded domain with fixed Boundary Conditions (BCs) such as Oil-Filled Power Transformers (OFPTs), Air Insulated Switchgears (AISs), Gas Insulated Switchgears (GISs) or Gas Insulated Transmission Lines (GILs). It is based on Electromagnetic (EM) measurements which are used to reconstruct the EM field produced by the PD and localize the PD itself. The reconstruction and localization tasks are based on the eigenfunctions series expansion method which intrinsically accounts for the physical information of the propagation phenomenon. This fact makes the proposed diagnostic method very robust and accurate even in real and complex scenarios. The promising experimental results, obtained in two different test cases, confirmed the ability and powerfulness of the proposed PD localization method.

A multiscale porous–resolved methodology for efficient simulation of heat and fluid transport in complex geometries, with application to electric power transformers

The numerical simulation of fluid flow through a complex geometry with heat transfer is of strong interest for many applications, such as oil-filled power transformers. A fundamental challenge here is that high resolution is necessary to resolve the fluid flow phenomena, but this makes simulation of the full geometry very expensive in terms of computational power. In this work, we develop a simulation methodology that combines a porous-medium approach for simulating some regions of the domain, coupled with fully resolved simulations in those regions which are deemed most interesting to study in detail. As one does not resolve flow features like thermal boundary layers in the regions modeled with the porous approach, the resolution in these parts can be orders of magnitude coarser. This multiscale approach is validated against the use of fully resolved simulations in the whole domain, as well as against analytical solutions to the extended Graetz problem. We then apply the approach to the study of oil flow and heat transfer in large electric power transformers and demonstrate a significant reduction in computational cost compared to a fully resolved approach.

FEM based 3D modelling of partial discharge detection and localization in an oil-filled power transformer using piezoelectric acoustic sensor

Abstract The main cause of insulation degradation is due to partial discharges (PDs) occurring inside the transformer, and its detection and localization are the most effective, non-destructive methods to assess the insulation condition of the transformer. Among the PD detection methods, the acoustic PD detection technique is popular because of its various advantages. The acoustic PD detection method for accurate PD source localization becomes quite challenging when PD occurs inside the transformer core and windings. As the acoustic sound wave can be distorted and vibration with its distribution, so the type of PD sensors with their setting in the transformer should be thoroughly investigated and chosen. In this work, via simulation, the acoustic sound distribution inside the power transformer due to PD occurs is studied. Based on the knowledge of acoustic pressure wave distribution, a Lead Zirconate Titanate (PZT-5H) sensor is designed using Finite element method based COMSOL Multiphysics software and placed it on the outer walls of the transformer for PD detection and localization. The PD induction position has been recognized from the sensor signal using an artificial neural network. The results of PD detection and localization by the proposed piezoelectric sensor and COMSOL probe point are in good agreement.

The economic feasibility of a multichannel monitoring of oil-filled power transformers of traction substations

The economic feasibility of a multichannel monitoring of oil-filled power transformers of traction substations

Modelling of Piezoelectric Sensor with Different Materials Approach for Partial Discharge Detection on Power Transformer: PZT-5H, ZnO and AlN

The acoustic emission (AE) technique is one of the unconventional methods of partial discharges (PDs) detection. It plays a most important role in oil-filled power transformers diagnostics because it enables the detection and online monitoring of PDs as well compared to the conventional method for PDs detection which are not suitable for on-site measurement due to electrical disturbance. In this paper, the acoustic based on piezoelectric sensor by different material is modelled in order to be able to obtain PDs signal occurred in power transformers. Modelling of a piezoelectric sensor with different material which is PZT-5H, ZnO, and AlN is approached in order to investigate the performance of resonant frequency, electric potential, and the performance in processing in order to match the range of AE detection. Piezoelectric materials have become very useful in processing devices because of their electrical-mechanical mutuality. Study was performed on frequency target of PDs should be higher and in the range of 10 kHz -300 kHz in order to prevent the power transformer from failure or breakdown and it has been found out by proven from analytical and simulation result by using the Finite Element Method (FEM). Based on this information, acoustic sensor is analyses with different types of cantilever beam and piezoelectric material and different length dimension of the beam in order to analyses the performance between them. Based on the result, the piezoelectric material that be chosen in this project is ZnO due to its high piezoelectric coupling and environmental friendly is used in order to support green technology compared to others material discussed which is harmful even though produced high performance. This detection method gave some improvement in monitoring system PD activities in the transformer’s tank.

A Machine Learning approach to fault detection in transformers by using vibration data

Transformer Vibration Technique is considered an effective method to monitor structural elements of transformers, in particular, to detect loose or deformed windings. As it is well known, vibrations vary with the sensor location on the transformer tank, which makes the number and the placement of sensors critical aspects for fault detection. In this paper, we investigate this issue by analyzing vibration spectra collected from various sensors installed on the tank of a typical oil filled power transformer operating under two limit cases, namely absence or presence of clamping looseness on windings. Support Vector Machines (SVM) are employed and an extensive analysis is performed to understand the informativeness of data corresponding to various sensors so as to figure out the appropriate number of sensors and their best location. This way fault detection is eventually achieved with a reduced and optimized number of sensors, resulting in a significant saving of time and costs.

Thermal modelling of oil-filled power transformer TM - 160/10

The results of modeling the thermal characteristics of the dry and oil-filled power transformer TM-160/10 in idle and short circuit modes are presented. The electrical, geometric and thermal characteristics of the TM-160/10 transformer are determined. Computer modeling is performed in the software package ANSYS 17.1. The 2D distributions of temperature and density of heat flows in the transformer in the longitudinal and transverse sections are determined. It is shown that the use of transformer oil for cooling the transformer significantly reduces the temperatures in the active part. The temperature distribution occupies the range of 67-91 °С. Accordingly, the temperature of the most heated part is 91 °C and also corresponds to the low voltage winding. The dependence of the most heated point of the transformer on the operating mode was studied. A formula is proposed for calculating the maximum temperature of a transformer as a function of power loss.

Application of neural networks to predict the moisture content in oil-filled power transformers insulation

Application of neural networks to predict the moisture content in oil-filled power transformers insulation

A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

Condition Assessment Techniques for Insulation Diagnosis of Oil Filled Power Transformers

Condition Assessment Techniques for Insulation Diagnosis of Oil Filled Power Transformers

Comparative Analysis of the Results of Diagnostic Measurements with an Internal Inspection of Oil-Filled Power Transformers

This article presents a description of four independent case studies concerning situations when power transformers were directed to internal inspection. This inspection was the result of a specific case of a routine diagnostic procedure that was carried out and, where the transformer was switched off by a Buchholz gas relay. The case studies described were selected such that they represented situations when availability of historical data on the previous measurements was limited and a quick diagnosis had to be made on the basis of the results from the last measurement. In all of the cases presented here, the analysis of the gases dissolved in oil had played an important role in the detection of the defects that turned out to be dangerous for further exploitation of the transformers considered. The first signal about a possible developing defect was elicited solely from the measurements of the oil samples taken from the transformer in service. However, more detailed recognition and initial localization of the defect was possible after additional supplementary measurements (winding resistance, sweep frequency response analysis, etc.), which required the transformer to be switched off. The conducted sequence of actions, based on the developed diagnostic procedure, indicated the possibility of effective and early withdrawal of the transformer from operation, before it underwent a serious failure.

Development of Acoustic Emission Sensor Optimized for Partial Discharge Monitoring in Power Transformers.

The acoustic emission (AE) technique is one of the unconventional methods of partial discharges (PD) detection. It plays a particularly important role in oil-filled power transformers diagnostics because it enables the detection and online monitoring of PDs as well as localization of their sources. The performance of this technique highly depends on measurement system configuration but mostly on the type of applied AE sensor. The paper presents, in detail, the design and manufacturing stages of an ultrasensitive AE sensor optimized for partial discharge detection in power transformers. The design assumptions were formulated based on extensive laboratory research, which allowed for the identification of dominant acoustic frequencies emitted by partial discharges in oil–paper insulation. The Krimholtz–Leedom–Matthaei (KLM) model was used to iteratively find optimal material and geometric properties of the main structures of the prototype AE sensor. It has two sensing elements with opposite polarization direction and different heights. The fully differential design allowed to obtain the desired properties of the transducer, i.e., a two-resonant (68 kHz and 90 kHz) and wide (30–100 kHz) frequency response curve, high peak sensitivity (−61.1 dB ref. V/µbar), and low noise. The laboratory tests confirmed that the prototype transducer is characterized by ultrahigh sensitivity of partial discharge detection. Compared to commonly used commercial AE sensors, the average amplitude of PD pulses registered with the prototype sensor was a minimum of 5.2 dB higher, and a maximum of 19.8 dB higher.