Transformer Research Articles

A Method for Identifying External Short-Circuit Faults in Power Transformers Based on Support Vector Machines

Being a vital component of electrical power systems, transformers significantly influence the system stability and reliability of power supplies. Damage to transformers may lead to significant economic losses. The efficient identification of transformer faults holds paramount importance for the stability and security of power grids. The existing methods for identifying transformer faults include oil chromatography analysis, temperature assessment, frequency response analysis, vibration characteristic examination, and leakage magnetic field analysis. These methods suffer from limitations such as limited sensitivity, complexity in operation, and a high demand for specialized skills. In this paper, we propose a method to identify external short-circuit faults of power transformers based on fault recording data on short-circuit currents. It involves analyzing the current signals of various windings during faults, extracting appropriate features, and utilizing a classification algorithm based on a support vector machine (SVM) to determine fault types and locations. The influence of different kernel functions on the classification accuracy of SVM is discussed. The results indicate that this method can proficiently identify the type and location of external short-circuit faults in transformers, achieving an accuracy rate of 98.3%.

The component outage model for power systems using availability approximations

The traditional outage models with constant failure rates do not truly reflect the environmental and operating conditions nor the effect of repair operations on power systems. This paper proposes two useful approximations for the availability function based only on the first three of the lifetime distribution. We compare the availability function of a power transformer using the numerical inverse of the transformed availability function. It also suggests methods to verify the effectiveness of the approaches. Finally, it outlines the proposed approximations to validate the efficacy of the approximations.

A comprehensive framework for identifying safety-related and ageing sensitive sscs in low power nuclear research reactor

This work aims to establish a novel approach for the selection of structures, systems, and components (SSCs) important to safety and vulnerable to ageing degradation for low-power research reactors. For this purpose, the framework developed combines the Preliminary Risk Analysis (PRA) technique to categorize SSCs into four different safety classes, and Ageing Failure Mode and Effect Analysis (AFMEA) technique to find out ageing failure modes related to a specific SSC and evaluate them according to the Risk Priory Number (RPN). The intersection of the PRA and AFMEA outcomes then yields a list of SSCs that are both important to safety and strongly ageing sensitive, making it possible to optimize the selection process of SSCs to be prioritized in further preventive maintenance, periodic testing, and inspection activities. The present paper also conducts a case study to apply the proposed methodology to the Brazilian IPR-R1 TRIGA nuclear research reactor. The selected SSCs were then both AL and SS fuel rods from the reactor core, stainless steel pipe, thermocouples, pressure indicators, and water level indicators from the primary circuit, and power monitoring channels, radiation monitors, and cooling water temperature channels from the controlling system. Moreover, the approach was also able to identify SSCs that are important from a safety perspective but are not ageing sensitive (reactor tank and control rods) and components that are not important to safety but are strongly ageing sensitive (pipe filters). These findings may further serve as input for the comprehensive safety assessment conducted during the Periodic Safety Review of the facility.

Analyzing the Seasonal Vertical Displacement Fluctuations Using the Global Navigation Satellite System and Hydrological Load: A Case Study of the Western Yunnan Region

The non-tectonic deformation caused by hydrological loads is an important influencing factor in GNSS vertical displacement. Limited by the temporal and spatial resolution of global models and model errors, the hydrological load results calculated by traditional methods are difficult to meet the high temporal and spatial resolution requirements of small to medium-scale regions. This paper introduces the idea of the remove–restore method, assimilates regional high-resolution hydrological data, and obtains higher temporal and spatial-resolution hydrological load results. Subsequently, utilizing data from 12 CORS observed in the western Yunnan region between January 2018 and December 2020, the quantitative relationship and variation characteristics between GNSS vertical displacement and hydrological load displacement were analyzed in detail. Furthermore, the annual signals of both were extracted using the SSA method for comparative analysis. After removing the effects of atmospheric load and non-tidal ocean load, the average correlation coefficient between GNSS vertical displacement and hydrological load displacement is 0.84, with an average reduction of WRMS (%) reaching 37.17%. The average correlation coefficient of the annual signals between GNSS vertical displacement and hydrological load deformation is 0.94, with an average reduction of WRMS (%) reaching 46.5%, indicating that the contribution of hydrological load to the GNSS non-tectonic vertical displacement annual signal is close to 50%. The research results provide scientific support and important references for studying surface tectonic deformation by removing non-tectonic deformations such as hydrological loads from GNSS vertical displacement. Additionally, it helps to explore the mechanisms of interaction between water storage migration and surface deformation.

The whole-life ratcheting behavior of internal pressurized elbow subjected to force and displacement cycling with a damage-coupled kinematic hardening model

In this paper, a study was conducted on the ratcheting-fatigue tests of internal pressurized elbow in the primary circuit of nuclear power plant under force and displacement cycling, and the ratcheting effects at the critical locations of the elbow were evaluated. Based on thermodynamics and continuous damage mechanics theory, the damage evolution rule of the material was derived according to the derivative relationship between damage dissipation potential and damage related variable. A cyclic plastic constitutive model with damage was established by combining the damage evolution rule with Chen-Jiao-Kim (CJK) kinematic hardening rule. The subroutine of this constitutive model was embedded in ANSYS finite element software to numerically evaluate the evolution law of the whole-life ratcheting of the elbow. The results indicate that this damage-coupled kinematic hardening model can describe the whole-life ratcheting behavior of the elbow, and the numerical calculation results and experimental data are basically identical.

Diagnosis of Radial Deformation and Axial Displacement Faults in Power Transformer Windings using X-ray and Compton Backscatter Imaging

Continuous and uninterrupted transmission and distribution of electrical energy is highly dependent on the flawless operation of power transformers. Failure of these critical components can lead to network power interruptions and consequently have severe detrimental consequences. Therefore, timely detection and diagnosis of power transformer faults is crucial not only to prevent the escalation of faults but also to minimize repair and maintenance costs. Conventional methods for inspecting windings faults have limitations such as the need to take the transformer out of service, unreliable results, and the addition of auxiliary devices inside the transformer, such as dielectric windows. In contrast, the proposed method in this paper utilizes a Compton Backscatter Imaging (CBI) or X-Ray Transmission (XRT) device located outside the transformer, eliminating the need for any additional equipment. This enables online scanning of any number of transformers to assess their health status and identify the type, extent, and progression of potential internal faults. Experimental results demonstrate the feasibility of the proposed method for detecting radial deformation and axial displacement and disk space variations faults of varying magnitudes and locations in both high-voltage and low-voltage windings of power transformers.

Kinetic Assessment of Kraft and Thermally Upgraded Kraft Papers Aged in Five Alternative Dielectric Fluids.

The lifespan of an electrical transformer, primarily determined by the condition of its solid insulation, is well known under various operating conditions when mineral oil is the coolant in these machines. However, there is a trend toward replacing this oil with biodegradable fluids, especially esters; therefore, an understanding of the ageing of solid insulation with these fluids is essential. Currently available data do not allow for the selection of the best ester among those available on the market, as each study applies different conditions, making it impossible to compare results. Thus, this paper analyses the degradation of Kraft and Thermally Upgraded Kraft papers with the following five most promising commercial esters: sunflower, rapeseed, soybean, palm, and synthetic. The materials underwent accelerated thermal ageing at 130, 150, and 170 °C, and the integrity of the papers was evaluated through their polymerisation degree and the obtaining of the degradation kinetic models. The wide range of materials studied in this work, which were subjected to the same treatments, allows for a comparison of the esters, revealing significant differences in the impact of the alternative fluids. Sunflower, rapeseed, and soybean esters provided the best paper protection, i.e., the degree of polymerisation of Kraft paper in the tests at 150 °C decreased by 71% with these fluids, compared to the 83% reduction with mineral oil, 79% reduction with palm ester, and 75% reduction with synthetic ester. Furthermore, different kinetic models were obtained to predict the degradation; it was concluded that the Emsley model provides the best fit. Additionally, it was found that the behaviour of a dielectric fluid with one type of paper cannot be extrapolated, which is only noticeable in broad-scope studies.

A comparative study of J–A and Preisach improved models for core loss calculation under DC bias

Power transformer is widely used in the power system with the rapid development of the power converters connected to the grid. When a transformer operates under DC bias conditions, its iron core loss increases significantly, causing local overheating and threatening the proper operation of the transformer. However, there are persistent difficulties in accurately assessing the core loss when the induction waveform is influenced by a DC bias. This paper first proposes improvements to the J–A and Preisach models to evaluate the core loss of the iron core under DC bias. Additionally, we incorporate the hysteresis models into the finite element method (FEM) by modifying the fundamental constitutive equations in the FEM model in order to perform a precise core loss/distribution calculation. To verify the accuracy of prediction, a transformer prototype with a laminated core is developed. The improved J–A-FEM and Preisach-FEM models were directly compared in terms of calculation accuracy, numerical implementation, and computational burden.

Health Assessment and Lifespan Estimation for Power Transformer using Machine Learning

Health Assessment and Lifespan Estimation for Power Transformer using Machine Learning

Analysis of existing approaches to monitoring and diagnostics of the active part of power transformers

Subject of research: monitoring and diagnostics of the condition of the active part of power transformers. Purpose of research: identification of promising trends in the development of methods for monitoring and diagnosing the state of the active part of power transformers. Object of research: power transformers. Methods of research: critical system analysis. Main results of research: the problem of monitoring and diagnosing the condition of the active part of power transformers, including windings, is considered. It is noted that a significant number (more than four million units) of distribution transformers are in operation in the Russian Federation. The causes of emergency failures of the specified class of power transformers are analyzed. One of the main causes of failures is highlighted – a winding circuit in the transformer winding. Attention is focused on the problem of monitoring the state of the active part of the transformer and on the diagnosis of the development of short circuits in the transformer winding. The presence of several groups of methods for monitoring and diagnosing winding circuits of transformer windings is shown. The economic attractiveness of monitoring and diagnostics based on the reaction to artificial electromagnetic influences, and monitoring and diagnostics based on the reaction to natural electromagnetic influences is shown.

Neural networks and particle swarm for transformer oil diagnosis by dissolved gas analysis

The lifetime of power transformers is closely related to the insulating oil performance. This latter can degrade according to overheating, electric arcs, low or high energy discharges, etc. Such degradation can lead to transformer failures or breakdowns. Early detection of these problems is one of the most important steps to avoid such failures. More efficient diagnostic systems, such as artificial intelligence techniques, are recommended to overcome the limitations of the classical methods. This work deals with diagnosing the power transformer insulating oil by analysis of dissolved gases using new techniques. For this, we have proposed intelligent techniques based on Multilayer artificial neural networks (ANN). Thus, a multi-layer ANN-based model for fault detection is presented. To improve its classification rate, this one was optimized by a meta-heuristic technique as the particle swarm optimization (PSO) technique. Optimized ANNs have never been used in transformer insulating oil diagnostics so far. The robustness and effectiveness of the proposed model is demonstrated, and high accuracy is obtained.

Internet of Things (IoT) System for Diagnosing Power Transformer Faults using the IEC 60076-1 Standard: Review

The evaluation of Internet of Things (IoT), which are contracted to research the adoption or acceptance of IoT technology, is deemed to be a subject of vital importance. Studying the adoption or admissibility of IoT technology is not a brand-new study area, and numerous scholars have undertaken the task. The current and upcoming generations of Internet are being shaped by the Internet of Things (IoT). The main aim of this survey, contrasted with previous surveys, is to systematically evaluate the IoT technology research in condition monitoring of power transformers to proffer a thorough cursor that can facilitate scholars to accomplish additional research in IoT acceptance. Almost all appliances may now be remotely monitored and connected to the internet thanks to the Internet of Things. Power transformers play a crucial role in electrical distribution networks, and their reliable operation is paramount. The existing diagnostic tests outlined in the IEC 60076-1 standard are essential for ensuring transformer health but suffer from manual procedures, leaving them susceptible to human error. This traditional method not only introduces the potential for inaccuracies due to human involvement but also incurs significant operational costs and delays. The need to physically dispatch technicians to transformer sites for routine diagnostics hampers timely fault detection and preventive maintenance. Therefore, there exists a pressing need to modernize and enhance the diagnostic procedures by integrating emerging technologies like the Internet of Things (IoT). This paper will review the most recent techniques for transformer health tracking system using IoT and highlight the contemporary approaches. A total of 50 primary research literature that was published between 2018 and 2024 were surveyed in this review paper.

First-Principles Predictions of MoS2-WS2 In-Plane Heterostructures for Sensing Dissolved Gas Species in Oil-Immersed Transformers.

This work from first-principles insight uses a MoS2-WS2 in-plane heterostructure as a potential sensing material for detection of CO and C2H2, two typical dissolved gases in oil-immersed transformers, in order to evaluate the operation status. The adsorption performance of the MoS2-WS2 heterostructure upon two gas species is assessed via three adsorption sites and compared with isolated MoS2 and WS2. Results indicate that MoS2-WS2 performs with a much stronger binding force and charge-transfer for adsorptions of CO and C2H2 in comparison to the isolated counterpart, which gives rise to more obvious deformation in the electronic property of MoS2-WS2 as well as a much larger resistance-based sensing response. The recovery time of MoS2-WS2 for desorption of CO and C2H2 molecules is also appropriate to allow the reusability of such a sensor. The findings in this work uncover the admirable sensing potential of transition metal dichalcogenides (TMDs)-based heterostructures upon oil dissolved gases, which opens up a new way to explore novel 2D nanomaterials as resistive gas sensors for dissolved gas analysis in electrical oil-immersed transformers.

Retraction Note: Determination of Power Transformer Fault’s Severity Based on Fuzzy Logic Model with GR, Level and DGA Interpretation

Retraction Note: Determination of Power Transformer Fault’s Severity Based on Fuzzy Logic Model with GR, Level and DGA Interpretation

Traditional fault diagnosis methods for mineral oil‐immersed power transformer based on dissolved gas analysis: Past, present and future

AbstractA key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent‐based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA‐based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA‐based methods for mineral oil‐immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA‐based diagnostic methods are also presented.

Temperature-dependent local structure and lattice dynamics of 1T-TiSe[formula omitted] and 1T-VSe[formula omitted] probed by X-ray absorption spectroscopy

The local atomic structure and lattice dynamics of two isostructural layered transition metal dichalcogenides (TMDs), 1T-TiSe2 and 1T-VSe2, were studied using temperature-dependent X-ray absorption spectroscopy at the Ti, V, and Se K-edges. Analysis of the extended X-ray absorption fine structure (EXAFS) spectra, employing reverse Monte Carlo (RMC) simulations, enabled tracking of the temperature evolution of the local environment in the range of 10–300 K. The atomic coordinates derived from the final atomic configurations obtained using the RMC method were used to calculate the partial radial distribution functions (RDFs) and the mean-square relative displacement (MSRD) factors for the first ten coordination shells around the absorbing atoms. Characteristic Einstein frequencies and effective force constants were determined for TiSe, TiTi, VSe, VV, and SeSe atom pairs from the temperature dependencies of MSRDs. The obtained results reveal differences in the temperature evolution of lattice dynamics and the strengths of intralayer and interlayer interactions in TiSe2 and VSe2.

Derivation of transformer winding equivalent circuit by employing the transfer function obtained from frequency response analysis data

AbstractPrecisely interpreting frequency response analysis (FRA) curves is crucial when investigating mechanical malfunctions in power transformer windings (TWs). However, many conventional explanations of FRA features cannot be validated by an equivalent circuit (EC) that represents winding structures using resistance, inductance, and capacitance components in a ladder network. Incorrect interpretation can lead to misdiagnosis and confusion in asset management. Previous ECs are often proposed by analysing winding structures without rigorous verification compared to the corresponding FRA data. The specific EC is acquired using the transfer function (TF) derived from the measured FRA data. This allows for the establishment of the relationship between TW FRA curves, TF equations, and EC topologies. The precise interpretation of FRA curves can be achieved by closely observing the FRA curves created from TFs and ECs. The remarkable similarity between the measured and modelled FRA curves verifies the authenticity of the derived ECs. This significant achievement clarifies many misunderstandings regarding FRA and represents a substantial advancement in FRA technology.

Fault Diagnosis of Power Transformer in One-Key Sequential Control System of Intelligent Substation Based on a Transformer Neural Network Model

Fault Diagnosis of Power Transformer in One-Key Sequential Control System of Intelligent Substation Based on a Transformer Neural Network Model

An improved electric equipment fault prediction based on recursive multi-parameter prediction algorithm

This paper proposes a method of beforehand prediction of electric equipment faults based on chain-linked recurrent neural network algorithm, which takes the operating parameters of power equipment and other relevant environmental factors as inputs, and takes the fault characteristics as output judgment marks, and constructs a machine learning training model to realize the prediction of power equipment faults. The neural network algorithm adopted in this paper adopts a tree structure. Each sub-node can transfer information with its multiple superior nodes, so that the correlation between the data of the front and back nodes can be obtained, which meets the needs of the equipment fault prediction model. Considering that the occurrence of power transformer faults is sudden and greatly affected by changes in the surrounding environment, the input of prediction algorithms should consider more environmental factors. This method takes the historical data of various parameters including meteorological phenomena, geography data, and temperature of adjacent equipment and facilities as the training sample set, improves the learning model, gives the trend curve of each index, and gives a prompt at its threshold to ensure the prediction accuracy and give the index prediction.

Insulation system diagnosis in power transformers using DGA analysis and Megger DC

Dissolved gas chromatography (DGA) analysis and Megger DC insulation tests are performed to diagnose the condition of a power transformer. In this context, the objective of this study is to assess whether there is a relationship between both types of tests. To this effect, a database with DGA and Megger DC test protocols is analyzed, using the theory of variable correlation as well as current DGA techniques. The results allow stating that there is indeed a relationship between both tests under specific conditions. Therefore, the DC isolation status of a transformer can be estimated via DGA tests.