Transformer Research Articles

Revisiting the dry friction-like magnetic vector hysteresis model

Physically correct simulations of magnetic devices require precise and energy-consistent hysteresis models. Electrical steel sheets of, e.g., power transformers or rotating machines represent one crucial element in such a simulation since the hysteresis is revealed uni- and multi-axial, and the steel sheets may have anisotropy. A dedicated vector hysteresis model based on dry friction-like pinning is revisited and derived in terms of thermodynamic state equations in a theoretical manner. The model is then linked to an incremental energy minimization procedure used in elasto-plasticity theory. A numerically efficient two-dimensional solution scheme of the hysteresis model is extended to the three-dimensional case. Rotational losses are also in the scope of this paper since the model cannot describe this loss type by nature. Therefore, numerical adaptations are discussed to account for vanishing rotational losses in saturation.

An Intelligent Power Transformers Diagnostic System Based on Hierarchical Radial Basis Functions Improved by Linde Buzo Gray and Single-Layer Perceptron Algorithms

An Intelligent Power Transformers Diagnostic System Based on Hierarchical Radial Basis Functions Improved by Linde Buzo Gray and Single-Layer Perceptron Algorithms

Analysis of interturn short circuit in regulating winding of power transformer based on field-circuit coupling

The interturn short circuit fault is one of the common faults in power transformers. At present, research on interturn short circuit faults focuses on high, medium, and low voltage windings, while there is relatively little research on interturn short circuit in regulating windings. Specifically, there is a lack of reported studies on the transient electromagnetic processes, magnetic field distribution, and electromagnetic force characteristics of interturn short circuits in regulating windings unconnected to the circuit. This study presents an actual fault scenario involving interturn short circuits occurring in the untapped portion of the regulating windings of a specific power transformer. A field-circuit coupled model was established to analyze the transient electromagnetic processes during the fault, and the model’s effectiveness was validated by comparing its results with actual fault recording data. Additionally, the magnetic field distribution and electromagnetic force characteristics during the fault were analyzed, and discussions were carried out regarding various ratios of short-circuit turns in the regulating windings. The results indicate that even when an interturn short circuit occurs in the portion of the regulating winding that is not connected to the circuit, the current in the short-circuited turns can reach several tens of times the rated value. Additionally, the leakage magnetic field and the electromagnetic force experienced by the short-circuited ring also increase significantly. The short-circuit ratio has a significant impact on the current of the short-circuited ring, leakage magnetic field intensity, and electromagnetic force. This study contributes to a better understanding of the impact of interturn short-circuit faults in the untapped portion of the regulating windings, offering crucial technical support for fault diagnosis and prevention of power transformers.

Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model

Sensor faults in nuclear power plants (NPPs) have the potential to propagate negative impacts on system stability, leading to false alarms and accident misdiagnosis. Existing methods seldom concurrently consider complex spatial–temporal correlations among multi-type sensors in the primary circuit. This study presents a novel sensor fault detection and isolation scheme named the knowledge-guided spatial–temporal model (KGSTM), using the knowledge-guided recurrent unit (KGRU) and the concurrent detection strategy. To organically express part and whole interdependencies from inherent sensor layout, several graphs are specifically designed with pertinent domain knowledge. KGRU consists of the multi-graph convolutional network (MGCN) for fusing various spatial information and the gate recurrent unit (GRU) for extracting dynamic temporal features, further obtaining precise reconstructed signals and residuals. The concurrent detection strategy can explicitly quantify abnormal behaviors to detect and isolate faulty sensors by characterizing spatial–temporal signal variation. Numerical results on two real-world datasets from a pressurized water reactor (PWR) with simulated faults illustrate that the KGSTM has superior performance over various state-of-the-art methods in terms of signal reconstruction and fault detection.

Темпоральные конструкции с наименованием праздников в произведениях И.А. Бунина

<p>Based on the material of the works of I.A. Bunin, the article analyzes temporal constructions, which include the names of holidays. The purpose of the study is to identify the structural and semantic features of the designated temporal units and their functioning in Bunin texts. The paper uses the method of continuous sampling of linguistic material, which helps to obtain the most objective research results. The methods of observation, generalization and interpretation are applied. As a result of the conducted research, a quantitative ratio of relative temporal syntaxes and relative temporal substantive turns with lexemes-names of holidays was revealed. Statistical data are presented in the form of diagrams. It is proved that in the analyzed temporal constructions, primitive prepositions are more often used, representing the antecedence or coincidence with the designated time. The peculiarities of the use of temporal constructions with names of holidays are reflected in I.A. Bunin's idiosyncrasy.</p>

Solid–liquid–gas-like phase transition in electric field driven dense granular media

The polarization induced by an external electric field significantly influences the contact charge of particles. It can easily cause solid–liquid–gas-like phase transitions in dense granular media, which are the key factors leading to environmental and safety disasters such as sandstorms, volcanic eruptions, and spacecraft emergencies. In this letter, our investigation focused on a densely packed granular system with different thicknesses subjected to bottom excitation and an external electric field. Using the discrete element method (DEM), we proposed conditions for the first- and second-order phase transitions and revealed the mechanism of electrical signal transformation. We presented the physical characteristics of charge and polarization diffusion coefficients for charged particles, and introduced a theoretical Turing model to predict critical phase transitions. Additionally, this phase transition model was extended to the application of electrostatic dust removal, quantitatively predicting the relationship between dust removal efficiency, particle layer thickness, and electric field intensity.

Methodology for the optimal replacement of power transformers based on their health index

Transformer life is critical to ensuring the reliability and stability of the power system. However, making decisions about the optimal time to replace a transformer is challenging due to the complexity of the interactions between equipment aging and applied maintenance. This study employs a risk analysis method based on the health status of the equipment and the consequences of its failure to develop a mathematical optimization methodology aimed at identifying the optimal timing for transformer replacement or monitoring activities, thus facilitating effective preventive maintenance strategies. The methodology was developed in AMPL with the objective of minimizing the risk index of the transformer fleet, taking as a constraint a given annual budget. It was applied to a study case covering a period of six years for a fleet of 39 110 kV transformers using real data. The results presented show that the developed methodology provides valuable information to the power system operator who is thus able to manage the transformer fleet and ensure a safe and financially efficient operation of the grid, providing a list of the 10 most risky units and suggesting a replacement and improvement scheme for them.

Hints to lightning breakdown enhancement for synthetic ester insulating oil: Unraveling the impact of molecular chain length on electron structural parameters under electric field stress

The high flash point and biodegradability of synthetic ester insulating oil offer transformers both operational stability and environmental protection. However, its poor lightning breakdown characteristics under long oil gaps and inhomogeneous electric field often lead to rapid streamer discharges, posing challenges and limits to the use of synthetic ester insulating oil and hinders the development of low-carbon, eco-friendly high voltage power transformers. To address this issue, we propose improving the lightning breakdown characteristics through molecular chain length design. Using density functional theory, time-dependent density functional theory, and wave function analysis, we investigate how microscopic factors like molecular polarity, discharge active sites, frontier orbitals, traps, ionization and electron affinity, and excitation processes influence macroscopic discharge characteristics. Our findings show that under an electric field, chain length is positively correlated with electron transfer, enhancing charge polarization. Chain length also correlates positively with dipole moment, facilitating the formation of space charge centers. While chain length minimally affects discharge active sites, the electric field can intensify their formation. Additionally, a longer chain length decreases the energy gap, increasing electron trap levels more than hole trap levels, thus capturing charges longer. Chain length is negatively correlated with ionization energy, promoting electron impact ionization, and has little effect on the excitation energy of S(0) → S(1), making excitation under high electric fields unlikely.

Economic and Exergy Analysis of TiO2 + SiO2 Ethylene-Glycol-Based Hybrid Nanofluid in Plate Heat Exchange System of Solar Installation

This paper concerns an economic and exergetic efficiency analysis of a plate heat exchanger placed in a solar installation with TiO2:SiO2/DI:EG nanofluid. This device separates the primary circuit—with the solar fluid—and the secondary circuit—in which domestic hot water flows (DHW). The solar fluid is TiO2:SiO2 nanofluid with a concentration in the range of 0.5–1.5%vol. and T = 60 °C. Its flow is maintained at a constant level of 3 dm3/min. The heat-receiving medium is domestic water with an initial temperature of 30 °C. This work records a DHW flow of V˙DHW,in = 3–6(12) dm3/min. In order to calculate the exergy efficiency of the system, first, the total exergy destruction, the entropy generation number Ns, and the Bejan number Be are determined. Only for a comparable solar fluid flow, DHW V˙nf=V˙DHW 3 dm3/min, and concentrations of 0 and 0.5%vol. is there no significant improvement in the exergy efficiency. In other cases, the presence of nanoparticles significantly improves the heat transfer. The TiO2:SiO2/DI:EG nanofluid is even a 13 to 26% more effective working fluid than the traditional solar fluid; at Re = 329, the exergy efficiency is ηexergy = 37.29%, with a nanoparticle concentration of 0% and ηexergy(1.5%vol.) = 50.56%; with Re = 430, ηexergy(0%) = 57.03% and ηexergy(1.5%) = 65.9%.

Adaptive convergence enhancement strategies for Newton–Raphson power flow solutions in distribution networks

AbstractThis study introduces a series of strategies to enhance convergence in the Newton–Raphson power flow method for unbalanced distribution networks. The proposed approach incorporates graph theory, Kron reduction, and current injection techniques. The network components, including transmission conductors, power transformers, power conductors, shunt capacitors/reactors, and demand loads, were merged into the bus impedance matrix. Using two identification processes (based on mismatches for bus voltages and bus power injections) and optimal multipliers (μ), the proposed approach estimates the initial bus voltages by approximating the converged solutions. To verify the effectiveness of the proposed approach, the authors performed a comparative analysis of five IEEE test feeders in various scenarios. The results confirmed the effectiveness of the proposed approach, particularly for unbalanced distribution systems with diverse transformer connections.

Health status perception of oil-immersed power transformers considering wind power uncertainty

The power transformer is the most important and critical component in the power grid of the electrical system. Its safe and stable operation is of great significance for the reliable transmission of renewable energy generation and the reliable power supply to end users. With the continuous development of new types of power systems, the load of the power system undergoes drastic changes, resulting in increased volatility and instability, which leads to issues such as overload, harmonics, and short circuits in transformers. Therefore, in order to accurately assess the operating status of transformers and promptly identify any existing conditions, a method is proposed in this paper. This method uses an optimal cloud entropy parameter calculation method and a variable weighting method to optimize the gray-cloud evidence model. Through a novel multi-source information fusion method, the results of various test items are fused to obtain the state awareness results. Meanwhile, different evaluation indicators are selected for different levels of renewable energy penetration and compared with other methods. The results are validated through examples, demonstrating that the method proposed in this paper can accurately reflect the operating status of transformers and has good scalability.

Evaluasi Ketidakseimbangan Beban Pada Penyulang Unicora

Load imbalance in electrical distribution transformers can lead to significant power losses and decreased efficiency in electrical distribution systems. This study focuses on the load imbalance problem at the Unicora feeder of PT PLN (Persero) ULP Singosari, which hinders optimal operational efficiency. The purpose of this research is to evaluate the extent of load imbalance, its impact on power losses, and the effectiveness of load balancing interventions in mitigating these losses. The methodology includes phase load measurements from February to September 2021, analysis of load imbalance, and power loss simulations using ETAP 12.6.0 software. The study's important findings indicate that the average load imbalance decreased from 34% to 21% after implementing load balancing interventions. Concurrently, power losses in the neutral conductor were reduced from 106.3 kW to 78.0 kW. ETAP simulations revealed a total power loss of 773.5 kW in the Unicora feeder, underscoring the significant impact of load imbalance on system efficiency. The implications of this research are crucial for operational practices at PT PLN (Persero) ULP Singosari. The findings demonstrate that effective load balancing can significantly enhance distribution efficiency, reduce operational costs, and improve the reliability of electricity provision. This study advocates for continuous monitoring and adaptive management of load distribution in electrical networks to maintain optimal performance and sustainability.

Distributed Vibration Monitoring System for 10 kV-400 kVA 3D Wound Core Transformer under Progressive Short-Circuit Impulses.

As large-scale, high-proportion, and efficient distribution transformers surge into the grids, anti-short circuit capability testing of transformer windings in efficient distribution seems necessary and prominent. To deeply explore the influence of progressively short-circuit shock impulses on the core winding deformation of efficient power transformers, a finite element theoretical model was built by referring to a three-phase three-winding 3D wound core transformer with a model of S20-MRL-400/10-NX2. The distributions of internal equivalent force and total deformation of the 3D wound core transformer along different paths under progressively short-circuit shock impulses varying from 60% to 120% were investigated. Results show that the equivalent stress and total deformation change rate reach their maximum as the short-circuit current increases from 60% to 80%, and the maximum and average variation rate for the equivalent stress reach 177.75% and 177.43%, while the maximum and average variation rate for the total deformation corresponds to 178.30% and 177.45%, respectively. Meanwhile, the maximum equivalent stress and maximum total deformation reach 29.81 MPa and 38.70 μm, respectively, as the applied short-circuit current increased to 120%. In light of the above observations, the optimization and deployment of wireless sensor nodes was suggested. Therefore, a distributed monitoring system was developed for acquiring the vibration status of the windings in a 3D wound core transformer, which is a beneficial supplement to the traditional short-circuit reactance detection methods for an efficient grid access spot-check of distribution transformers.

Evaluation of Thermal Properties of Various Insulating Liquids Used in Power Transformers

Evaluation of Thermal Properties of Various Insulating Liquids Used in Power Transformers

Corrosion phenomena and deposits in ITER Neutral Beam Test Facility primary cooling circuits

The ITER Neutral Beam Test Facility (NBTF) is hosted in Padua and includes two experiments: MITICA, the 1 MeV full-scale prototype of the ITER HNB injector, and SPIDER, the 100 keV full-size ITER Radio Frequency (RF) negative ion source. SPIDER and MITICA experiments are actively cooled by Ultrapure Water (UPW) to electrically insulate in-vessel components that are biased to high voltage levels. Water conductivity is an important monitored parameter to ensure components' insulation by limiting the leakage current of active cooled equipment. A very low conductivity water is especially important in MITICA, where components need to operate up to 1 MVdc, an insulation level beyond the actual industrial standard. Careful selection of suitable materials for any in-vessel (vacuum insulation) and out-vessel component (air insulation) is of utmost importance, as their interaction with water and different environment may affect their chemical and mechanical properties. Additionally, materials selection can severely influence water chemical characteristics: cooling circuits are made of metals (copper, aluminium, steel) and insulating materials (plastic, rubber) when connecting parts at different electric potentials.During the first years of cooling plant exploitation, it was shown that water degrades more quickly than estimated by design. The Primary Circuits (PCs) that showed the most severe water degradation during operation are SPIDER and MITICA power supply ones, respectively called PC01 and PC08. This paper describes the results of specific experimental tests performed on MITICA PC08 to evaluate possible causes of water degradation and detect sources of contaminants that might compromise future experimental campaigns. The cooling circuit was subdivided in different sections and water circulation tests were performed at constant temperature and flowrate. Water samples were collected and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) analyses were performed to quantify the type and amount of metals released. Moreover, components samples collected along their cooling circuits were characterized by Scanning Electron Microscope (SEM) technique to detect undesired contaminants and immersed in UPW to study their corrosion behaviour using metal release tests.

INFLUENCES OF NANOPARTICLES ON DIELECTRIC FLUID TRANSFORMERS: A REVIEW

Applications of nanoscience & nanotechnology have produced advanced materials & demonstrated impressive development in a number of industrial domains. Many technological advancements & research & development initiatives involving nanotechnology concepts are underway these days in an effort to ace the performance & dependability of traditional materials. The electrical sector has been benefited from these nanotechnology initiatives by seeking applications for its various apparatuses, such as electrical transformers, which are regarded as one of essential parts of the electrical grid. This paper presents an extensive assessment of the literature on the uses of nanotechnology principles for transformers, with a focus on studies pertaining to dielectric fluids, monitoring systems, solid or outdoor insulators & many other components. The most recent research on the use of nanomaterials in transformer applications has been examined and documented. Furthermore, prospects for further investigation

Thermo-Chemo-Mechanical Modeling of Residual Stress in Unidirectional Carbon Fiber-Reinforced Polymers during Manufacture.

The application of carbon fiber-reinforced composite materials in marine engineering is growing steadily. The mechanical properties of unbonded flexible risers using composite tensile armor wire are highly valued. However, the curing process generates a certain amount of internal residual stress. We present a detailed analysis of epoxy resin laminates to assess the impact of thermal, chemical, and mechanical effects on the curing stress and strain. An empirical model that correlates temperature and degree of cure was developed to precisely fit the elastic modulus data of the curing resin. The chemical kinetics of the epoxy resin system was characterized using differential scanning calorimetry (DSC), while the tensile relaxation modulus was determined through a dynamic mechanical analysis. The viscoelastic model was calibrated using the elastic modulus data of the cured resin combining temperature and degree of the curing (thermochemical kinetics) responses. Based on the principle of time-temperature superposition, the displacement factor and relaxation behavior of the material were also accurately captured by employing the same principle of time-temperature superposition. Utilizing the empirical model for degree of cure and modulus, we predicted micro-curing-induced strains in cured composite materials, which were then validated with experimental observations.

Magnetic behavior of a laminated magnetic core in the presence of interlaminar faults: A simulation method based on fractional operators

Stacks of grain-oriented silicon steel (GO FeSi) laminations play a crucial role as magnetic cores of power transformers. These cores undergo degradation over time due to corrosion, thermal cycles, etc. Geometrical abnormalities and residual stress from manufacturing processes exacerbate these degradation processes. Edge burrs can form around cut edges and lead to InterLaminar Faults (ILFs). In a recent work, we described an innovative method for simulating dynamical GO FeSi lamination hysteresis cycles. This method can be applied without any change to a stack of electrically isolated laminations, like in a magnetic core. It is especially easy when the working conditions impose a homogeneous behavior (B-imposed conditions). The simulation technique combines the resolution of the magnetic diffusion equation and a fractional differential equation as material law, yielding excellent simulation results across a broad frequency range with only two parameters accounting for the dynamic contribution. This new article outlines the successful extension of this simulation method to consider ILFs and predict their impact on the performances. For this, lamination stacks were initially simulated under full short-circuit conditions. Then, we used linear combinations between responses from these stacks and flawless ones. The simulation successfully reproduced the experimental data obtained for one or three aligned ILFs on several conditions. Then it was used to predict the behavior of additional aligned ILFs and/or different numbers of laminations in the simulated stack.

BiPLS-RF: A hybrid wavelength selection strategy for laser induced fluorescence spectroscopy of power transformer oil

In this paper, a method for indirect diagnosis of transformer faults based on the fluorescence spectrum and characteristic wavelength screening of transformer oil has been proposed. Specifically, a hybrid strategy (BiPLS-RF) for establishing the fluorescence spectrum feature screening of transformer oil using backward interval partial least squares (BiPLS) and random forest (RF) has been proposed. Aiming at the problem of transformer fault diagnosis, the laser induced fluorescence (LIF) spectroscopy of transformer oil in different states was first collected, and it is found that the fluorescence spectrum intensity of normal transformer oil was stronger than that of faulty transformer oil. Then the characteristic bands of the original fluorescence spectra were screened by BiPLS. It is found that when the original fluorescence spectra were divided into 15 sub-intervals, the minimum root mean squares error of cross-validation can be obtained by selecting 3 sub-intervals (including 411 wavelengths). On this basis, RF was employed to further screen the characteristic wavelengths and realized the identification of the fluorescence spectrum. It is found that in the RF model composed of 54 trees, the selected 196 characteristic wavelengths of the fluorescence spectrum can minimize the analysis error (0.56%). In addition, the selected characteristic wavelength information was fed into other common classifiers to construct a fluorescence spectrum identification model, which further proved the effectiveness of BiPLS-RF for wavelength selection for LIF spectroscopy of power transformer oil. The results show that it is feasible to use BiPLS-RF to screen the characteristic wavelength of LIF spectroscopy and apply it to transformer fault diagnosis, which provides a new solution for transformer fault diagnosis.

Research on soft measurement model of flow in bends of primary circuits of the nuclear power plant

When measuring the coolant flow in a nuclear power plant using the elbow flowmeter, the complex fluid-heat coupling environment at the measurement location and other factors will affect the accuracy of the flow measurement, and the uncertainty of the influencing factors on the flow measurement needs to be considered to improve the measurement accuracy. To address this problem, this paper adopts the finite element simulation method to simulate and analyze the flow-heat field of the bend section of the primary circuit of a nuclear power plant and optimizes the Optimal Cross-section selection of the pipeline for flow measurement. Based on the pressure values measured using the traditional method, temperature information is added, and a BP neural network bend pipe flow soft measurement model based on the whale optimization algorithm is established to quantify the effects of temperature and pressure on flow measurement. The experimental results show that compared with the traditional engineering empirical method, the average absolute percentage error measured by the soft measurement method is reduced from 2.57 % to 0.21 %, which realizes the accurate measurement of the coolant flow rate of the elbow pipe.