Transformer Research Articles

Investigation of Dynamic Viscoelastic Characteristics of Permeable Asphalt

In order to provide a basis for the structural analysis, design and maintenance of permeable asphalt pavements, and to promote their engineering promotion and application, this study investigated the dynamic viscoelastic properties of permeable asphalt mixtures (PAC-13) under complex stress states. A Simple Performance Tester (SPT) system was used to measure the dynamic modulus of the mix under complex stress states. The displacement factor and principal dynamic modulus curves were formed by fitting Sigmoidal functions and using 1stOpt (first optimization) software, the phase angle principal curves were further determined, and the dynamic modulus was predicted for the ambient phase (15–25 °C) using the Hirsch model. The results showed that the dynamic modulus of the mixtures decreases with an increasing temperature, and the maximum decrease in the dynamic modulus is 93% when the confining pressure is 100 kPa and the loading frequency is 10 Hz. The dynamic modulus increases with an increasing confining pressure and loading frequency, the maximum increase with an increasing confining pressure is 26.1% when the temperature is 25 °C and the loading frequency is 10 Hz, and the maximum increase with an increasing loading frequency is 411% when the temperature is 25 °C and the confining pressure is 100 Hz. The dynamic modulus has a strong frequency dependence at low temperatures, while it is stress-dependent at high temperatures. Meanwhile, based on the Hirsch model, a new modified prediction model was developed, which can well predict the dynamic modulus of permeable asphalt mixtures at room temperature.

New Short-Circuited Coaxial Method Implementation for Complex Permittivity Measurement of Power Transformer Oils

The investigation into the dielectric properties of transformer oils has been a focal point in both historical and contemporary electric insulation technology as high-voltage applications have greatly benefited from the continuous research efforts in this field. The dielectric permittivity is one of the factors to be negotiated when discussing the electric insulation of any material. This work aims to showcase a new short-circuited coaxial cable method to evaluate the complex dielectric permittivity of palm, sunflower and rapeseed oil and review other previously used measurement techniques. Mainly, the test was performed using a sample holder that represents a short-ended coaxial cable filled with the test material which is connected to one port of a vector network analyser. The measurements of the input impedance for various oil samples were conducted at frequencies ranging from 1 MHz to 10 MHz. The technique uses the value of the input impedance which depends on the reflected signals from the test sample to the network analyser, consequently, the value of the dielectric permittivity and dielectric loss have been calculated using the short circuit impedance formula which involves numerical methods to get the results which shows that this method can be used as an alternative to investigate the oils insulation parameters as it provides smooth results for permittivity without any divergence, ensuring more reliable and consistent measurements as well as providing high accuracy in determining the permittivity of materials.

Splitter-Based Sensors Realized via POFs Coupled by a Micro-Trench Filled with a Molecularly Imprinted Polymer

An optical–chemical sensor based on two modified plastic optical fibers (POFs) and a molecularly imprinted polymer (MIP) is realized and tested for the detection of 2-furaldehyde (2-FAL). The 2-FAL measurement is a scientific topic of great interest in different application fields, such as human health and life status monitoring in power transformers. The proposed sensor is realized by using two POFs as segmented waveguides (SW) coupled through a micro-trench milled between the fibers and then filled with a specific MIP for the 2-FAL detection. The experimental results show that the developed intensity-based sensor system is highly selective and sensitive to 2-FAL detection in aqueous solutions, with a limit of detection of about 0.04 mg L−1. The proposed sensing approach is simple and low-cost, and it shows performance comparable to that of plasmonic MIP-based sensors present in the literature for 2-FAL detection.

Ti3C2Tx (T = F, O, OH) as a sensor for dissolved gas in transformer oil: A theoretical study

Power transformer is the hub of power grid energy transmission, and its safe and reliable operation is very important to power grid. In this paper, based on density functional theory, Ti3C2Tx (T = F, O, OH) material was used as sensing material. The adsorption models of Ti3C2Tx and dissolved characteristic gases (H2, CH4, C2H4, C2H6, C2H2, CO, and CO2) in transformer oil are constructed. The adsorption distance, bond length change, adsorption energy, charge transfer and density of states (DOS) of the adsorption system were analyzed. The results showed that the gases can adsorb on the surface of Ti3C2Tx spontaneously. Specifically, CO2, CO, C2H2 and C2H4 exhibit a strong adsorption effect on Ti3C2(OH)2, forming hydrogen bonds or chemical bonds, accompanied by large charge transfer and adsorption energy, indicating Ti3C2(OH)2 shows a good sensing performance for these gases.

DC bias impact analysis on the capability of power transformer and failure risks

A significant transition towards low-carbon, renewable energy sources and technological adaptation is mandatory in the present scenario to address climate change and achieve a sustainable energy system. Aligned with the upgradation in technology, as the need for efficient and smart energy grows, power electronics are being extensively used in power systems. Various factors can affect the pervasive use of power electronics associated with power transformers, thereby introducing the DC bias in the system. DC bias in the transformer leads to a shift in operating point, a rise in excitation current and a generation of harmonics. This causes an increase in core losses and temperature, resulting in exceeding the thermal limits and leading to transformer failure. Consequently, there is an emergency need to understand the DC bias capability of power transformers that aims at optimal transformer selection without being overrated. This research employs Ansys maxwell software to model and simulate a 500 kVA, 11kV/420V three-phase power transformer. The experimental investigation is carried out through a scale-down value of a 5 kVA laboratory prototype. The research findings revealed a significant negative impact of DC bias on power transformer capabilities that could be detrimental to its performance, health and lifespan. The analysis results provide valuable insights for the design engineer during the initial stages of designing a high-performance, cost-effective transformer with a low failure rate.

A causal reasoning approach for power transformer failure diagnosis

A causal reasoning approach for power transformer failure diagnosis

Experimental Study of Clamping Pressure during Step Loading of Power Transformer with and without Prior Energisation

Experimental Study of Clamping Pressure during Step Loading of Power Transformer with and without Prior Energisation

New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers

The dissolved gas analysis of insulating oil in power transformers can provide valuable information about fault diagnosis. Power transformer datasets are often imbalanced, worsening the performance of machine learning-based fault classifiers. A critical step is choosing the proper evaluation metric to select features, models, and oversampling techniques. However, no clear-cut, thorough guidance on that choice is available to date. In this work, we shed light on this subject by introducing new tailored evaluation metrics. Our results and discussions bring fresh insights into which learning setups are more effective for imbalanced datasets.

A new hybrid approach for cold load restoration using deep learning

After an extended outage in a power system, restoration of feeders with high penetration levels of thermostatically controlled loads (TCLs) will be a challenging problem. By restoring these feeders, the demand will increase several times the normal conditions which is called the cold load pick-up (CLPU) phenomenon. This can lead to overload of equipment such as power transformers in transmission substations. In order to increase the reliability of network load supply, in transmission substations, usually several transformers are used which can be operated in parallel by closing circuit breakers on the secondary side called bus section (BS). When the restoration process is long and the loads are cold, more loads can be restored by closing the BSs and paralleling the transformers. On the other hand, it is not possible to close all the BSs simultaneously because paralleling all the transformers at the same time increases the short circuit level on the secondary side. Furthermore, in power systems, some loads are voltage-dependent and by reducing the voltage, their demand can be decreased and therefore the restoration process is expedited. Due to the increase in demand caused by CLPU, additional stress is applied to the power system, which can lead to operational constraints violation and even long-term voltage instability. Therefore, at the time of restoration of each feeder, the settings of voltage control devices are updated to avoid the violation of operational constraints and the occurrence of wider events. In this paper, in order to reduce cold load restoration time, closing the bus sections and voltage reduction are used simultaneously. Also, short circuit and operational constraints are considered. The cold load restoration problem is formulated as a mixed integer nonlinear programming and is solved using the pattern search algorithm. Since there are many decision variables and solving the problem is time-consuming, a combination of deep learning networks, pattern search algorithm and parallel computing is used to accelerate the computations.

Heating and cooling challenges for electric vehicles: A comprehensive experimental study

An efficient mobile air conditioning (MAC) system using a safe and climate-friendly refrigerant is necessary for human comfort, increasing the vehicle’s energy performance, and reducing the fuel/electricity consumption. In the current work, we perform a drop-in experimental comparison for low GWP alternatives to R134a in reversible MAC systems. The study compares two lower global warming potential (GWP) refrigerants; R456A, a non-flammable refrigerant for R134a direct MAC replacement, and R1234yf, an ultra-low GWP and mildly flammable refrigerant that is the standard for new MAC systems. The system’s operational and energy performance is investigated, simulating a wide range of indoor and outdoor steady-state conditions. The measurements are recorded operating a fully monitored vapour compression test rig that uses secondary circuits to set the target conditions. In the heating mode (heat pump), the evaporation temperatures are set at −10 °C, −5 °C, and 0 °C, and condensation temperatures at 40 °C, 50 °C, and 60 °C. In cooling mode (air conditioning), the condensation temperatures are 37.5 °C, 45 °C, and 52.5 °C and are combined with evaporation temperatures of −7.5 °C, 0 °C, and 7.5 °C. In the heating mode, R456A presented a competitive COP to R134a with a reduction of less than 5 %, more remarkable at lower evaporation temperatures. In the cooling mode, R456A showed the lowest COP at a low condensation temperature. R456A and R1234yf COP are comparable at a higher condensation temperature, which suits the T3 climate conditions.

Surgical procedures and complications in placement of totally implantable venous access port in pediatric hemophilia patients: A retrospective analysis

This retrospective study at Beijing Children's Hospital (2020–2023) analyzed surgical procedures and complications in 24 pediatric hemophilia patients undergoing Totally Implantable Venous Access Port (TIVAP) insertion, primarily in the right jugular vein (RJV). We detailed the surgical process, including patient demographics and intraoperative imaging use. The choice of the RJV for TIVAP placement was influenced by its larger diameter and superficial anatomical position, potentially reducing risks like thrombosis and infection. Our findings support the RJV as a safer alternative for port placement in pediatric patients, aligning with current literature. Statistical analysis revealed no significant correlation between complications and baseline characteristics like weight and diagnosis type. However, the length of hospital stay and implant brand were significant risk factors for catheter or port displacement and removal. The limited patient number may introduce bias, suggesting a need for further studies with larger samples. Despite a 14.7 %–33 % complication rate and 5 port removals, the advantages of TIVAP, including reliable venous access, reduced discomfort, and treatment convenience, were evident. Most complications improved with symptomatic treatment, and there were no deaths due to port-related complications, underscoring the impact of TIVAP on improving pediatric hemophilia treatment.

Research on infrared spectroscopy detection of furfural content in transformer oil based on acetonitrile extraction

Abstract The stable operation of power transformers depends on the oil-paper insulation system, in which the aging degree of insulating paper is the key to evaluate the remaining life of the transformer. Currently, methanol extraction is widely used to detect the furfural content in oil to evaluate the aging state of insulating paper, but it ignores the influence of methanol produced during the operation of the transformer on the extraction results. To address this issue, this paper proposes a new method that can replace methanol extraction for detecting furfural in oil through simulation and experiment. Firstly, through simulation and experiment, it is proved that the strong vibration absorption peaks at 1677 cm-1 for furfural carbonyl and 2240 cm-1 for acetonitrile cyano can be used to establish a new method for extracting furfural content in oil using acetonitrile. A quantitative model between the concentration x of furfural in the furfural-acetonitrile mixed solution and the area y of the infrared absorption peak at 1677 cm-1 is established, with a goodness of fit of 0.9974. Secondly, a comparison between direct detection and acetonitrile extraction methods is conducted. The results show that direct detection is simple to operate, but the minimum detection concentration is 40mg/L, which is difficult to meet practical requirements. Acetonitrile extraction can reduce the minimum detection concentration to 0.1mg/L. At the same time, the extraction conditions are analyzed to determine the extraction ratio of 30, extraction times of 5, and extraction rate of 60%. Finally, the proposed detection method is applied to thermal aging tests on insulating paper of different types. The experimental results show that the proposed method has good repeatability and improves the detection resolution of furfural content. This paper combines infrared spectroscopy with acetonitrile extraction technology to open up a more efficient and practical new method for detecting furfural content in transformer oil.

Automated Measurement System of High Accuracy for Shunt Reactors

System for automated transformer tests (ATT) enables different measurements on power transformers, from simple to the most complex, with a high degree of automation. ATT system is now expanded with modules for measurements on shunt reactors. The measurements related to shunt reactors that are covered with the system are: measurement of losses, impedance measurement, linearity test, zero impedance measurement, winding resistance measurement, temperature rise test and measurement of mutual reactance. Besides the measurement, ATT enables data analysis and report generation, all in accordance with [1,2]. Measurement of shunt reactor losses with satisfactory accuracy is a big challenge due to power factors of the reactors as low as 0,001. A system for loss measurements with high accuracy, consisting of measuring bridge, current comparator and standard capacitor is integrated into ATT. In the paper, a comparison in terms of loss measurement accuracy on shunt reactor is given for two measurement systems – a newly integrated system for shunt reactors and standard system used for transformer measurements.

Gray Matter Atrophy in a 6-OHDA-induced Model of Parkinson’s Disease

IntroductionMagnetic resonance imaging (MRI) based brain morphometric changes in unilateral 6-hydroxydopamine (6-OHDA) induced Parkinson’s disease (PD) model can be elucidated using voxel-based morphometry (VBM), study of alterations in gray matter volume and Machine Learning (ML) based analyses. MethodsWe investigated gray matter atrophy in 6-OHDA induced PD model as compared to sham control using statistical and ML based analysis. VBM and atlas-based volumetric analysis was carried out at regional level. Support vector machine (SVM)-based algorithms wherein features (volume) extracted from (a) each of the 150 brain regions (b) statistically significant features (only) and (c) volumes of each cluster identified after application of VBM (VBM_Vol) were used for training the decision model. The lesion of the 6-OHDA model was validated by estimating the net contralateral rotational behaviour by the injection of apomorphine drug and motor impairment was assessed by rotarod and open field test. Results and discussionIn PD, gray matter volume (GMV) atrophy was noted in bilateral cortical and subcortical brain regions, especially in the internal capsule, substantia nigra, midbrain, primary motor cortex and basal ganglia-thalamocortical circuits in comparison with sham control. Behavioural results revealed an impairment in motor performance. SVM analysis showed 100% classification accuracy, sensitivity and specificity at both 3 and 7 weeks using VBM_Vol. ConclusionUnilateral 6-OHDA induced GMV changes in both hemispheres at 7th week may be associated with progression of the disease in the PD model. SVM based approaches provide an increased classification accuracy to elucidate GMV atrophy.

Proposal of Testing Procedure for Resonance and Ferroresonance Inception Possibility in Instrument Transformers

This paper deals with the possibility of ferroresonance occurence in the interaction between circuit breakers and inductive instrument transformers. Existing standards lack guidance on testing for ferroresonant behaviour. The paper proposes a standardized testing procedure and presents measurements on a full-scale system. EMTP simulations complement the measurements for a broader network topology analysis, i.e. circuit breaker capacitance combinations. EMTP simulations are validated for a 170 kV voltage transformer and a combined instrument transformer, showing accuracy within 10%. The paper also extends the EMTP modelling application to a 420 kV voltage power transformer during design phase, ensuring it doesn't experience ferroresonance. This study offers a practical approach for testing and simulating ferroresonance in inductive instrument transformers, contributing to the safe operation of power networks.

Miniaturized quantitative detection of particles in transformer oil based on lensless holographic microscopy

The insulation effectiveness of transformer oil has a substantial impact on the safety of transformers. In the presence of particles within the transformer oil, they are able to adversely affect its performance, potentially originating from electrical arcs or severe carbonization processes. These particles are commonly capable of reducing the insulating capability of the transformer oil, thereby enhancing the risk of transformer failure. To overcome this dilemma, we propose a miniaturized quantitative particle detection approach in transformer oil based on a lensless digital holographic microscope. In general, methods based on traditional high-resolution optical microscopy are essentially limited by their fairly huge cost and importability. However, the proposed approach utilizes a homemade miniaturized model combined with a CMOS image sensor to achieve microscopic imaging. The deployed device here weighs ∼ 50 g, making it portable and affordable for on-the-go detection. By simplifying the microscope system, we can quickly and accurately detect tiny particles in transformer oils. This enables us to efficiently assess the status of impurities in power transformers and to identify potential issues in advance, thereby enhancing the reliability of transformer oil. This detection method not only features simplicity of operation, convenient on-the-go detection, low cost, and high precision but also provides a convenient approach for accurately assessing the condition of impurity particles in power transformers and preemptively identifying potential issues.

Allocation of charging stations in Kota city using data analysis/machine learning

ABSTRACT This paper proposes the allocation of charging stations of optimal capacity at appropriate locations within Kota city in Rajasthan state of India. The location of charging stations has been identified by considering various parameters including population density, family income, distribution network performance, number of electric vehicles and transformer capacity. The proposed approach of allocation of charging stations has been modeled mathematically and also by machine learning algorithms such as data mining, clustering with all the possible scenarios and constraints. In identifying suitable places for positioning charging stations in the city by observing different parameters and aspects, i.e. geo-statistical approaches, minimum traveling time and travel distance for charging stations has been included in the proposed approach. The proposed approach has been tested in MATLAB/Simulink environment.

The occurrence and mechanism of hysteresis between axial deformation and short‐circuit electromagnetic force during the vibration of power transformer windings

AbstractMechanical stability is one of the core capabilities of power transformers. External short‐circuit accidents are the main cause of winding instability. International Electrotechnical Commission 60076‐5 standard recommends a method to calculate the short‐circuit strength of power transformer windings by comparing the stress within windings under the effect of the maximum electromagnetic force with the critical stress of the winding. This method assumes that the maximum deformation will be produced by the maximum electromagnetic force, which corresponds to the first peak of the waveform. However, owing to the interactions between disks during the vibration process, the maximum deformation may occur after the occurrence of the maximum electromagnetic force. The hysteresis phenomenon between disk deformation and electromagnetic force is studied. The definition of the hysteresis phenomenon during the vibration process is first demonstrated, and the mechanism of the hysteresis phenomenon is investigated. The vibration model is established. By decoupling analysis, the conditions for the formation of hysteresis are proposed, and the mechanism of the hysteresis phenomenon is validated by the experiment, which is conducted on a winding sample. In the deformation formula, the term that determines the time‐varying characteristic is found. The waveform‐determining term is the difference between the two cosine components, whose frequencies are the natural vibration frequency and the electromagnetic force frequency. When the two frequencies are close, the maximum deformation lags behind the maximum force, and the hysteresis phenomenon occurs.

An Approach to Remaining Life Estimation for Power Transformers with Biased Data

Abstract Power transformers are among the most important equipment in power systems. Estimating the remaining life of transformers is an important issue for energy companies because of the need for planning maintenance and capital expenditures. Traditional methods fail to provide accurate results mainly because data is incomplete, and there is a significant lack of records of equipment scrapped before 2015. Given this, the study provides a new approach to life estimation for power transformers based on survival analysis. First, the data was selected from only the transformers that were still operating after 2015. Then, a Bayesian survival model with a truncated Weibull distribution is developed to fit the biased data. Parameters are, at last, estimated through maximum likelihood estimation. The experimental results indicate that the proposed method performs well.

Power Transformer On-Load Capacity-Regulating Control and Optimization Based on Load Forecasting and Hesitant Fuzzy Control

The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transformer capacity based on load changes is of great significance. The existing control methods for on-load capacity-regulating (OLCR) transformers have low timeliness, and the daily switching frequency of the capacity-regulating switch is not controlled. To ensure the safe and stable operation of transformers, this paper proposes a control method for OLCR transformers based on load prediction and fuzzy control. Firstly, the operating principle of OLCR transformers is analyzed, and a multi-strategy enhanced dung beetle optimizer (MSDBO) combined with a CNN−LSTM model is proposed for load forecasting. On this basis, the daily switching frequency of the capacity-regulating transformer is introduced, and hesitant fuzzy control is used to select the optimal capacity-regulating strategy relying on three factors: loss, economy, and switching frequency. Finally, simulation models are constructed using the MATLAB/SIMULINK platform and simulation analysis is conducted to verify the effectiveness and superiority of the proposed control method. For the three scenarios in this paper, the method reduces daily power loss by 28.5% to 56.3% and daily operating costs by 25.4% to 50.8%. The method used in this paper can sacrifice 3.5% to 9.2% of the loss reduction capability in exchange for reducing the number of switch operations by 28.6% to 57.1%, significantly extending the lifespan of the switches and thereby increasing the operational lifespan of the transformer.