Operation Of Transformer Research Articles

Evaluation and prediction of the transformer health index based on matter element information entropy and SVM

To solve the problem that the operation state of transformer is difficult to quantify, a method of quantitative evaluation and prediction of transformer operating state is proposed, which combines the information entropy of matter element and Support Vector Machine. In the evaluation, various hydrogen gases in the transformer operation are taken as the evaluation indexes and the three-dimensional cross compound element is constructed. The analytic hierarchy process (AHP) is used to determine the theoretical weight of the evaluation index, and the entropy method is used to determine the objective weight of the evaluation index, and the final weight is the joint weight of the theoretical weight and the objective weight. Transformer Health index is calculated by using complex element correlation entropy. In prediction, the grid search, genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize the parameters of Support Vector Machine. and the prediction model of Health index is established by SVM. Experiment results show that the Support Vector Machine based on Gauss kernel function and genetic algorithm has a prominent effect on the prediction of health index.

Research on Changes in the Phase Shift Angle and Admittance of the Cellulose–Bio-Oil Composite under the Influence of Increasing Moisture during the Long-Term Operation of Power Transformers

In this study, the temperature–frequency dependencies of the pressboard–bio-oil–water nanoparticle composite’s fundamental parameters—phase shift angle and admittance—were examined using the Frequency Domain Spectroscopy method. Measurements were conducted in a frequency range of 10−4 Hz to 5 × 103 Hz at temperatures ranging from 293.15 K to 343.15 K, with a step of 10 K. The temperature stabilization accuracy was less than ±0.05 K. A total of 15 Arrhenius plots were determined for various phase shift angle values, from which 15 values and the average activation energy of relaxation time were determined. Similarly, the values of the activation energy of admittance relaxation time and the activation energy of admittance were determined. It was established that all three average values are identical within the bounds of uncertainty. Based on 45 values, a generalized activation energy was determined, with a value of ΔW ≈ (1.032 ± 0.0196) eV. Using the generalized activation energy value, the phase shift angle curves determined for all temperatures were recalculated to a temperature of 293.15 K. It was found that after conversion, all curves perfectly overlap. A similar operation was carried out for the frequency dependencies of admittance. In this case, too, the recalculated dependencies perfectly match. This means that the shape of the frequency dependencies of the phase shift angle and admittance depends solely on the moisture content in the pressboard–bio-oil–water nanodrop composite. The position of the curves in doubly logarithmic coordinates depends only on temperature and is determined by the generalized activation energy Using the generalized activation energy determined in this study will allow for the development of accurate methods for estimating moisture content in cellulose insulation of power transformers containing bio-oil. This will contribute to the detection of critical moisture content, which is about 5% by weight, and to the elimination of the risk, associated with such a high moisture content, of catastrophic failure of power transformers.

Scalability Assessment of the Parallel Operation of Direct Current Transformer

Scalability Assessment of the Parallel Operation of Direct Current Transformer

Analysis and identification of transformer acoustic signal based on compressed sensing

The operation state of the transformer affects the operation of the whole power system. Aiming at the application of online monitoring of transformer operation, based on compressive sensing theory and wavelet packet analysis technology, a transformer acoustic fault diagnosis method based on compressive sensing is proposed. Firstly, the partial Hadamard matrix is constructed as the observation matrix to compress the acoustic signal of the transformer. The energy decomposition of the compressed signal is completed based on the wavelet packet. Finally, the feature selection is completed by considering the energy distribution characteristics using wavelet information entropy. The acoustic signal characteristics of transformer core fault simulation data are extracted by this method, and the fault diagnosis simulation is completed by using a particle swarm -SVM classifier. The results show that this method can obtain higher fault identification accuracy under the condition of a high compression ratio.

Direction of Arrival Estimation With Gain-Phase Uncertainties in Unknown Nonuniform Noise

This paper considers the problem of direction-of-arrival (DOA) estimation for narrowband source signals under the coexistence of gain-phase uncertainties and nonuniform noise. Firstly, by exploiting the low-rank property of the covariance of signal components, and the diagonal property of noise covariance, an optimization problem with low-rank constraints is formulated to estimate the nonuniform noise covariance matrix, which is then solved efficiently by the alternating direction method of multipliers (ADMM). Secondly, a linear transformation operator is employed to construct a rank reduction (RARE) estimator, and subsequently an initial DOA and gain-phase uncertainties are obtained via one-dimensional (1-D) spectral search. With initial estimates, an augmented DOA estimation is finally achieved by constructing a weighted and structured sparse total least-squares (WSS-TLS) optimization problem, which is solved using the block coordinate descent algorithm. Simulation results show the superior performance of the proposed method, and it achieves better tradeoff between computational complexity and performance, compared to existing methods.

Human Motion Prediction Based on a Multi-Scale Hypergraph for Intangible Cultural Heritage Dance Videos

Compared to traditional dance, intangible cultural heritage dance often involves the isotropic extension of choreographic actions, utilizing both upper and lower limbs. This characteristic choreography style makes the remote joints lack interaction, consequently reducing accuracy in existing human motion prediction methods. Therefore, we propose a human motion prediction method based on the multi-scale hypergraph convolutional network of the intangible cultural heritage dance video. Firstly, this method inputs the 3D human posture sequence from intangible cultural heritage dance videos. The hypergraph is designed according to the synergistic relationship of the human joints in the intangible cultural heritage dance video, which is used to represent the spatial correlation of the 3D human posture. Then, a multi-scale hypergraph convolutional network is constructed, utilizing multi-scale transformation operators to segment the human skeleton into different scales. This network adopts a graph structure to represent the 3D human posture at different scales, which is then used by the single-scalar fusion operator to spatial features in the 3D human posture sequence are extracted by fusing the feature information of the hypergraph and the multi-scale graph. Finally, the Temporal Graph Transformer network is introduced to capture the temporal dependence among adjacent frames within the time domain. This facilitates the extraction of temporal features from the 3D human posture sequence, ultimately enabling the prediction of future 3D human posture sequences. Experiments show that we achieve the best performance in both short-term and long-term human motion prediction when compared to Motion-Mixer and Motion-Attention algorithms on Human3.6M and 3DPW datasets. In addition, ablation experiments show that our method can predict more precise 3D human pose sequences, even in the presence of isotropic extensions of upper and lower limbs in intangible cultural heritage dance videos. This approach effectively addresses the issue of missing segments in intangible cultural heritage dance videos.

Developing a novel optical sensor for condition assessment of aged mineral oil based on carbon dots

Power transformers are major linking equipment in power systems, most of which use oil as insulating medium. This work aims to develop a sensitive opto-active material for effective condition assessment of mineral oil. Thermal aging is performed in the lab under conditions that simulate the actual operation of power transformers. Novel carbon dots are prepared in the lab to be used as an optical sensor for the aged mineral oil. The interaction between a fixed 2 mL of carbon dots and mineral oil samples aged for different times and at different concentrations is studied. A fixed oil concentration of 250 µL is selected and the emission spectra are linearly correlated with aging and dielectric dissipation factor. The in-service limits of dielectric dissipation factor are used to judge the mineral oil’s condition. The obtained photoluminescence spectra proved the suitability of this novel developed carbon dots for condition assessment of mineral oil.

Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging.

Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring additional adjuvant systemic therapy. Identification of this latter cohort that is likely to recur following surgery is key to better personalized therapy selection, but there is a lack of proper quantitative assessment tools for potential clinical adoption. The purpose of this study is to employ Mueller matrix (MM) polarized light microscopy in combination with supervised machine learning (ML) to quantitatively analyze the prognostic value of peri-tumoral collagen in CRC in relation to 5-year local recurrence (LR). A simple MM microscope setup was used to image surgical resection samples acquired from stage III CRC patients. Various potential biomarkers of LR were derived from MM elements via decomposition and transformation operations. These were used as features by different supervised ML models to distinguish samples from patients that locally recurred 5 years later from those that did not. Using the top five most prognostic polarimetric biomarkers ranked by their relevant feature importances, the best-performing XGBoost model achieved a patient-level accuracy of 86%. When the patient pool was further stratified, 96% accuracy was achieved within a tumor-stage-III sub-cohort. ML-aided polarimetric analysis of collagenous stroma may provide prognostic value toward improving the clinical management of CRC patients.

AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference With Transformers

Self-attention-based transformer models have achieved tremendous success in the domain of natural language processing. Despite their efficacy, accelerating the transformer is challenging due to its quadratic computational complexity and large activation sizes. Existing transformer accelerators attempt to prune its tokens to reduce memory access, albeit with high compute overheads. Moreover, previous works directly operate on large matrices involved in the attention operation, which limits hardware utilization. In order to address these challenges, this work proposes a novel dynamic inference scheme, DynaTran, which prunes activations at runtime with low overhead, substantially reducing the number of ineffectual operations. This improves the throughput of transformer inference. We further propose tiling the matrices in transformer operations along with diverse dataflows to improve data reuse, thus enabling higher energy efficiency. To effectively implement these methods, we propose AccelTran, a novel accelerator architecture for transformers. Extensive experiments with different models and benchmarks demonstrate that DynaTran achieves higher accuracy than the state-of-the-art top-k hardware-aware pruning strategy while attaining up to 1.2× higher sparsity. One of our proposed accelerators, AccelTran-Edge, achieves 330K× higher throughput with 93K× lower energy requirement when compared to a Raspberry Pi device. On the other hand, AccelTran-Server achieves 5.73× higher throughput and 3.69× lower energy consumption compared to the state-of-the-art transformer co-processor, Energon. The simulation source code is available at.

High Temperature Superconducting Transformer Operating at an Increased Frequency of Alternating Current

The study's aim is to substantiate and employ materials with high technical characteristics for the active elements of a transformer, particularly in the context of increased operating frequencies. To accomplish this objective, the following tasks were addressed: An analysis and synthesis of the properties of a magnetic core composed of amorphous iron and superconducting windings in a high-temperature superconducting transformer (HST) were conducted. Dependencies and graphs illustrating the impact of frequency increase on heat losses related to hysteresis and eddy currents were derived. These losses occur within the magnetic circuit. The study also demonstrates the relationship between reducing size and weight parameters and material consumption of windings in high-temperature superconducting (HST) tapes. Current densities in these tapes can reach up to 500 A/mm². The most noteworthy outcome, taking advantage of superconductivity in the HST, is that HST acts as an ideal diamagnetic material. Additionally, the windings exhibit high electrical conductivity. This resolves issues related to current displacement to the conductor's surface, commonly known as the "skin effect," and effectively addresses concerns about increased resistance in transformers and electrical machines. The absence of the skin effect in HST conductors has been rigorously demonstrated using Bessel functions. The significance of these results is in enhancing the efficiency of transformers operating at high frequencies. This is achieved by synthesizing the properties of the transformer's active elements, which include an amorphous iron magnetic core, HST windings, and a dielectric medium utilizing liquid nitrogen.

New Algorithm Applied to Transformers' Failures Detection Based on Karhunen–Loève Transform

Industry and science have been growing attention to developing systems that ensure the integrity of high voltage devices like power transformers. The goal is to avoid unexpected stoppages by detecting incipient failures before they become a major problem. In this context, the detection of discharge activity is an effective way to assess the condition operation of power transformers since this type of flaw can lead the transformer to total failure. The effectiveness of the fault diagnosis systems is related to their capability to distinguish the types of discharges since different flaws require different maintenance planning. This article proposes a new data analysis which combined the frequency spectrum of the signals with the Karhunen-Loève Transform to perform self-organization maps. The effectiveness of this analysis was validated by comparing it with the Fundamental Signals Properties Classification Technique, which is widely applied for pattern recognition.Two types of sensing techniques were assessed in order to enhance the capability of the new approach. Results indicated that the new methodology presented lower standard deviation for data classification, being a promising tool to monitoring systems.

Kinh nghiệm thực hiện chuyển đổi số tại một số cơ sở giáo dục đại học trên thế giới và bài học kinh nghiệm cho Việt Nam

Digital transformation in education in general and at higher education institutions in particular has been taking place strongly in recent years, bringing positive results for innovation in teaching and learning as well as training and scientific research governance. This study presents theoretical frameword of digital transfer in higher education institutions, accordingly analyzes the experience of implementing digital transformation at some leading universities in Asia and Europe in terms of digital infrastructure transformation, training programs and digital platform operations, from which some lessons can be drawn for Vietnam.

Construction of a model of steganographic embedding of the UAV identifier into ADS-B data

Secure data exchange in the control system of unmanned aerial vehicles (UAVs) is an important aspect for preventing unauthorized access and safety of aerial vehicles. Given the problems of automatic dependent surveillance-broadcast (ADS-B) data protection, the safety level of UAV flight tasks and air traffic in general is significantly reduced. Therefore, the protection of ADS-B data is an urgent task. The object of the study is the process of steganographic protection of ADS-B format data. A relevant problem of estimating the probabilistic time characteristics of the steganographic protection process is solved, taking into account the features of data embedding in the ADS-B format container. To solve it, a mathematical formalization of the methods of finding probabilistic-temporal characteristics of steganographic systems was carried out. A model of steganographic data transformation operations based on the Chinese remainder theorem has been built. The main difference of the model is taking into account the features of the ADS-B format data. This made it possible to formalize and evaluate the time functions of steganographic encoding and decoding of UAV identifiers with an integrated ADS-B system. A model of steganographic data transformation operations based on the finite integral ring theorem has been constructed. A list of operations performed in the developed algorithm has been compiled. This made it possible to carry out mathematical formalization of operations for complex use in the model of steganographic protection of UAV identifiers with a built-in ADS-B system. The mathematical model was studied and the estimation of the random value of the time of steganographic transformation of data, as well as the confidence interval, was performed. With the help of the reported set of models, it is possible to estimate the probability of the algorithm’s execution time falling within the given interval. The results of the calculation of probabilistic-time characteristics could be used in models of a higher level of the hierarchy

Technical aspects of the development of solar energy refrigeration technology on solid sorbents

Objective. The purpose of the study is a scientific review of the stages of development of refrigeration technology with sorption action on solid sorbents and an assessment of the current state of development of such household equipment in relation to the use of solar energy.Method. The dynamics of the development of solar energy refrigeration equipment based on sorption thermotransformers, the working vapors of which are solid sorbents and ozone-safe refrigerants, are analyzed. The beginning of development comes from the creation of periodic installations operating from the thermal energy of traditional energy carriers: hot water, burning of hydrocarbon or organic fuels, electric energy, and cooling with water and air and ends with modern refrigeration units operating from the energy of solar radiation and the daily temperature difference of the environment.Result. The peculiarities of making technical decisions underlying the design of the main devices of solar energy refrigeration units are noted. Circuit designs and operating principles of installations and their devices used to improve operating efficiency are described. The main trends in research conducted by scientists to improve the operation of solar energy thermal transformers of cyclic action are considered.Conclusion. This article may be useful to young scientists, engineers and designers for the design and development of innovative autonomous thermal transmitters and refrigeration units for cooling and short-term storage of products and medicines, obtaining food ice, air conditioning, operating independently from solar energy. Such installations are easy to manufacture and operate.

Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm

With the promotion of energy transformation, the utilization ratio of electrical power is progressively rising. Since electrical power is challenging to store, real-time production and consumption become imperative, imposing significant demands on the dependability and operational efficiency of electrical power apparatus. Suppose the load distribution among multiple transformers within a transformer network exhibits inequality. In such instances, it will amplify the total energy consumption during the voltage conversion process, and local, long-term high-load transformer networks become more susceptible to failures. In this article, we scrutinize the matter of transformer energy utilization in the context of electricity transmission within grid systems. We propose a methodology grounded on genetic algorithms to optimize transformer energy usage by dynamically redistributing loads among diverse transformers based on their operational status monitoring. In our experimentation, we employed three distinct approaches to enhance energy efficiency. The experimental findings evince that this approach facilitates swifter attainment of the optimal power level and diminishes the overall energy consumption during transformer operation. Moreover, it exhibits a heightened responsiveness to fluctuations in power demand from the electrical grid. Experimental results manifest that this technique can truncate monitoring time by 27% and curtail the overall energy consumption of the distribution transformer network by 11.81%. Lastly, we deliberate upon the potential applications of genetic algorithms in the realm of power equipment management and energy optimization issues.

Unsupervised Nonlinear Hyperspectral Unmixing with Reduced Spectral Variability via Superpixel-Based Fisher Transformation

In hyperspectral unmixing, dealing with nonlinear mixing effects and spectral variability (SV) is a significant challenge. Traditional linear unmixing can be seriously deteriorated by the coupled residuals of nonlinearity and SV in remote sensing scenarios. For the simplification of calculation, current unmixing studies usually separate the consideration of nonlinearity and SV. As a result, errors individually caused by the nonlinearity or SV still persist, potentially leading to overfitting and the decreased accuracy of estimated endmembers and abundances. In this paper, a novel unsupervised nonlinear unmixing method accounting for SV is proposed. First, an improved Fisher transformation scheme is constructed by combining an abundance-driven dynamic classification strategy with superpixel segmentation. It can enlarge the differences between different types of pixels and reduce the differences between pixels corresponding to the same class, thereby reducing the influence of SV. Besides, spectral similarity can be well maintained in local homogeneous regions. Second, the polynomial postnonlinear model is employed to represent observed pixels and explain nonlinear components. Regularized by a Fisher transformation operator and abundances’ spatial smoothness, data reconstruction errors in the original spectral space and the transformed space are weighed to derive the unmixing problem. Finally, this problem is solved by a dimensional division-based particle swarm optimization algorithm to produce accurate unmixing results. Extensive experiments on synthetic and real hyperspectral remote sensing data demonstrate the superiority of the proposed method in comparison with state-of-the-art approaches.

High proportion and large value harmonic current influence on the magnetic field, loss and temperature distribution for ultrahigh voltage converter transformer

AbstractThe temperature distribution of ultrahigh voltage (UHV) converter transformer is the key to its service life, allowable load and safe operation. The influence of a high‐proportion and large‐value harmonic current on the magnetic field, loss and temperature distribution of a UHV converter transformer is studied. A new calculation method is proposed to determine the winding temperature under the combined actions of multiple key factors. First, the skin effect of UHV converter transformer winding under high proportion and high current harmonics is analysed extensively. It is found that the increase of harmonic current frequency leads to exponential increase of winding loss and temperature by changing skin depth. Second, based on the superposition principle, a calculation method for winding loss considering harmonic current and different load rates is developed. The temperature distribution under different harmonic current frequencies and contents is obtained. The winding losses and temperature under different harmonic currents are quantified. Finally, a new calculation method is proposed for the converter transformer winding temperature, considering the combined actions of factors such as load rate, cooling oil inlet rate and temperature, harmonic current frequency and content. Experimental verification showed an error of only 0.58 K in the actual transformer hotspot temperature, confirming the effectiveness of this method. This method is of great significance for temperature control and safe operation of UHV converter transformers.

Steps toward a homogenization procedure for spiking neural P systems

A spiking neural P system (SN P system) is a model of computation inspired by the mechanism of a network of spiking neurons. A homogeneous SN P system is an SN P system whose neurons have identical rule set. In this work, we introduced an algorithm that, under certain condition, transforms a non-homogeneous SN P system Π to a homogeneous SN P system Π′ that computes the same set as the system Π. Two transformation operations called neuron translation and neuron scaling are introduced which are then used by the homogenization algorithm.

Transformation operator for the Schrodinger equation with additional exponential potential

In this paper, we consider the one-dimensional Schrodinger equation on the semiaxis with an additional exponential potential. Using transformation operators with the asymptotics at infinity, a triangular representation of a special solution of this equation is found. An estimate is obtained with respect to the kernel of the representation

Time-dependent system reliability analysis for mechanical on-load tap-changer with multiple failure modes

To regulate voltage level, the mechanical on-load tap-changer is a crucial series system of the ultra-high-voltage converter transformer. Whether its motion is reliable throughout the whole process is vital for the safe operation of converter transformer. Therefore, the time-dependent system reliability analysis is carried on for the mechanical on-load tap-changer with multiple failure modes based on the first-passage method. First, the failure modes of the mechanical on-load tap-changer are comprehensively analyzed according to the safety requirement, and then, the output error of each failure mode related to the moving mechanism of the system is modeled based on the motion equation to establish the varied limit-state functions of the time intervals over the whole motion. Then, the first-passage method is leveraged to solve this time-dependent reliability problem with multiple failure modes, in which the analytical formula of the outcrossing rate, the crucial element of the first-passage method, is derived by introducing the first-order moment of the doubly truncated multivariate Gaussian distribution. On this basis, the time-dependent system kinematic reliability is calculated using the Simpson integration algorithm. Finally, a numerical case is studied first, and in what follows, the practical engineering application to the mechanical on-load tap-changer is showcased in detail to demonstrate the effectiveness and efficiency of the proposed method by comparison with previous methods.