Operation Of Transformer Research Articles

Analysis of overvoltage impact on shell-type on-board traction transformer by multi-physics field simulation

With the rapid development and increasing demands for the high-speed railway in China, it is of great significance to examine the defects of the high-speed train and ensure the safety of the operating railway. In the actual operation of the high-speed train, there is a split-phase section to transit the train to the next substation power supply arm. In this particular stage, operational overvoltage would occur as the VCB and the pantograph switch on and off, which threatens the safety of the operation of high-speed trains and contributes to the breakdown of transformer windings, which is indispensable in transformer operation. In this work, a finite element model of shell type on board traction transformer is constructed and multi-physics field simulation results, including electromagnetic field, temperature field, and flow velocity field, are analyzed to investigate the potential risky spots in the practical operation of high-speed train under overvoltage impulse, which could be supportive in maintaining the devices and detecting the winding defects.

A transformer acoustic signal analysis method based on matrix pencil and hybrid deep neural network

Acoustic signal analysis is an important component of transformer online monitoring. Currently, traditional methods have problems such as low spectral resolution, imbalanced sample distribution, and unsatisfactory classification performance. This article first introduces the matrix pencil algorithm for time-frequency spectrum analysis of acoustic signals, and then uses the SMOTE algorithm to expand the imbalanced samples. Then, an ACmix hybrid deep neural network model is constructed to classify 11 types of transformer operation and environmental acoustic signals. Finally, detailed experiments were conducted on the method proposed in this paper, and the experimental results showed that the matrix pencil algorithm has high time-frequency resolution and good noise resistance performance. The SMOTE sample expansion method can significantly improve the recognition accuracy by more than 2%. Overall accuracy of the proposed method in acoustic signal classification tasks reaches 91.81%.

Surface enhanced substrate simulation optimized coupled PLS model for quantitative detection of acetone in transformer oil

AbstractAcetone is a reliable index to evaluate the aging state of oil paper insulation. It is of great significance to study a fast, highly sensitive and accurate method of detecting acetone in oil to ensure the safe and stable operation of oil-immersed transformers. In this paper, a method based on surface enhanced Raman spectroscopy for the detection of acetone in oil is investigated. Based on the mechanism of surface-enhanced Raman spectroscopy (SERS), a simulation model of surface-enhanced substrate was constructed using COMSOL software, and it was determined that the enhancement effect was better when silver nanowires/zinc oxide nanorods (Ag/ZnO) composite structure was selected. Ag/ZnO substrates were prepared by solvothermal method and experimentally tuned and characterized to obtain SERS substrates with high enhancement properties. The substrate coupled partial least square (PLS) model was used to establish a method for the detection of acetone in insulating oils, and its limit of quantification for acetone was 0.003 mg/g, which meets the requirements for engineering testing.

RGMU: A High-flexibility and Low-cost Reconfigurable Galois Field Multiplication Unit Design Approach for CGRCA

Finite field multiplication is a non-linear transformation operator that appears in the majority of symmetric cryptographic algorithms. Numerous specified finite field multiplication units have been proposed as a fundamental module in the coarse-grained reconfigurable cipher logic array to support more cryptographic algorithms; however, it will introduce low flexibility and high overhead, resulting in reduced performance of the coarse-grained reconfigurable cipher logic array. In this article, a high-flexibility and low-cost reconfigurable Galois field multiplication unit (RGMU) is proposed to balance the tradeoffs between the function, delay, and area. All the finite field multiplication operations, including maximum distance separable matrix multiplication, parallel update of Fibonacci linear feedback shift register, parallel update of Galois linear feedback shift register, and composite field multiplication, are analyzed and two basic operation components are abstracted. Further, a reconfigurable finite field multiplication computational model is established to demonstrate the efficacy of reconfigurable units and guide the design of RGMU with high performance. Finally, the overall architecture of RGMU and two multiplication circuits are introduced. Experimental results show that the RGMU can not only reduce the hardware overhead and power consumption but also has the unique advantage of satisfying all the finite field multiplication operations in symmetric cryptography algorithms.

Relevancy transformation operators in the form of polynomial functions

Relevancy transformation operators (RET operators) have been widely used in fuzzy systems modelling and the construction of weighted aggregation functions. Several construction methods of RET operators based on different aggregation functions such as t-norm, t-conorm and copula, have been proposed. In this paper, the attention is paid to the expression of RET operators, which is an important feature from an application the point of view. Polynomial RET operators are introduced as those RET operators in the form of polynomial functions of two variables. A complete characterisation of polynomial RET operators of degree less than 4 are presented.

FSN: Joint Entity and Relation Extraction Based on Filter Separator Network.

Joint entity and relation extraction methods have attracted an increasing amount of attention recently due to their capacity to extract relational triples from intricate texts. However, most of the existing methods ignore the association and difference between the Named Entity Recognition (NER) subtask features and the Relation Extraction (RE) subtask features, which leads to an imbalance in the interaction between these two subtasks. To solve the above problems, we propose a new joint entity and relation extraction method, FSN. It contains a Filter Separator Network (FSN) module that employs a two-direction LSTM to filter and separate the information contained in a sentence and merges similar features through a splicing operation, thus solving the problem of the interaction imbalance between subtasks. In order to better extract the local feature information for each subtask, we designed a Named Entity Recognition Generation (NERG) module and a Relation Extraction Generation (REG) module by adopting the design idea of the decoder in Transformer and average pooling operations to better capture the entity boundary information in the sentence and the entity pair boundary information for each relation in the relational triple, respectively. Additionally, we propose a dynamic loss function that dynamically adjusts the learning weights of each subtask in each epoch according to the proportionality between each subtask, thus narrowing down the difference between the ideal and realistic results. We thoroughly evaluated our model on the SciERC dataset and the ACE2005 dataset. The experimental results demonstrate that our model achieves satisfactory results compared to the baseline model.

Offline and online task allocation algorithms for multiple UAVs in wireless sensor networks

In recent years, UAV techniques are developing very fast, and UAVs are becoming more and more popular in both civilian and military fields. An important application of UAVs is rescue and disaster relief. In post-earthquake evaluation scenes where it is difficult or dangerous for human to reach, UAVs and sensors can form a wireless sensor network and collect environmental information. In such application scenarios, task allocation algorithms are important for UAVs to collect data efficiently. This paper firstly proposes an improved immune multi-agent algorithm for the offline task allocation stage. The proposed algorithm provides higher accuracy and convergence performance by improving the optimization operation. Then, this paper proposes an improved adaptive discrete cuckoo algorithm for the online task reallocation stage. By introducing adaptive step size transformation and appropriate local optimization operator, the speed of convergence is accelerated, making it suitable for real-time online task reallocation. Simulation results have proved the effectiveness of the proposed task allocation algorithms.

Analysis of Microphone Coupling and Zero-Point Calibration Model for Non-Resonant Photoacoustic Spectroscopy

Dissolved Gas Analysis (DGA) of transformer insulation oil is an effective method for monitoring transformer operating conditions and diagnosing faults. Photoacoustic spectroscopy (PAS) is widely employed for online dissolved gas analysis in transformer oil. The positioning of the pressure equalization hole within the sound field significantly impacts the microphone’s sensitivity when coupled with the photoacoustic cell in non-resonant photoacoustic spectroscopy. The static temperature and humidity characteristics inside the photoacoustic cell also have a notable influence on the microphone’s output signal. Consequently, an analysis is performed to explore the relationship between the sensitivity of the photoacoustic spectrometer and temperature/humidity, leading to the optimization of the system configuration based on the microphone parameters. Moreover, the photoacoustic zero-point signal is influenced by the temperature and humidity levels within the photoacoustic cell. To address this problem, air mixtures at different humilities are measured, resulting in the establishment of a zero-point model for the background signal in Photoacoustic Spectroscopy (PAS). Subsequently, the experimental detection of acetylene under various humidity mixtures is conducted, successfully extracting effective signals for the measurement of 5 ppm acetylene gas. These results serve as evidence showcasing the efficacy of both the proposed structural optimization and zero-point model put forth in this study.

Damage index based on the strain‐to‐displacement relation for health monitoring of railway bridges

AbstractThis paper proposes a novel damage index for railway bridges based on synchronous strain and displacement data collected at the passage of trains. The approach identifies a transformation operator that converts strains into displacements in a data‐driven fashion without prior structural knowledge and with no parameter selection. The displacement prediction error is proposed as a robust damage index, insensitive to the vehicle loads and temperature effects. Numerical simulations and data generated through the calibrated finite element model of a steel truss bridge showcase the effectiveness of the proposed approach.

Improvement of operation and maintenance efficiency of power transformers based on knowledge graphs

AbstractCurrently, the digital transformation of the power grid is underway, and the intelligent health management technology for power transformers is rapidly advancing. However, there are issues in the operation and maintenance process, such as weak information correlation and low decision‐making efficiency. Knowledge graphs have been applied in other industrial fields, such as spacecraft maintenance, to significantly improve knowledge query efficiency. However, there is a lack of literature on knowledge graph construction in the field of power transformer operation and maintenance. Additionally, there is limited publicly available data and difficulties in effectively mining operation and maintenance knowledge in this field. A method for constructing a knowledge graph for power transformer operation and maintenance based on ALBERT is proposed. Firstly, publicly available literature in the field of power transformers is collected, and a sample enhancement method using regular matching is used to enrich the semi‐structured corpora, such as power system accident investigation reports, to construct a training dataset for power transformer operation and maintenance. Then, the ALBERT‐BiLSTM‐CRF deep learning algorithm is applied to extract power transformer operation and maintenance entities from the relevant literature and accident investigation reports, and this method is compared with traditional deep learning algorithms to demonstrate its superiority. Subsequently, the ALBERT‐BiLSTM‐Attention deep learning algorithm, which incorporates ALBERT and attention mechanism, is utilised to extract relationships between power transformer operation and maintenance entities. Compared to other deep learning algorithms, this algorithm demonstrates better performance in the domain‐specific texts of power transformer operation and maintenance. Finally, the Neo4j graph database is used to visualise and present the knowledge graph, enabling decision support based on the power transformer operation and maintenance knowledge graph.

Estimating the age of power transformers using the concentration of furans in dielectric oil

Transformers have an insulation system based on dielectric oil and paper. Several factors as shortcircuits, high load, or overheating influence the aging of the insulating system. Thus, these materials are decomposed during transformer operation, and its state determines the status of the transformer. The degree of polymerization of dielectric paper is an indicator of its real condition. The concentration of furanic compounds in oil offer an estimation of paper degradation. Moreover, these compounds are easy to measure from oil samples. This paper review equations which relate one or more properties of the insulating system with the life expectancy of the transformer. These equations were used in a fleet of transformers in order to estimate the accuracy of these mathematical models. Results show that these methods achieve the most reliable estimation when the transformers have operated less than 8 years. Nevertheless, when the transformers are older, the estimation of lifetime based on furanic compounds is quite erratic and should be improved.

Optimal operation of paralleled power transformers

Parallel operation of power transformers is a common practice. Interest is placed on minimizing the reactive current circulation between transformers due to mismatching of electrical properties. Several control schemes have been applied over the years to ensure efficient operation of paralleled transformers with OLTC. These methods rely either on field measurements of circulating currents or on classic Master/Follower approach and usually assume that the paralleled transformers have matching impedances and OLTC positions. On the present work a generalized approach is implemented calculating the circulating current and minimizing losses directly at the System Operation Office and using the distribution system SCADA to control tap position of the paralleled transformers. This permits the determination of optimal tap matching between transformers and calculation of losses under emergency situations where coupling of transformers with slight impedance mismatch might be considered. In the proposed method a map of circulating current is calculated based on the transformer impedance, X/R ratio and OLTC positions, then the optimal tap positions (minimizing losses) under parallel operation are determined.

Impact of OLTC equipped transformer operation on PV installation in urban distribution network

Presented paper addresses the problem of proper large-scale integration of photovoltaic (PV) systems in urban area distribution network (DN). A methodology for minimization of annual energy losses in DN by optimal placement of PV systems, which simultaneously considers rooftop PV potential and time dependent network operation, is presented. Optimal reactive power generation of PV systems and optimal setting of on-load tap changer (OLTC) equipped transformer in secondary substation are determined as well. Furthermore, proposed methodology enables evaluation of the impact of OLTC equipped transformer on the share of PV installation that urban DN can accommodate. Proposed methodology is based on the optimization tool called differential evolution, with the objective function set to minimize network’s annual energy losses, while preventing the voltage violations and thermal overloading of lines. Results of the case study performed on a real urban low voltage distribution network, with consideration of different scenarios of OLTC operation are presented. The results indicate that simultaneous consideration of chosen variables yields greater benefits to DN operation in terms of reduction of annual energy losses and greater PV penetration.

An Efficient Noise Reduction Method for Power Transformer Voiceprint Detection Based on Poly-Phase Filtering and Complex Variational Modal Decomposition

The transformer is a core component in power systems, and its reliable operation is crucial for the safety and stability of the power grid. Transformer faults can be diagnosed early using acoustic signals. However, effective acoustic features are often affected by complex environmental noise, which reduces the accuracy of fault identification. As a solution, this study proposes a poly-phase filtering (PF)-based noise reduction algorithm for complex variational mode decomposition (CVMD) of multiple acoustic sources in power transformers. The algorithm dissects the received signal from the power transformer into subbands, downsizing their sampling rates via PF. Subsequently, it independently targets noise reduction within these subbands, focusing on specific acoustic sources. Leveraging complex signal transformations, we extend the variational mode decomposition (VMD) to mitigate the field of complex signals and utilize the CVMD to reduce the noise of each acoustic source within each subband for every acoustic source. The experimental results reveal that the proposed method effectively separates and denoises the sound signal of transformer operation under the interference of multiple sound sources in the substation. Its powerful noise reduction ability, combined with minimal computational complexity, greatly improves the accuracy of transformer fault identification and the reliability of the system.

Fuzzy fractional factors in fuzzy graphs-II

Abstract The fractional factor implicates the characteristics of fractional flow in network data transmission, and it is a crucial tool for analyzing network information transfer. When there is uncertain information in the network, the corresponding network graph should be characterized by fuzzy graphs, in which the vertex membership function (MF) describes the uncertainty of sites, and the edge membership reveals the uncertainty of channels. The previous work introduced the concept of fuzzy fractional factor (FFF) on fuzzy graphs, but the correlated concepts are still open on other fuzzy graph classes. In order to overcome this defect, in this contribution, the concept of fuzzy fractional factor is extended to intuitionistic fuzzy graph, Pythagorean fuzzy graph, and picture fuzzy graph. Sign-alternating walk and increasing walk are extended to the corresponding settings, and the transformation operations are redefined in light of various situations. By means of constructive approaches, the corresponding theoretical results are further generalized in these settings, which characterize the existence of (resp. maximum) fuzzy fractional factors in different kinds of fuzzy graphs.

A Consistent Augmented Stacking Polynomial Optimized Tool (ASPOT) for Improving Security of Cloud-IoT Systems

The cloud computing transforms information technology by offering end users simulated, flexible resources on demand that require fewer resources and facilities giving them greater flexibility. These materials are delivered over the Internet using predefined networking protocols, regulations, and styles, and they are overseen by various management groups. There are flaws and vulnerabilities in the underlying technology and legacy protocols that could allow an attacker to get access. A recent assessment of the literature led to the conclusion that most intelligence algorithms have a number of complex issues, including a high false prediction rate, difficulty classifying threats, high processing costs, and system load. Hence, the proposed work aims to develop an innovative and lightweight Augmented Stacking Polynomial Optimized Tool (ASPOT) for strengthening the cloud-IoT system security against modern cyberattacks with a accuracy of 99%. The current study uses the lightweight Deep Augmented Preprocessing Model (DAPM) to clean and normalize the input cyber-attack dataset by executing transformation and normalization operations on it. Furthermore, the Binary Sand Cat Swarm Optimization (BSCSO) technique is utilized to identify the most significant and relevant features of the normalized dataset in an optimal manner. Moreover, the class of assaults is promptly and precisely identified from the given data by applying the Deep Stacking Polynomial Learning Network (DSPLN) technology. The effectiveness and results of the proposed ASPOT method are analysed with the use of current cyber-attack statistics and a variety of assessment metrics.

A Novel Crossover based Discrete Artificial Algae Algorithm for Solving Traveling Salesman Problem

The Artificial Algae Algorithm (AAA) is a newly proposed metaheuristic algorithm that is inspired by microalgae behaviors. This algorithm has been proposed for solving continuous optimization problems and achieved good results for the continuous problems. In addition, binary versions of AAA are proposed in the literature. This paper presents a discrete version of AAA, which is named Discrete Artificial Algae Algorithm (DAAA). For discretization of AAA, Crossover operators (one-point and uniform) are used in the processes (helical movement, evolutionary process, and adaptation). In this study, in addition to crossover operators, transformation operators such as swapping, insertion, symmetry, and reversion are also used. DAAA’s performance was analyzed on a well-known discrete optimization problem called the Traveling Salesman Problem (TSP). DAAA was tested on thirty-two Benchmark instances of the TSP. These instances were small-sized, medium-sized, and large-sized. Firstly, the AAA processes (evolutionary process, adaptation, and helical movement) with the combination of nearest neighbor and transformation operators were tested for selected benchmark instances and this testing was called Process Analysis. After this process Analysis the best processes with which to continue were selected, and after this decision comparisons with other algorithms were started. The main comparison is between discrete Social Spider Algorithm (DSSA) and DAAA, and DAAA outperformed DSSA on most of the problems. Further, DAAA’s performance on some of the benchmark instances was compared with some of the well-known algorithms for TSP. In this comparison, DAAA has achieved better results than many other algorithms. Experimental results show that DAAA has the capability of solving discrete optimization problems and outperforming other algorithms.

A developed DQ control method for shunt active power filter to improve power quality in transformers.

Power transformers are the most important component in power system. Exposing these transformers to the harmonic distortions causes additional heat losses, insulation stress, decrease in lifetime of insulation, and reduced power factor with decrease in efficiency of the system. The lifespan of distribution transformers is influenced by the fragility of power quality in power networks. Harmonic mitigation filters with robust control technique are required to reduce the harmonic effects on power transformer. Traditionally, synchronous reference frame (DQ) is employed to control the shunt active power filter (APF) for mitigation of harmonics in power transformers. DQ control of Shunt APF lacks merits of fast response, delayed operation due to phased lock loop under abnormal grid conditions leading to insufficient harmonic elimination. A developed DQ method based on detecting the positive and negative sequence components is proposed to precisely control the shunt APF for reliable operation of power transformer. This detection technique improves the response time, mitigate the harmonics effecting the operation of transformer and overall power factor. The proposed control system is evaluated under different abnormal operating scenarios and compared with traditional DQ method. The results and analysis confirm the efficacy of the developed DQ method in improving the power transformer performance.

On Stokes-Lagrange and Stokes-Dirac representations for 1D distributed port-Hamiltonian systems

Port-Hamiltonian systems were recently extended to include implicitly defined energy and energy ports thanks to a (Stokes-)Lagrange subspace. Here, we study the equivalent port-Hamiltonian representations of two systems with damping, written using either a classical Hamiltonian or a Stokes-Lagrange subspace. Then, we study the Timoshenko beam and Euler-Bernoulli models, the latter being the flow-constrained version of the former, and show how they can be written using either a Stokes-Dirac or Stokes-Lagrange subspace related by a transformation operator. Finally, it is proven that these transformations commute with the flow-constraint projection operator.

Overvoltages at the incomplete phase modes of transformer operation in the networks of mining enterprises and methods of their limitation

The article presents analytical and graph-analytical methods of calculating overvoltages under incomplete-phase modes of power transformer operation with verification of their adequacy on mathematical models in Matlab Simulink software package. The existing solutions were analyzed for their efficiency in relation to mine networks. The overvolt-age levels at breakages in the most probable points of distribution network of one of the own open-pit mines of metal-lurgical enterprise have been determined. The circuit solutions for reducing the overvoltages on power transformers with different winding connection groups in incomplete phase modes of supply network operation have been developed and their mathematical models have been constructed. By means of Simulink package a number of calculations for deter-mining the necessary parameters of the proposed schemes of overvoltage limitation with graphical representation of the results have been made. Attention is paid to the unification of the study results for a wider and more flexible applica-tion.