Operation Of Transformer Research Articles

Altered microbial diversity, interaction pattern, and nitrogen transformation processes response to different iron addition amounts in constructed wetlands

Microbial interactions affect the biogeochemical cycles, pollutant transformation, and stable operation of constructed wetlands (CWs), and these interaction networks are vulnerable to environmental stress. Iron (Fe)-based CWs have been demonstrated as an intensified nitrogen (N) removal system, however, whether and how the addition of Fe, an exogenous environmental stressor, and its amount affect the microbial interactions and functions are poorly understood, which inevitably determine the stability and sustainability of CW system. Herein, we demonstrated that higher Fe addition could effectively improve the stability and sustainability of the CW system by regulating the diversity and interactions of the microbial communities rather than the relative abundance of functional genes. Specifically, low Fe-addition reduced the complexity and robustness of microbial network and the number of keystone taxa, while the high Fe-addition CWs showed the opposite pattern. In CWs with low Fe-addition, N-Fe cycles were accomplished by increasing competition represented by a high proportion of negative links between the same function, whereas increased cooperation was observed in CWs with high Fe-addition. Moreover, Fe addition shaped divergent N transformation processes, with denitrification-dissimilatory nitrate reduction to ammonium (DNRA) dominating in control group, and nitrification-partial denitrification/anammox (PD/A) dominating in Fe-based CWs, and the intensity of nitrification-PD/A increased with the addition amount of Fe. Our study provided insights into the potential ways to improve the stability and sustainability of Fe-CWs for intensified pollutant elimination, which would be valuable to advance the design, sustainable operation, and application of Fe-CWs.

Optimal operation of flexible interconnected distribution grids based on improved virtual synchronous control techniques

With distributed energy sources connected to the distribution grid on a large scale for distributed photovoltaic power randomness, this paper proposes a flexible interconnection system optimization operation strategy. First, the virtual synchronous control technology is improved to improve the DC bus voltage stability; second, it analyzes the system operation mode to judge the output logic of PV and storage units, takes DC bus power balance as the underlying logic, and puts forward the power coordination optimization strategy and fault power supply restoration strategy with full consideration of factors such as the load balance degree of the distribution station area, the economic operation of the main transformer, and the amount of power lost in the faulty station area. It also establishes a multi-objective optimization model to obtain the power commands of each port and achieves the power flexibility mutualization of the flexible interconnected system through the accurate regulation of the soft normally open point (SNOP). Finally, a simulation model of the flexible interconnection system is built using MATLAB/Simulink to verify the effectiveness of the proposed optimization strategy.

Numerical simulation and optimisation design for ventilation and heat dissipation in high-temperature and high-load indoor substations

Under high-temperature and high-load operational conditions, inadequate ventilation and suboptimal cooling arrangements within indoor substations result in high oil temperatures, posing a threat to the secure and steady operation of transformers. In this paper, the ventilation and heat dissipation effect of a 110 kV indoor substation is studied by the computational fluid dynamics method. Initially, the three-dimensional simulation model of the main transformer chamber is constructed to mirror the actual substation structure. Subsequently, the impact of the upper outlet on ventilation and heat dissipation is explored. The results show that the proximity of the upper outlet to the fan on the side wall prompts the fan to draw air from the upper outlet, diminishing the air volume entering the lower intake outlet by 38.8 %. It proves detrimental to the transformer's heat dissipation efficiency. Lastly, with the upper outlet closed, the study delves into the impact of various configurations of intake and exhaust ports on optimizing ventilation and heat dissipation. A total of six cases of two scenarios for the air inlet location and three exhaust vent positions are explored. According to the simulation results, the optimal case of substation ventilation and heat dissipation is obtained by considering four evaluation parameters.

Accurate fault section diagnosis of power systems with a binary adaptive quadratic interpolation learning differential evolution

Fault section diagnosis (FSD) is essential for ensuring the effective operation of power systems. To determine the faulty sections accurately, we proposed an improved binary adaptive quadratic interpolation learning differential evolution called BAQILDE for solving the FSD problem. By comparing the received warning data with the anticipated states of circuit breakers and protective relays, an analytical 0–1 integer programming function is established. To tackle the resultant function accurately, the population in BAQILDE is directly encoded in binary instead of floating-point to facilitate the solving convenience. Besides, three enhanced strategies including adaptive mutation operator, time-varying crossover rate, and dual transformation operator are developed to equilibrate the population diversity and convergence well to strengthen BAQILDE. To evaluate BAQILDE's performance, four test systems were used for verification, including 4-substation power system, IEEE 118 bus system, and two actual failures that occurred in Guangzhou and Jilin power grids, China. The results show that BAQILDE can diagnose various failures within 0.12 s with 100 % success rate and 0 diagnosis error, consuming an average of 32.21 function evaluation times. It outperformed other well-known peer algorithms in success rate, diagnosis error, robustness, convergence, and statistical analysis, which demonstrates its strong competitiveness in solving the FSD problem.

Research for transformer operation state prediction method based on BO-CNN-GRU

In the realm of electrical engineering, this study introduces a novel approach for forecasting transformer operational conditions by leveraging BO-CNN-GRU (Bayesian Optimized Convolutional Neural Network Gated Loop Unit). The initial step involves an in-depth analysis of the key parameters that significantly impact the operational performance of the transformer. Then, the comprehensive weights of each characteristic parameter of the transformer are obtained by the G1 method, entropy weight method, and CRITIC method, and the comprehensive state data of the transformer is obtained. Finally, BO (Bayesian optimization) and CNN (convolutional neural network) are used to optimize the GRU neural network to form a comprehensive prediction model of the future operation state for the transformer based on BO-CNN-GRU. Employing transformer operation status data, we develop a unified neural network model to forecast the forthcoming operation statuses of transformers. This model can more accurately and faster predict the future status of transformers. By analyzing specific cases, it has been determined that the predictive model’s average accuracy in forecasting transformer operational status one month ahead is at an impressive 98.44%, which can accurately predict the future state changes of transformers.

Zynq FPGA-Based Acceleration of Kernelized Correlation Filters via High-Level Synthesis of a Custom DFT Block

This study presents a hardware-software co-design implementation of an accelerator for the Kernelized Correlation Filter (KCF) tracking algorithm. Leveraging the High-level synthesis (HLS) and the Zynq heterogeneous platform, the KCF algorithm’s performance is enhanced by using a custom hardware implementation for the computationally intensive Discrete Fourier Transform (DFT) operation. Within this framework, a custom combined DFT and inverse DFT IP, named CDFT, is developed and optimized on the Programmable Logic (PL) side of the Xilinx ZCU102 FPGA, whereas the rest of the KCF algorithm is run with customized Petalinux build on the (Processing System) side. To assess real-world performance, a driver for the CDFT IP and a user application were created to measure metrics like Center Location Error (CLE), Intersection over Union (IoU), and Frame per Second (FPS). The designed DFT accelerator achieves a remarkable speedup of 21x compared to a software DFT implementation. At the algorithm level, the KCF accelerator obtains a 6x speed up with negligible precision loss. In comparison to prior studies employing exclusively hardware implementations, the proposed approach demonstrates a high accuracy at a moderate speed, while there exists potential for further optimizations to enhance its performance even further.

Block‐iterative schemes for the split common fixed point problem and applications

In this paper, based on the extrapolated Landweber‐type operators, we present new strongly convergent block‐iterative schemes for solving the split common fixed point problem (SCFPP) with demiclosed strongly quasi‐nonexpansive operators on Hilbert spaces. The strong convergence is proved without the additional assumptions such as the boundedly regular condition and the closedness property of the range of the transformation operator, assumed recently in the literature for the problem. A necessary and sufficient condition that ensures that a th iterate is a solution is given. An application of our results to solve the multiple‐sets split convex feasibility problem (MSSCFP) is showed with computational experiments for illustration.

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

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

Gas-Sensitive Performance Study of Metal (Au, Pd, Pt)/ZnO Heterojunction Gas Sensors for Dissolved Gases in Transformer Oil.

An oil-immersed transformer is a critical electrical device for power delivery. Online monitoring of transformer operation is the key to ensuring the regular operation of power systems. This paper proposes Au/ZnO, Pd/ZnO, and Pt/ZnO heterojunctions as new gas-sensitive materials and investigates their gas-sensitive performance to dissolved gases (C2H4, CO, and H2) in transformer oil. Upon theoretical density functional theory (DFT) calculations, the analysis of the total density of states (TDOS), partial density of states (PDOS), molecular orbital theory, and charge deformation density reveals that Au, Pd, and Pt form heterojunctions with ZnO, which enhance the electrical conductivity of the system. Meanwhile, intrinsic ZnO is unsuitable for gas detection and adsorption, while the Au/ZnO heterojunction suits C2H4 detection. In contrast, the Pd/ZnO heterojunction is suitable for H2 detection, and the Pt/ZnO heterojunction is suitable for C2H4 and CO detection. The electrical conductivity of the adsorption models is changed to varying degrees after gas adsorption. The different change rate electrical conductivity just serves as a theoretical foundation for determining the type and concentration of dissolved gases in transformer oil. The research results act as a theoretical foundation for constructing gas sensors with a ZnO-based material.

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

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

PLC Based Transformer Protection from Over Voltage and Under Voltage

A PLC (Programmable Logic Controller) based transformer protection system is an electronic system designed to protect industrial transformers from various electrical faults and abnormalities. The system utilizes sensors to detect electrical parameters such as voltage, current, and temperature and uses a PLC to process the data and control the transformer's operation. The protection system can be programmed to detect faults such as overload, overvoltage, undervoltage, phase loss, phase reversal, and over-temperature conditions. Once a fault is detected, the PLC will automatically shut down the transformer to prevent further damage. The system provides reliable and accurate protection to the transformer, reducing the risk of costly downtime and repairs. Transformer protection from over and under voltages is a critical aspect of any transformer control system. Overvoltage can damage the transformer's insulation, bearings, and winding, while undervoltage can cause the transformer to stall or overheat. To protect the transformer from these conditions, various protection devices can be employed, including overload relays, circuit breakers, and voltage monitors. To protect against overvoltage, a voltage monitor can be installed, which will detect any voltage spikes or surges and trigger an alarm or shut down the transformer if the voltage exceeds a predetermined threshold. Similarly, an undervoltage relay can be used to protect the transformer from low voltage conditions. The undervoltage relay will monitor the voltage levels and shut down the transformer if the voltage drops below a certain level.. Key Words:-PLC (Programmable Logic Controller), SMPS, Voltage Controlled Relay, LED indicators, Transformer and Dimmer.

TRANSFORMATION OPERATORS FOR THE PERTURBED HILL EQUATION WITH COMPLEX COEFFICIENTS

In this paper, we consider the perturbed Hill equation on the entire axis. Using a transformation operator that preserves asymptotics at infinity, triangular representations of special solutions of this equation are found. Estimates for the representation kernels are obtained.

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

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

Digital transformation and port operations: Optimal investment under incomplete information

Port digital transformation requires substantial investments. However, the benefits after transformation are difficult to accurately predict due to incomplete information available to ports. Balancing digital investments with benefits poses a key challenge for ports. We propose a game model based on revenue sharing between the port and the technology company to determine the digital level in equilibrium under incomplete information. Our study reveals that the incomplete information influences digital level of ports. When information is incomplete and ambiguous, the digital level of ports may be lower. However, significant ambiguous does not necessarily decrease the digital level due to risk pooling and agent cost effects. Additionally, revenue sharing reduces the incentive for technology company to withhold private information, and specific sharing ratios are identified. The study also finds that under incomplete information, digital level of port initially increases and then decreases with increasing optimism or the ratio of port's retained revenue. By considering these factors, our study provides guidance for ports in optimal investment strategies for digital transformation.

CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation

The Transformer architecture has gained widespread acceptance in image segmentation. However, it sacrifices local feature details and necessitates extensive data for training, posing challenges to its integration into computer-aided medical image segmentation. To address the above challenges, we introduce CCFNet, a collaborative cross-fusion network, which continuously fuses a CNN and Transformer interactively to exploit context dependencies. In particular, when integrating CNN features into Transformer, the correlations between local and global tokens are adaptively fused through collaborative self-attention fusion to minimize the semantic disparity between these two types of features. When integrating Transformer features into the CNN, it uses the spatial feature injector to reduce the spatial information gap between features due to the asymmetry of the extracted features. In addition, CCFNet implements the parallel operation of Transformer and the CNN and independently encodes hierarchical global and local representations when effectively aggregating different features, which can preserve global representations and local features. The experimental findings from two public medical image segmentation datasets reveal that our approach exhibits competitive performance in comparison to current state-of-the-art methods.

Hybrid Artificial Bee Colony Algorithm with Variable Neighborhood Search for Capacitated Vehicle Routing Problem

Aiming at the capacitated vehicle routing problem, a hybrid integer programming model with goal of lowest path cost is constructed, and a hybrid artificial bee colony algorithm with variable neighborhood search based on the model and the characteristics of the CVRP problem is proposed to solve the problem. The hybrid algorithm integrates the artificial bee colony algorithm and the variable neighborhood search algorithm, embeds a multi-variable neighborhood operator in the local search link of the artificial bee colony to carry out iteration. And the operator contains targeted transformation operations on path nodes, strings, and sub paths to ensure the diversity of the bee population. In addition, a variable neighborhood perturbation strategy is used to strengthen the algorithm's ability to escape from local optima. The comparative analysis of the literature study set and its algorithm solution shows that the designed hybrid variable neighborhood artificial bee colony algorithm has strong global search ability and high solution accuracy, especially in solution stability. HABC-VNS can obtain 47 optimal solutions in 74 examples. The average minimum deviation of the optimal solution is 0.34%, and the average deviation of the average CVRP set is 0.57%. The overall performance is better than the algorithm in the comparative literature.

The Degree of Big Data Technology Transformation and Green Operations in the Banking Sector

The Degree of Big Data Technology Transformation and Green Operations in the Banking Sector

Analyzing and mitigating parasitic capacitances in planar transformers for high-frequency operation

Planar magnetic components are compact and less susceptible to skin effect due to their thin copper layers. However, the increase in parasitic capacitance has been a challenge in high-frequency operation of planar transformers (PTs). Parasitic capacitances resonate with inductance and may damage switch devices. Thicker printed circuit board (PCB) and reconfigured windings are suggested in this paper to mitigate the parasitic capacitances. A detailed analysis of the parasitic capacitances is performed with a prototype PT. The resonant frequency increased from 1.27 to 1.63 MHz with a 0.4 mm thicker PCB. The reconfigured PT was 1.38 MHz, 0.3 MHz higher than the original PT.

A Transformer Heavy Overload Spatiotemporal Distribution Prediction Ensemble under Imbalanced and Nonlinear Data Scenarios

As a crucial component of power systems, distribution transformers are indispensable to ensure the sustainability of power supply. In addition, unhealthy transformers can lead to wasted energy and environmental pollution. Thus, accurate assessments and predictions of their health statuses have become a top priority. Unlike assumed ideal environments, however, some complex data distributions in practical scenarios lead to more difficulties in diagnosis. One challenge here is the potential imbalanced distribution of data factors since sparsely occurring factors along with some Unusual High-Risk (UHR) components, whose appearance may also damage transformer operations, can easily be neglected. Another is that the importance weight of data components is simply calculated according to their frequency or proportion, which may not always be reasonable in real nonlinear data scenes. With such motivations, this paper proposes a novel integrated method combining the Two-fold Conditional Connection Pattern Recognition (TCCPR) and Component Significance Diagnostic (CSD) models. Initially, the likely environmental factors that could result in distribution transformer heavy overloads were incorporated into an established comprehensive evaluation database. The TCCPR model included the UHR time series and factors that are associated with heavy overload in both spatial and temporal dimensions. The CSD model was constructed to calculate the risk impact weights of each risky component straightforwardly, in line with the total risk variation levels of the whole system caused by them. Finally, the results of one empirical case study demonstrated their adaptation capability and enhanced performance when applied in complex and imbalanced multi-source data scenes.

Mathematical Model of a Nonlinear Power Transformer for Needs of Relay Protection

In this work, a mathematical model of a three-phase nonlinear transformer is suggested. The model enables simulating the transformer operation with allowance for its nonlinearity and covers needs of the relay protection. Our model has been developed on the basis of a mathematical model with phase coordinates, where differential equations are composed by the Kirchhoff’s phase-voltage law. Based on this model, we first compose a mathematical model for simulating steady-state operation modes of a transformer, taking into account the asymmetry and nonlinearity of its ferromagnetic core. In this model, the initial values of inductances and mutual inductances of loops are determined from the main phase inductance calculated by the experimentally found no-load current, and their current values are determined from the currents in windings and the magnetic fluxes in legs of the transformer core. The magnetic fluxes are calculated by the nodal-pair method. This improved mathematical model is verified through a comparison between the calculated harmonic components of the phase currents and the experimental results. The harmonic components are calculated with the use of Fourier expansion of the calculated phase currents. Their experimental values are determined with a spectrum analyzer. The calculated and experimental harmonic components of the currents of phase A during no-load and rated-load operation of the transformer are tabulated. The comparison of these results shows that the mathematical model of a three-phase transformer we suggest makes it possible to simulate currents in transformer windings under steady-state operation modes with accuracy acceptable for relay protection.