Transformer Research Articles

Ternary particle composite synergistically enhances the fluidity and dielectric properties of filled thermal conductive epoxy resin

Abstract Particle-packed epoxy materials are widely used to improve the thermal conductivity of insulation materials. However, the addition of fillers increases the viscosity of the composite which reduces its operability, and is not conducive to the packaging of power electronic equipment such as electric transformers and reactors. Multi-scale particle compounding is one of the effective methods to enhance the co-processing performance of materials by reducing the friction between particles and epoxy matrix while forming an effective thermal conductivity network. Three types of spherical alumina sized around 4 μm, 38 μm, and 125 μm were used as fillers to study the fluidity, thermal conductivity, and dielectric properties of single-filled and ternary compounds. The results showed that the ternary particle composite reduced the viscosity of the precursor by up to 68.8%, achieving a synergistic improvement in operability, thermal conductivity, and electrical performance.

Optimization study on the configuration of electric hydrogen bidirectional coupling microgrid considering system flexibility margin

Abstract The solution to the global environmental concerns lies in the clean transformation of the energy system. However, the rapid expansion of renewable energy has increased the dangers of the power grid’s reliable functioning. It is vital to look into more controllable and flexible resources to increase the power grid’s capacity for regulation. Therefore, this paper provides an optimization model for a microgrid that couples electricity and hydrogen, which can improve the flexibility margin of the system significantly through bidirectional transformation and complementarity of electricity and hydrogen. The capacity configuration of each device in the system is solved to optimize the net profit of the system over the course of the system’s life cycle based on the forecast of the demand for wind and solar power generating output and load in the near future. A rolling scheduling simulation is run within a day to optimize the system capacity configuration with the goal of reducing daily operating expenses and maximizing flexibility margins. The results show that the electric-hydrogen coupled system can guarantee the absorption of 100% renewable energy of the traditional electric storage system, which improves the flexibility of the system by 410% and the benefit by 55.6%. This study offers recommendations and resources for the creation of flexible planning for the microgrid system with electro-hydrogen connection.

26‐1: Embedded a‐Si Photo‐Transistor Sensors Integration in Remote Optical Touch‐input Panel Using Four‐Mask Process Architecture Technology

An optical type touch‐input flat panel is designed. The driving system is based on the passive pixel sensor (PPS) array with amorphous silicon (a‐Si) TFT technology. The embedded sensing structure of the optical sensor is using the a‐Si TFT phototransistor and four‐mask process architecture technology. This paper presents a primary optical pixel sensor circuit that utilizes hydrogenated amorphous silicon photo‐transistor sensor. The a‐Si TFT phototransistor sensor is high reliability by using Multi‐N+ layer in the a‐Si TFT structure.

Evaluation of the Electrical Power Transformer Fins Design Technology: Numerical Analysis and Experimental Validation

The study includes numerical analysis and experimental verification on an electrical power distribution transformer (250 kVA, 11 kW, Oil Natural Air Natural). ANSYS Fluent R3 2019 software was used to develop the numerical simulation model. The validity of the numerical model was confirmed by comparing the results of the numerical model and experimental data. The study aims to improve the efficiency and performance of electrical power distribution transformers by proposing a design that reduces the temperature of the transformer while maintaining its traditional size. Numerically, the effect of fin geometry on the temperature and density of transformer oil was studied. Four different fin designs were proposed and compared with the traditional design. According to the results, all proposed designs contributed to improving the cooling performance of the transformer compared to the traditional design. Design A is similar to the traditional transformer design, with the only modification being manipulation of the fin length, and achieves an average oil temperature reduction of 4 K. Design B showed the smallest temperature drop of the four designs, with a 3 K drop. Designs C and D include ventilation channels that match the shape of the fin, providing distinct design differences. The difference between both designs relied on the fact that for design C, the orthogonally of fin plates was retained. On the other hand, in design D, skewing of fin plates was introduced. Design D proved to be the most effective in reducing the average oil temperature, being reduced by 10 K. On the other hand, Design C reduced the average oil temperature by 7 K.

Contribution to the determination of the effect of magnetic storms on the electric power transmission system

Abstract When a magnetic storm hits a power transmission system, quasi-stationary geomagnetically induced currents (GIC) are generated in the high-voltage part of the system. These currents cause semi-saturation of the magnetic circuits of power transformers, which induces current overload in their high-voltage windings and subsequently thermal overload, which can lead to system failures. This rather complex phenomenon was described in [11] by a system of nonlinear differential equations and subsequently solved. This very challenging method is replaced in the present work by a simple approach. It allows not only predicting the imminent danger of system collapse, but gives transformer designers valuable information on how they can counteract this danger.

Analytic Hierarchy Processed Grey Relational Fuzzy Approach for Health Assessment of Power Transformers

Analytic Hierarchy Processed Grey Relational Fuzzy Approach for Health Assessment of Power Transformers

Investigating the effects of corrosion parameters on the surface resistivity of transformer’s insulating paper using a two-level factorial design

The integrity of the insulation in oil-filled power transformers, shunt reactors, and high voltage bushings can be affected when copper dissolves in the insulating oil and then deposits onto the paper insulation. The presence of dissolved copper in the oil increases dielectric losses, while copper deposition significantly improves the conductivity of the paper insulation. Various factors, including temperature, oxygen, sulfur groups, passivators, and ageing time, have been found to contribute to the acceleration of corrosion activity in transformer insulating oils. Unfortunately, there is a lack of extensive research focused on systematically analysing and measuring the impact of corrosion-related factors on the dissolution of copper in transformer insulating oils and the deposition of copper onto solid insulation surfaces (Kraft paper). Therefore, this study aims to thoroughly examine the effects of corrosion factors on copper and sulfur deposition on Kraft paper insulation when it is submerged in transformer mineral oil (TMO). Using a two-level (2k) factorial design, we investigated three crucial factors: i) oil temperature, ii) elemental sulfur concentration, and iii) ageing time. It is worth mentioning that the results obtained from the two-level factorial design indicate that the surface resistivity is primarily affected by the temperature of the oil. This factor alone explains a significant 38.68% of the observed variation. In order to improve predictability, a regression model was created to estimate the surface resistivity of TMO-impregnated paper insulation. This model takes into account factors such as oil temperature, elemental sulfur concentration, and ageing time.

Analysis of thermoelectric conversion efficiency of tritium breeding blanket with He-CO2 mixture coolant in fusion reactors

The first wall of the tritium breeding blanket is an important high-heat flux component in fusion reactors, and its thermal analysis is critical. Helium is chosen as the coolant for the CFETR tritium breeding blanket due to its good compatibility with structural materials and excellent thermal properties. However, its low density requires a significant amount of drive power. After extensive feasibility research, using He-CO2 mixture as coolant is one of the main optimization directions. In this paper, a new method is proposed to consider the variation of thermal properties parameters of the He-CO2 mixture with temperature. A fitting formula for the He-CO2 mixture at different ratios under 12MPa is provided, and the mixture is simulated in the commercial CFD software Fluent by compiling a UDF program. The results show that the optimum He mole fraction in the He-CO2 mixture is 60%, which reduces the required drive power by 89.36% compared with using pure He as coolant and by 81.57% compared with using CO2.With further programming calculations and considering the effect of the secondary circuit, it is calculated that the highest energy conversion efficiency is 29.43% .This study provides valuable insights into the thermal-hydraulic performance of the tritium breeding blanket with a binary gas mixture coolant in fusion reactors.

Research on the construction method of virtual mapping digital twin system for substation

Abstract At present, there are problems in the process of fault location and cause analysis in the actual work of substations, such as long time consumption, high investment in manpower and material resources, and low returns. A Digital Twin (DT) system construction method that can be used for virtual mapping in substations is proposed to address this problem. Firstly, the design principles and overall structure of the substation DT system were formulated by comprehensively considering the needs of transient modeling, debugging, and verification calculations in the substation. Then, the construction method and process of the three-dimensional twin model of the primary system (PS) in the substation were analyzed by using SolidWorks and Unity3D, and the graphical modeling method and process of the general protection and secondary circuit logic modules were analyzed by using the Cybersim integrated simulation support system. Finally, a three-dimensional twin model of the PS and secondary system (SS) equipment inside the substation was constructed. The reliability of the proposed method was verified through simulation experiments, and the results showed that the fault identification accuracy, fault location accuracy, and fault cause judgment accuracy were the highest during bus faults, with 96.10%, 94.17%, and 92.93%, respectively. The capacitor had the lowest accuracy, with 94.86%, 92.96%, and 91.73%, respectively. The DT system can accurately achieve functions such as status viewing and alarm of the substation, precise positioning and navigation of equipment, and positioning of faulty and abnormal equipment.

High Accuracy Localization for Miniature Ingestible Devices Using Mutual Inductance.

This article demonstrates an inductively coupled high-accuracy localization system for miniature ingestible devices. It utilizes an inductance double capacitances-series capacitance (LCC-S) compensation architecture that enables mutual inductance measurement at primary side that is positioned outside the human body and less constrained by power budget and size than the miniature ingestible. Depending on the secondary circuit architecture, only limited and simple cooperative measurements are needed from the ingestible secondary side, which saves power and area in the miniature device. The errors in the system are modeled thoroughly, providing insights about system require-ments for a particular localization accuracy target for efficient design and to identify key building blocks with large influence on overall performance. The model shows that sub-centimeter localization root-mean-square error (RMSE) can be achieved with a modest external ADC (18bit) using three primary coils and three secondary coils. The localization is verified along a complete small intestine tract with realistic dimensions. The proposed model is verified by simulation and experiment showing that at the selected frequency range up to 5 MHz the body has no influence on the accuracy. The use of 0.9% saline as phantom is proposed which guarantees the analysis validity for all body types.

Design scheme research on key technologies of portable and efficient grinding equipment for high dose stop valves in the primary circuit of nuclear power plants

Design scheme research on key technologies of portable and efficient grinding equipment for high dose stop valves in the primary circuit of nuclear power plants

Response characteristics of PWR primary circuit under SBLOCAs considering steam bypass discharging

Response characteristics of PWR primary circuit under SBLOCAs considering steam bypass discharging

Multi-criterion analysis-based artificial intelligence system for condition monitoring of electrical transformers

Multi-criterion analysis-based artificial intelligence system for condition monitoring of electrical transformers

Long-term rolling prediction of transformer power load capacity based on the informer model

Abstract There are currently no methods capable of long-term forecasting based on very long-term real-world data. Any false prediction may damage the electrical transformer. For this problem, a transformer power load based on the Informer model is used as a method of long-term rolling forecasting. This method uses the self-attention distillation mechanism in the Informer model to allow the decoder of each layer to shorten the length of the input sequence by half, thus greatly saving the encoder memory overhead, and this paper adds a rolling long-term prediction function, making the prediction decoding time extremely short. Taking the power load data of a certain province in China as a test case, the improved model Informer* was compared with the traditional Informer model. The results show that the prediction accuracy of the Informer* model is higher and the load prediction accuracy is effectively improved.

IoT Based Transformer Monitoring System using ESP-32

Abstract: The integration of Internet of Things (IoT) technology with power transformer monitoring systems presents a significant advancement in electrical grid management. This paper explores the development of an IoT-based transformer monitoring system utilizing the ESP-32 microcontroller. The primary objective is to enhance the reliability and efficiency of transformers by providing real-time data on their operational status and health. The system leverages the ESP-32's capabilities in wireless communication and data processing to monitor critical parameters such as temperature, load current, voltage, and oil levels. Data is transmitted to a centralized cloud platform for analysis and visualization, enabling predictive maintenance and rapid response to potential failures. The proposed system promises to reduce downtime and maintenance costs while ensuring the longevity and optimal performance of transformers. This research highlights the design, implementation, and testing phases of the monitoring system, demonstrating its effectiveness through various case studies and field trials.

Advanced Machine Learning Techniques for Corrosion Rate Estimation and Prediction in Industrial Cooling Water Pipelines.

This paper presents the results of a study on data preprocessing and modeling for predicting corrosion in water pipelines of a steel industrial plant. The use case is a cooling circuit consisting of both direct and indirect cooling. In the direct cooling circuit, water comes into direct contact with the product, whereas in the indirect one, it does not. In this study, advanced machine learning techniques, such as extreme gradient boosting and deep neural networks, have been employed for two distinct applications. Firstly, a virtual sensor was created to estimate the corrosion rate based on influencing process variables, such as pH and temperature. Secondly, a predictive tool was designed to foresee the future evolution of the corrosion rate, considering past values of both influencing variables and the corrosion rate. The results show that the most suitable algorithm for the virtual sensor approach is the dense neural network, with MAPE values of (25 ± 4)% and (11 ± 4)% for the direct and indirect circuits, respectively. In contrast, different results are obtained for the two circuits when following the predictive tool approach. For the primary circuit, the convolutional neural network yields the best results, with MAPE = 4% on the testing set, whereas for the secondary circuit, the LSTM recurrent network shows the highest prediction accuracy, with MAPE = 9%. In general, models employing temporal windows have emerged as more suitable for corrosion prediction, with model performance significantly improving with a larger dataset.

Design and analysis of mode-matched decoupled mass MEMS gyroscope with improved thermal stability

This paper presents an efficient design approach for microelectromechanical systems (MEMS) gyroscope considering the design constraint of MEMSCAP’s Silicon-on-Insulator Multi-User MEMS Processes (SOIMUMPs). A design for a decoupled mass MEMS resonant gyroscope with tuning electrodes is presented to minimize cross-axis coupling and optimize the gyroscope’s performance. The device features a size of 1.8 ×2.772 mm2 . A decoupled mass MEMS gyroscope has been designed using a novel mechanical beam configuration that optimizes the structural design to reduce unwanted interactions between drive and sense axes motions. Mode order has been achieved by obtaining the first two modes as drive and sense modes at 9946.3 Hz and 10 202.7 Hz, respectively. Parallel plate electrostatic tuning electrodes have been added to adjust and match the drive and sense modes at the resonant frequencies of 9946 Hz. This mode matching is crucial for optimizing gyroscope performance, accuracy, and sensitivity. The effects of temperature variation on the performance of the designed decoupled mass MEMS gyroscope have been investigated, particularly in terms of its mode-matched operation achieved using tuning electrodes. Finite element method (FEM) simulations were conducted, demonstrating that thermal stability was achieved, and mode-matched operation was maintained within the temperature range of −40 °C to 80 °C. The gyroscope exhibits enhanced performance compared to existing MEMS gyroscopes under mode-matched conditions, featuring a lower temperature coefficient of frequency (TCF) at 0.0415 Hz °C−1 in the drive mode and 0.0165 Hz °C−1 in the sense mode. FEM analysis for the fabrication process tolerances has been done, where MEMS gyroscope’s resonant frequencies, output capacitance, output displacement, and sense mode quality factor have shown negligible changes to fabrication process tolerances. Transient analysis was conducted using CoventorWare MEMS+ 3D schematic, which was integrated with MATLAB Simulink to measure the MEMS gyroscope’s output response, which corresponded with the varying input angular velocity signal.

A first-principles study of the formation and regulation of the electric double layers at Cu (0 0 1)/mineral oil interfaces

Copper-mineral oil interfaces are key components of oil-impregnated power transformers and are commonly believed to be one of their weak points. The formation of an electric double layer (EDL) at this interface as a result of charge accumulation and transfer is crucial to its insulating properties, but a molecular-level understanding of this phenomenon remains unclear. To understand this fundamental aspect, we have investigated the effect of different EDLs on the electric potential and interfacial potential barrier between copper and mineral oil by using first principle calculations. Based on the calculations, the EDL is shown to reduce the interfacial potential barrier and enhance the diffusion of oil molecules at the interface when the copper side is negatively charged and the mineral oil side is positively charged. In contrast, when the copper side is positively charged and the mineral oil side is negatively charged, the corresponding EDL can increase the interfacial potential barrier and reduce the diffusion of oil molecules at this interface. Our findings shed light on the relationship between the structure of EDLs and their electrical properties in oil-impregnated power transformers.

Visualization display and exit fault diagnosis of secondary virtual real circuit in intelligent substation

Most communication in smart substations uses fiber optic connections to connect secondary equipment now. Due to the lack of intuitive connection between devices, it leads to difficulties in monitoring and relay protection. In order to solve the problems of visualizing, monitoring, and diagnosing secondary virtual circuits in the operation and maintenance of intelligent substations, a comprehensive monitoring technology system for secondary virtual circuits in intelligent substations is constructed. Firstly, a breadth first search algorithm is proposed to sort out the physical connection relationships between secondary devices, which achieves visual display of secondary virtual circuits. Then, based on deep search fault inference algorithms, it can diagnose and locate the fault area. Through the massive data source of intelligent substations, multiple information fusion can be achieved. By applying the D-S evidence theory, precise fault localization can be achieved. Finally, the proof table method is used to determine the type of fault and to meet the practical application requirements for the operation and maintenance of secondary equipment in intelligent substations.

Mathematical models of power transformers winding faults diagnosis based on voltage-current characteristics

Mathematical models of power transformers winding faults diagnosis based on voltage-current characteristics