Transformer Equipment Research Articles

Comparison Between Maintenance of Distribution Substations Using the Mobile Substation Unit (UGB) Method and Maintenance of Blackout Distribution Substations regarding Energy Not Served (ENS) which is not distributed to PLN UP3 Serpong

Electricity has become an important part of human life along with technological developments in this modern era. Economic progress, quality of life and the business world are very dependent on the electricity supply supplied by PT. PLN (Persero) UP3 Serpong. Electric power distribution is the final stage in the electric power delivery process. This is the process of distributing electric power from the electricity transmission system to electricity consumers. One of the main components in the electric power distribution system is the distribution substation. A distribution substation is an electrical installation device which contains disconnecting, connecting, safety and distribution transformer equipment to distribute electrical power according to consumer needs. Maintenance at distribution substations is carried out to support services to electricity consumers so that the substation equipment used is in reliable condition. and guaranteed continuity. Regular maintenance on distribution substations is important so that distribution substations do not experience damage and disruption while operating.

Experimental study on nonlinear development process of oil-filled equipment fire under external fire source

Abstract The transformer is the key oil-filled equipment in the power system, and its fire behavior seriously affects the safe operation of the power grid. In this paper, in order to analyze the fire development process and combustion behavior of oil-filled equipment, a mesoscale model of transformer equipment was constructed, and fire simulation experiments of transformer equipment under the action of external ignition sources were conducted. The flame temperature, flame height, heat release rate, oil temperature, and pressure were measured. The experimental results show that the oil-filled equipment fire presents the characteristics of nonlinear development. The fire can be divided into three stages: ignition stage, stable growth stage, and combustion mutation stage. The transformer oil near the wall is pyrolyzed by the external heat source, and the combustible gas and transformer oil form a gas-liquid two-phase flow, which is the main reason for the nonlinear development of oil-filled equipment fire. The experimental results are of great significance for the safe operation and fire control of power system oil-filled equipment.

Extrapolation of transformer equipment noise from excessive background noise environment

Dynamic, high relative background noise environments present challenging measurement conditions for discerning the noise level contributions for equipment of interest, particularly in circumstances where equipment cannot ideally be shut down for baseline measurement comparison. This paper considers the evaluation of large transformer noise emissions contributions in excessive background noise conditions for purposes of property line compliance assessment, exemplifying such an occurrence. While often insufficient alone, typical field measurements may be accompanied by fundamental, but powerful, narrowband signal analysis techniques to estimate and demonstrate equipment noise contributions, especially for sources containing prominent tonal components, such as those that are common to electrical equipment. This simple and straightforward approach focusing on calculation of average spectral densities and use of spectrograms, supplemented by other analysis tools including three-dimensional environmental noise modeling as was employed in this case study, offers an efficient and effective equipment noise assessment strategy that may be readily applied by the practitioner.

Transformer Fault Diagnosis Based on Intelligent Algorithm and Partial Discharge

Abstract As an important part of power transmission and transformation equipment and power systems, transformers’ operating conditions will have a direct impact on the stability and reliability of the power system. In view of the problems of high diagnosis cost and low accuracy of diagnosis results in existing fault diagnosis technology, this paper uses the pulse current method to detect the pulse signal generated by the discharge model based on the partial discharge experimental platform data, analyzes and studies the discharge characteristics of different discharge defects, and draws For the partial discharge phase distribution (PRPD) spectrum of each defect type, the statistical characteristic parameters characterizing the characteristics of the spectrum were extracted. The partial discharge statistical characteristics were used as classification feature quantities and input into three network models: RBF, GRNN, and PSO-GRNN. The results show that PSO-GRNN has a good recognition effect and is suitable for partial discharge type identification. It has important practical significance and application value for the all-round health management of power transformers.

Intelligent Diagnosis Method of Transformer Based on Oil Chromatographic Data

Abstract As one of the most important equipment in the operation of power system, ensuring the safe and stable operation of power transformer is a prerequisite for ensuring the normal supply of power grid. The failure of the power transformer will lead to the interruption of the power supply of the power grid and cause huge losses to the national economy. With the rapid development of China’s power grid scale and the continuous improvement of the intelligent construction of modern power system, the number of power equipment such as transformers is increasing. Therefore, it is necessary to make timely and accurate judgment on the status of key power transformation and distribution equipment in the power system, and grasp the operation of electrical equipment in real time, so as to ensure the reliability of power supply in the power system. This paper takes 220kV oil-immersed transformer as the research object. This paper considers the problems and characteristics of the current transformer fault diagnosis methods. By making full use of dissolved gas analysis (DGA) information in oil, a method for diagnosing faults in electrical transformers based on particle swarm optimization and generalized regression neural networks has been proposed. The simulation comparison experiment is carried out to select the most suitable transformer intelligent diagnosis method based on oil chromatographic data.

System for assessment and prediction of the technical condition of power oil-filled transformer equipment of distribution networks using machine learning

RELEVANCE the research is to develop a new system for assessing the technical condition of power oil-filled transformer equipment of distribution networks.OBJECT. To increase the accuracy of assessing the technical condition of power oil-filled transformer equipment (POTE) of distribution networks through the use of machine learning methods. Currently, an increase in the volume of analyzed information about the state of the management system of distribution networks leads to significant changes in the choice of data processing methods. The use of machine learning methods is associated both with the need to apply operational experience (in the form of expert assessments) and to obtain objective assessments of the condition of transformer equipment of distribution networks from instrumentation and sensors.METHODS. This work uses research methods such as mathematical modeling and the method of paired comparisons. As an example, we consider the oil-filled power transformer TMN-6300, its diagnostic parameters, external and operating parameters. The technical condition of the TMN-6300 transformer is assessed and a predictive model is created based on the existing monitoring system and machine learning methods, which make it possible to formalize expert knowledge and automate the process of data processing and analysis.RESULTS. A database has been created to assess and predict the technical condition of POTE of distribution network management systems. The algorithm for predicting the technical condition of POTE of the technical equipment in the form of an artificial neural network model was tested in the developed assessment system.CONCLUSION. The results of assessing and predicting the technical condition of POTE of the metering system of distribution networks obtained in this work prove the unconditional relationship between the parameters of the metering system and external, operating parameters. The data obtained as a result of modeling helps to increase the accuracy of forecasting the technical condition and determine the longterm prospects for the functioning of POTE the equipment management system, timely maintenance and repairs over the course of years and months.

Fluidization characteristics of ore particles for downstream hydrogen mineral phase transformation equipment

Fluidized roasting is an effective way to deal with refractory iron ores. Fluidization is a prerequisite for the effective occurrence of physical and chemical reactions. This article conducts cold state experiments on Single-layer Overflow type Reaction Chamber, using hematite ore and quartz as bulk particles，processing pressure drop signals to analyze and explain gas-solid motion. The results indicate that the gas flow average pressure drop being stable is the criterion for the fluidized bed, the bulk properties significantly affect the critical fluidization characteristics. Pressure drop pulsation is an important characteristic of fluidized bed, fluidized gas velocity, initial bed height, and bulk properties all affect the amplitude of bed pressure drop pulsation, and the spectrum analyses of bed pressure drop signals all exhibit “High-energy and Low-frequency”. This study has certain guiding significance for the structural design optimization and engineering scaling of Downstream Hydrogen Mineral Phase Transformation Equipment.

Transformer equipment fault detection based on multiple machine learning algorithms

Transformer is an indispensable equipment in the power system, and its normal operation plays a vital role in the stable operation of the power system and power quality. However, due to the complexity of the working environment of the transformer, its internal structure is also more complex, so the transformer failure frequency is higher. Therefore, timely detection and early warning of transformer failure is of great importance. In this paper, a machine learning algorithm is introduced to automatically discover the laws and patterns in the data by learning and analysing a large amount of data, and use these laws and patterns to make predictions and decisions. In this paper, the winding temperature indicator, oil temperature indicator alarm, oil temperature indicator trip and magnetic oil level indicator are taken as the target variables, and the current and voltage parameters are taken as the input variables, and the dataset is divided into training set, validation set and test set according to the ratio of 6:2: 2. The dataset is divided into training set, validation set and test set, and 12 kinds of machine learning models are introduced to predict the winding temperature indicator, the oil temperature indicator alarm, the oil temperature indicator trip, and the magnetic oil level indicator, respectively. indicator alarm, oil temperature indicator trip, and magnetic oil level indicator values, respectively, to observe the operating status of the transformer in real time by predicting the transformer state parameters. The results show that the machine learning model has high application value in transformer condition monitoring. Through the input of current and voltage parameters, the machine learning model can accurately predict and monitor the important indicators of the transformer, and discover the abnormal state of the transformer in time, so as to ensure the safe operation of the transformer. In the prediction of winding temperature, oil temperature and oil temperature indicator tripping, the prediction accuracy of the 12 machine learning models reaches more than 85%, and the prediction accuracy of some models even reaches 100%. For magnetic oil level prediction, the prediction accuracy of the 12 machine learning models is more than 90%. These results show that the machine learning models have high accuracy and stability and can provide reliable technical support for transformer fault detection. In summary, machine learning algorithms have high application value in transformer fault detection, which can automatically discover the laws and patterns in the data by learning and analysing a large amount of data, and use these laws and patterns to make predictions and decisions.

A digital twin model of the axial temperature field of a DC cable for millisecond calculations

The most important features of digital twin technology for transmission and transformation equipment are symbiotic evolution and state prediction. In constructing the digital twin model for DC cables, the transient and steady-state temperature fields need to be considered comprehensively in the modeling process to consider the multi-physical field coupling, mathematical model, operational data, and environmental conditions in specific cases, which makes it difficult to satisfy the real-time requirements of the digital twin. To solve this problem, in this paper, a digital twin technology architecture for DC cables is proposed, and a neural network learning model is constructed to minimize the latency due to the complexity of the model simulation. Moreover, an efficient digital twin model that is based on the model-driven approach is developed for the fast computation of the axial temperature field of DC cables. The use of the Sequence-to-Sequence residual neural network algorithm for the reduced-order multi-physics field coupling modeling reduces the computational time and ensures as much accuracy as possible up to the finite element model simulation. The simulation results show that the use of the digital twin model reduces the simulation time from 275 s to 0.56 s, and the relative error between the accuracy of the digital twin model and the finite element model for the axial temperature field of the DC cable is 0.46 %. Finally, verification of the reasonableness and feasibility of the proposed method is carried out via an experimental platform according to the standard of CIGRE TB496. In particular, the maximum value of the DC cable temperature is verified to have a small error compared with the result of the Sequence-to-Sequence calculation under specific working conditions.

External defect detection technology and application status of transmission and transformation equipment based on video images

Power transmission and distribution equipment plays a critical role in supplying electricity to consumers. However, these assets are susceptible to external defects, such as corrosion, mechanical damage, and wear, which can lead to failures and disruptions in the electrical grid. Traditional inspection methods for detecting these defects often rely on manual inspections, which are time-consuming, costly, and subjective. To overcome these limitations, this paper explores the current state of video image-based external defect detection techniques for power transmission and distribution equipment. This makes up for the deficiencies of conventional approaches to inspecting and maintaining power transmission and transformation equipment by decreasing the waste of human resources and increasing the frequency and efficiency of intelligent operation and maintenance of power systems. This work investigates a completely convolutional block detection-based defect identification method to address the issue of defect recognition. The fully convolutional neural network is enhanced with the concept of block detection thanks to this approach. The local discrimination mechanism may be realized, and the drawbacks of the conventional block detection receptive field are avoided in the process. This approach offers improved generalization and fault identification over the original ResNet image classification system.

Technical solutions to reduce losses in magnetic cores and material consumption of three-phase transformer and reactor equipment

Purpose. The increase in energy costs and the need for further energy saving lead to an increase in requirements for reducing losses in the magnetic cores of transformers and reactors. Problem. The improvement of transformer and reactor equipment is traditionally carried out by applying the achievements of electrical materials science and new technologies to traditional designs and structures of electromagnetic systems. The basis of modern transformers is made up of laminated and twisted magnetic cores. The disadvantage of laminated magnetic cores is large additional losses in corner zones due to the texture of anisotropic steel. Disadvantage of twisted three-phase three-contour magnetic cores is large additional losses caused by the lack of magnetic coupling of three separate magnetic flux contours. The disadvantages of combined joint tape-plate magnetic cores are the unsatisfactory use of the active volume and increased losses, which are determined by the uneven distribution of the magnetic field and the negative impact of steel texture in the corner zones of the twisted parts. Aim. To determine the possibility of improving three-phase transformers and reactors. Methodology. The improvement is achieved by geometrical and structural transformations of the outer contours and elements of the varieties of magnetic cores. Results. The possibility of eliminating additional losses of a planar laminated magnetic core by a combination of anisotropic and isotropic steels at the appropriate location in the yoke-rod and corner sections is determined. With an octagonal outer contour of the combined magnetic core, a reduction in mass is achieved without an increase in losses. The mutually orthogonal position of the steel layers or the elements of the joint twisted and combined three-phase planar and spatial magnetic cores achieves magnetic coupling and elimination of additional losses of individual twisted contour sections. The hexagonal configurations of the inner contours of the twisted yoke-corner parts and the cross-sections of the laminated rods of the variants of the axial spatial joint magnetic core improve the magnetic flux density distribution and reduce the main losses of the yokes, as well as reduce the complexity of manufacturing rods from identical rectangular steel layers. Originality. The paper presents constructive and technological proposals and features of varieties of non-traditional planar and spatial, laminated, twisted and combined tape-plate joint magnetic cores, which differ in the combination of anisotropic, isotropic and amorphous steels, as well as the multifaceted geometric shape of contours and the spatial arrangement of elements. Based on the identity of the optimal geometric ratios of the variants of electromagnetic systems of transformers and reactors, with joint planar and spatial twisted and combined and tape-plate magnetic cores, the unification of the structure of transformer and reactor equipment with a capacity of I-III dimensions.

Preface

With great and successful prestigious history of ICETAC, 2023 4th International Conference on Electrical Technology and Automatic Control (ICETAC 2023) was held in Chongqing, China during 24th–26th, November 2023 via virtual form. It was sponsored by School of Electrical Engineering, Chongqing University and organized by National Key Laboratory of Power Transmission and Transformation Equipment Technology.ICETAC 2023 is a great academic platform and is of great significance for disseminating cutting-edge information in disciplines and industry, promoting the transformation of scientific and technological achievements, and condensing academic achievements in the industry at this event.The Conference consisted of three plenary keynote speeches (addressed by Associate Professor Vandana Sharma from CHRIST University, India, Professor Xiaofang Yuan from Hunan University, China, and Associate Professor Qiushi Cui from Chongqing University, China, respectively), several oral and poster presentations, in which a wide range of topics were covered and the most recent significant results were presented. They discussed such topics as Unlocking the Potentials of AI in the domain of Automatic Control, Collaborative Operation Optimization of Processing Equipment and AGVs for Green Discrete Manufacturing, New Power System Modeling, Simulation and Testing Technology, etc. Besides, the attending scholars and experts exchanged deeply on the latest research achievements and advanced experience in the industry development and shared their unique insights into development trends and research directions of the industry.Some selected works presented at the Conference are published in this conference proceedings edition of Journal of Physics: Conference Series., which shows readers the latest research results and application cases of electrical and automatic control technology. We firmly believe that by reading these papers, readers will deepen their understanding of these fields and appreciate their enormous significance and potential impact on contemporary society.We wish to acknowledge all the participants, and the support of our sponsors. We would also like to take this opportunity to thank the members of Technical Program Committee and Organizing Committee, as well as the expert reviewers. What’s more, we hope that the conference series ICETAC shall continue to the next many sessions and deliver its purpose as the arena for presenting and exchanging the ideas of research in the fields of electrical and automatic control technology and anything related.The Committee of ICETAC 2023List of Committee Member is available in this pdf.

Research on a Partial Discharge Expert System for the Diagnosis of Damaged Transformation Equipment

Partial discharge (PD) characteristics are very important for the diagnosis of damaged transformation equipment. If the power transmission and transformation equipment fails, it will cause large economic losses, and thus prevention is better than treatment. One of the effective methods for high-voltage insulation degradation detection is to observe the phenomenon of partial discharge of equipment, which is the earliest characteristic. Prevention can be carried out in advance, and trend observation is better than periodic inspection. Long-term observation can effectively reduce the probability of misjudgment. This project intends to develop a high-speed data-acquisition device to acquire the original discharge waveform data of partial discharge for noise suppression. In order to improve the diagnostic efficiency and accuracy of the diagnostic system, it is necessary to suppress the noise of the measurement data, so as to carry out the fault identification of the discharge type. Through this method, the actual operating data of the system can be recorded as the original discharge waveform, in order to understand the original discharge waveform conditions of the power transmission and transformation equipment in multiple partial discharge measurements, and then after noise suppression, it can be regarded as a type of partial discharge identification.

Energy storage system coordinated with phase-shifting transformer and dynamic rating equipment for optimal operation of wind-rich smart power networks

Emergence of flexibility devices into smart power systems can assist the power system operators in making effective and economical decisions for the power system scheduling. These devices include energy storage system (ESS), phase-shifting transformer (PST), dynamic transformer rating (DTR), and dynamic line rating (DLR). In this paper, an approach is proposed for optimal day-ahead scheduling of power system using coordinated operation of ESS, PST, DTR, and DLR units under high wind power penetration situation. The aim is to minimize operational, load shedding, and wind power curtailment costs by regarding all the problem constraints. All of the model's relations as well as AC power flow equations have been convexified in order to construct a mixed-integer quadratically-constrained programming (MIQCP) model whose solution will obtain global optimum results. The developed model is implemented in GAMS and applied to the IEEE 24-bus system under various case studies, and the obtained results are investigated thoroughly. By coordinated operation of the employed flexibility devices, the loading of transformers and lines reach 159 % and 246 % compared to their nominal rating without violation of temperature limitations. Through releasing the capacity of lines and transformers, the total scheduling cost is reduced by about 52 % compared to traditional operation. Also, the whole energy of wind farms is utilized without any curtailment or constraints violation.

Prospects and Management Strategies for High Noise, Soft Geological and Strong Earthquake Environments

With global warming, environmental degradation is becoming increasingly severe, and the impact on power transmission and transformation equipment is increasing. The changing environment will also have cross impacts, and people are paying more attention to environmental protection. Therefore, under complex environmental conditions, research on new technologies continues, and the emergence of new technologies can improve efficiency and reduce costs. This article focuses on three typical environments: noise environment, soft geological environment, and seismic environment, with a focus on analysing their latest technological situation and management strategies. At the same time, it proposes future development directions, guides the emergence of new business models and service methods, and promotes industry innovation and change.

Energy-Saving Control Method for Factory Mushroom Room Air Conditioning Based on MPC

The energy consumption of the mushroom room air conditioning system accounts for 40% of the total energy consumption of the mushroom factory. Efficient and energy-efficient mushroom factories and mushroom houses are the development direction of the industry. Compared with maintenance structure transformation and air conditioning equipment upgrading, energy-saving technology based on regulation methods has the advantages of less investment and fast effectiveness, which has attracted attention. The current methods for regulating air conditioning in edible mushroom factories include simple on/off thermostat control or PID. In the field of energy efficiency in commercial building air conditioning, a large number of studies have shown that compared with traditional control algorithms such as classic on/off or PID control, model predictive control can significantly improve energy efficiency. However, there is little literature mentioning the application of MPC in factory mushroom production rooms. This paper proposes a data-driven MPC and PID combined energy-saving control method for mushroom room air conditioning. This method uses the CNN-GRU-Attention combination neural network as the prediction model, combined with prediction error compensation and dynamic update mechanism of the prediction model dataset, to achieve an accurate prediction of indoor temperature in mushroom houses. Establish an objective function for air conditioning control duration and temperature, use the non-dominated sorting genetic algorithm II (NSGA-II) to solve for the optimal control sequence of the air conditioning in the control time domain, and use the entropy weight method to determine the optimal decision quantity. Integrate rolling optimization, feedback mechanism, and PID to achieve precise and energy-saving control of the mushroom room environment. The experimental results show that compared with the on/off thermostat and PID controller, the designed controller reduces power consumption by 12% and 5%, respectively, and has good application and demonstration value in the field of industrial production of edible mushrooms.

Problems of safe operation of Zaporizka NPP in the conditions of war in Ukraine

Among the specified types of terrorist threats, the most dangerous today is the nuclear threat, which has moved from the realm of the probable to a real military-terrorist attack on the Zaporizhzhia NPP and its capture by the occupiers. Therefore, the issue of determining the level of nuclear and environmental safety of Ukraine's nuclear power plants in extreme war conditions, using the example of the situation that developed at the Zaporizhzhia Nuclear Power Plant (ZNPP), the largest in Europe, is extremely urgent, precisely because of the station's location in the war zone.During the period of the full-scale war, there have already been dozens of warheads hitting the objects of the critical infrastructure of the ZNPP, the external and emergency power supply of the ZNPP (transformer equipment, power supply systems to consumers, the premises of diesel generators, the industrial site of the station, and others). There have been corresponding emergency shutdowns of the power units of the ZNPP due to the blackout of the station and due to the forced disconnection of consumers as a result of the destruction of various objects in the power grid systems. These events cause great alarm and concern of the world public regarding the possibility of new global nuclear-ecological disasters due to nuclear-radiation accidents at the ZNPP. Many years of global experience in the operation of nuclear power reactor facilities (NPRF) and their safety regulation, as well as lessons from the largest nuclear and radiation accidents at NPPs, have revealed the insufficient validity of traditional probability indicators and safety criteria for NPPs, including the Zaporizhzhya plant. Probabilistic approaches to assessing the objective level of safety of the Zaporizhzhya NPP in extreme conditions of war are insufficiently substantiated, taking into account, among other things, the need to predict the possibility of the occurrence of unlikely emergency events and their combinations, which will have catastrophic consequences.Therefore, conducting an objective assessment of the level of nuclear and radiation safety at the ZNPP due to the station's location in the war zone, as well as substantiating practical recommendations for preventing nuclear and environmental disasters at the NPPs of Ukraine, is an extremely urgent issue for the entire nuclear energy industry of Ukraine.

Effect of sample temperature on laser-induced plasma of silicone rubber

Silicone rubber in power transmission and transformation equipment is subjected to considerable temperature changes under different application environment conditions and in different operational states. In tropical areas and the Turpan region of China, surface temperatures of silicone rubber insulators may reach or exceed 70°C. During in situ testing of silicone rubber, the spectral signal may fluctuate or even be distorted when the temperature changes, and consequently, the accuracy of the analysis may be affected. Therefore, we performed a LIBS-based investigation into the dependence of the spectral signal of rubber silicone on the sample temperature. Using high-temperature vulcanized silicone rubber as the experimental material, we determined the trends in spectral line intensity for different elements, plasma temperature, and electron density with temperature when the sample temperature was increased from 25°C to 310°C. The results indicated that the intensities of the Al I 394.40 nm, Al I 396.15 nm, and Si I 390.55 nm lines in the LIBS spectra underwent a gradual decrease as the temperature was increased, whereas the intensity of the Al I 309.27 nm spectral line was essentially stable. However, the spectral line intensity, plasma temperature, and electron density all exhibited a spike at approximately 260°C, which occurred because of the decomposition of aluminum hydroxide. The results of the present study should prove to be of significance in further increasing the accuracy of LIBS analysis as applied to silicone rubber surface monitoring in high-temperature environments.

Retracted: Operational Status Monitoring and Fault Diagnosis System of Transformer Equipment

Retracted: Operational Status Monitoring and Fault Diagnosis System of Transformer Equipment

Fault diagnosis model of main equipment in substation based on multi-mode time-frequency combination

The conventional fault diagnosis method of the main equipment in substations has strong limitations and the fault diagnosis accuracy is low, so this paper introduces the multi-mode time-frequency combination method and designs the fault diagnosis model of the main equipment in substations. First, the transmission mode of fault information of main transformer equipment is analyzed and the fault source is diagnosed. Secondly, the UHF partial discharge method is used to detect the partial discharge signal of the main substation equipment. On this basis, the main equipment fault diagnosis model of the substation is established based on the multi-mode time-frequency combination, and the equipment fault degree is obtained to complete the fault diagnosis. According to the experimental results, the proposed fault diagnosis model is highly feasible, and the accuracy of an insulation fault, overheating fault and discharge fault diagnosis is more than 98%.