Abstract:High Voltage Direct Current (HVDC) transmission represents the most efficient way for transporting produced electrical energy from remotely located offshore wind farms to the shore. Such systems are implemented today using very expensive and large power transformers and converter stations placed on dedicated platforms. The present study aims at elaborating a compact solution for an energy collections system. The solution allows for a minimum of total transformer weight in the wind turbine nacelle reducing or e… Show more
“… In situations when the BPNN algorithm experiences a shortage of data, data augmentation is employed to improve the size and diversity of the data. Data augmentation involves making numerous transformations and adjustments to existing data, by creating modified copies of a dataset using existing data 125 , 126 . Lastly, data visualization is employed to further study and comprehend the data.…”
Section: Methods Used To Investigate Transformers In-servicementioning
Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.
“… In situations when the BPNN algorithm experiences a shortage of data, data augmentation is employed to improve the size and diversity of the data. Data augmentation involves making numerous transformations and adjustments to existing data, by creating modified copies of a dataset using existing data 125 , 126 . Lastly, data visualization is employed to further study and comprehend the data.…”
Section: Methods Used To Investigate Transformers In-servicementioning
Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.
“…In addition, the high temperature, high humidity and high heat environment of the ocean requires power transformers to operate reliably at high temperatures [6]. Insulation damage and overheating fault are the main factors leading to equipment damage [7] [8]. Therefore, it is of great significance to improve the operating temperature, enhance the insulation performance in salt fog environment and reduce the weight of the equipment for the application and promotion of dry-type transformers on offshore platforms.…”
Dry-type transformer is the key hub equipment connecting power
generation platform and power consumption platform in marine power
system. Partial insulation aging caused by transformer thermal effect is
one of the important factors that adversely affect the operation
stability. The new ceramic insulation winding prepared by micro-arc
oxidation technology has the characteristics of high thermal
conductivity and high temperature resistance, and is an ideal product to
replace traditional organic insulation materials. Therefore, in this
paper, the thermal characteristics and overload capacity of a ceramic
insulated aluminum winding dry-type transformer are studied by combining
heat flow coupling simulation and experiment. By comparing the
temperature field and velocity field characteristics of traditional
organic insulated dry-type transformer and ceramic insulated dry-type
transformer, the influence of different winding materials on transformer
heat dissipation under the same load condition is studied. The hottest
spot temperature of ceramic insulated winding dry-type transformer is
about 86% of that of traditional organic insulated transformer. The
ceramic insulated dry-type transformer has a good overload capacity.
Under the premise of meeting the H-class insulation, it can carry 1.4
times the rated load. Finally, the simulation results are compared with
the experimental data of the ceramic insulated dry-type transformer. The
accuracy of the results was verified.
“…Xiang [10] presented a circuit evolution of the high step-ratio transformer-coupled RMMC into its low step-ratio transformer-less RMMC counterpart. Kharezy [11] designed a compact solution for an energy collections system that allowed for a minimum of total transformer weight. It is obvious that all these studies on transformers made great contributions to structure, principle, and design methods to improve their performance.…”
For transformer enterprises, energy consumption and environmental pollution mainly occur in the manufacturing process. The conventional manufacturing mode does not conform to the current green manufacturing mode. The green manufacturing mode requires enterprises to improve the transformer production technology, the utilization rate of materials and equipment, and the production efficiency and to achieve clean production through energy conservation and consumption reduction. The main objective of this research is to schedule the blanking of multiple transformer cores together rather than the conventional calculation conducted one by one. An optimization model of the silicon steel coil blanking is established, an evaluation method for blanking schemes is proposed, algorithms to solve the optimization model are analyzed in detail, and the solving processes and results are compared through a case study. Compared with the current manual calculation, this paper is of practical significance for transformer manufacturers to improve the production efficiency, reduce material waste, meet the personalized market demand characterized by multiple varieties and small batches, and achieve the green manufacturing of transformers.
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