scholarly journals Thermal optimization research of oil-immersed transformer winding based on the support machine response surface

2021 ◽  
pp. 264-264
Author(s):  
Fating Yuan ◽  
Wentao Yang ◽  
Bo Tang ◽  
Yue Wang ◽  
Fa Jiang ◽  
...  
Keyword(s):  
Response Surface ◽  
Low Voltage ◽  
Hot Spot ◽  
Optimization Method ◽  
Design Parameters ◽  
Support Vector ◽  
Structure Parameters ◽  
Transformer Winding ◽  
Polynomial Response Surface ◽  
Spot Temperature

In this paper, the CFD (computational fluid dynamics) model is established for the low voltage winding region of an oil-immersed transformer according to the design parameters, and the detailed temperature distribution within the region is obtained by numerical simulation. On this basis, the RSM (response surface methodology) is adopted to optimize the structure parameters with the purpose of minimizing the hot spot temperature. After a sequence of designed experiments, the second-order polynomial response surface and the SVM (support vector machine) response surface are established respectively. The analysis of their errors shows that the SVM response surface can be better used to fit the approximation. Finally, the PSO (particle swarm optimization) algorithm is employed to get the optimal structure parameters of the winding based on the SVM response surface. The results show that the optimization method can significantly reduce the hot spot temperature of the winding, which provides a guiding direction for the optimal design of the winding structure of transformers.

scholarly journals Study on the Method of Calculating Temperature Rise of Transformer

2020 ◽  
Vol 179 ◽  
pp. 01027
Author(s):  
Tao Li ◽  
Xiaoping Du ◽  
Xuewu Sun ◽  
Yuanyuan Song
Keyword(s):  
Temperature Rise ◽  
Thermal State ◽  
Hot Spot ◽  
Scientific Basis ◽  
Internal Temperature ◽  
Insulation Aging ◽  
The Neural Network ◽  
Forecasting Method ◽  
Transformer Winding ◽  
Spot Temperature

The internal temperature of the transformer is a key parameter to measure the thermal state of the transformer. The service life of the transformer generally depends on the life of the insulating material, and high temperature is the main reason why cause insulation aging, this paper studies the temperature rise of transformer winding hot spot temperature for the key, using the neural network forecasting method, forecasts transformer winding hot spot temperature change rule, calculate the transformer internal temperature rise, provide the temperature of the scientific basis for the safe operation of the transformer.

Stochastic Optimization of Nonlinear Energy Sinks Using Resonance-Based Clustering

2016 ◽  
Author(s):  
Ethan Boroson ◽  
Samy Missoum
Keyword(s):  
Stochastic Optimization ◽  
Adaptive Sampling ◽  
Dimensional Space ◽  
Optimization Method ◽  
Expected Value ◽  
Design Parameters ◽  
Support Vector ◽  
Design Variables ◽  
Energy Dissipated ◽  
Nonlinear Energy

Nonlinear energy sinks (NESs) are promising devices for achieving passive vibration mitigation. Unlike traditional tuned mass dampers (TMDs), NESs, characterized by nonlinear stiffness properties, are not tuned to specific frequencies and absorb energy over a wider range of frequencies. NES efficiency is achieved through time-limited resonances, leading to the capture and dissipation of energy. However, the efficiency with which a NES dissipates energy is highly dependent on design parameters and loading conditions. In fact, it has been shown that a NES can exhibit a near-discontinuous efficiency. Thus, NES optimal design must account for uncertainty. The premise of the stochastic optimization method proposed is the segregation of efficiency regions separated by discontinuities in potentially high dimensional space. Clustering, support vector machine classification, and dedicated adaptive sampling constitute the basic techniques for maximizing the expected value of NES efficiency. Previous works depended solely on the ratio of energy dissipated by the NES for clustering. This work also includes information about the type of m:p resonances present. Three examples of optimization for the maximization of the expected value of efficiency for NESs subjected to transient loading are presented. The optimization accounts for both design variables with uncertainty and aleatory variables to characterize loading.

Direct Measurement of Transformer Winding Hot Spot Temperature

1984 ◽  
Vol PER-4 (6) ◽  
pp. 26-27 ◽  
Author(s):  
W. J. McNutt ◽  
J. C. McIver ◽  
G. E. Leibinger ◽  
D. J. Fallon ◽  
K. A. Wickersheim
Keyword(s):  
Direct Measurement ◽  
Hot Spot ◽  
Transformer Winding ◽  
Spot Temperature

scholarly journals Effect of Loads on Temperature Distribution Characteristics of Oil-Immersed Transformer Winding

2021 ◽  
Author(s):  
Zhengang Zhao ◽  
Zhangnan Jiang ◽  
Yang Li ◽  
Chuan Li ◽  
Dacheng Zhang
Keyword(s):  
Temperature Distribution ◽  
Hot Spots ◽  
Hot Spot ◽  
Transformer Oil ◽  
Distribution Characteristics ◽  
Thermal Circuit ◽  
Transformer Winding ◽  
Overload Capacity ◽  
The Impact ◽  
Spot Temperature

The temperature of the hot-spots on windings is a crucial factor that can limit the overload capacity of the transformer. Few studies consider the impact of the load on the hot-spot when studying the hot-spot temperature and its location. In this paper, a thermal circuit model based on the thermoelectric analogy method is built to simulate the transformer winding and transformer oil temperature distribution. The hot-spot temperature and its location under different loads are qualitatively analyzed, and the hot-spot location is analyzed and compared to the experimental results. The results show that the hot-spot position on the winding under the rated power appears at 85.88% of the winding height, and the hot-spot position of the winding moves down by 5% in turn at 1.3, 1.48, and 1.73 times the rated power respectively.

Thermal Model of Disk Coil With Directed Oil Flow

2008 ◽  
Author(s):  
Shahram Khalil Aria ◽  
Sahar Samsami
Keyword(s):  
Mathematical Model ◽  
Optimal Design ◽  
Temperature Rise ◽  
Thermal Model ◽  
Hot Spot ◽  
Exact Location ◽  
Oil Flow ◽  
Transformer Winding ◽  
Spot Temperature

In this paper, a developed mathematical model for temperature rise calculation is briefly described. In this model, at first, load loss of a transformer winding with forced directed oil is calculated and the winding temperature rise along the horizontal ducts and vertical ducts is computed. Then hot spot temperature and its exact location is determined. The model can also be used for optimal design of winding in size and cooling. Finally the results are given and compared with experiment values.

Research of Temperature Field in Oil-Immersed Transformer Considering the Oil Speed

2014 ◽  
Vol 912-914 ◽  
pp. 1041-1045
Author(s):  
Guo Liang Yue ◽  
Yong Qiang Wang ◽  
Jie He ◽  
Hong Liang Liu
Keyword(s):  
Temperature Field ◽  
Hot Spot ◽  
Element Model ◽  
Thermal Coupling ◽  
Normal Range ◽  
Steady State Temperature ◽  
Oil Flow ◽  
Transformer Winding ◽  
The Mathematical Model ◽  
Spot Temperature

In this paper, we have Elaborated the mathematical model of temperature field and flow field of the oil-immersed transformer, and analysis its structure of thermal .We established a temperature finite element model of an oil-immersed transformer using the method of flow-solid-thermal coupling. Using the software of ANSYS, simulating on a 250MVA oil-immersed transformer, we obtain the steady-state temperature distribution and the winding hottest locations. Analyze the effect of oil-speed to the temperature field and location of the hot spot temperature of oil-immersed transformer. The results show that when oil flow rate is increases in the normal range, Transformer temperature rise corresponding slowly, and its location hottest temperature slightly pulled accordingly. The fiber measure different speeds Oil immersed transformer winding hot spot temperature to provide a basis for positioning.

scholarly journals Multi-Objective Optimization Design of Permanent Magnet Torque Motor

2021 ◽  
Vol 12 (3) ◽  
pp. 131
Author(s):  
Jiawei Chai ◽  
Tianyi Zhao ◽  
Xianguo Gui
Keyword(s):  
Permanent Magnet ◽  
Optimization Method ◽  
Torque Ripple ◽  
Numerical Control ◽  
Design Parameters ◽  
Support Vector ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Output Torque ◽  
Low Torque

Permanent magnet torque motor (PMTM) is widely used in aerospace, computer numerical control (CNC) machine tools, and industrial robots with many advantages such as high torque density, strong overload capacity, and low torque ripple. With the upgrading of industrial manufacturing, the requirements for the performance of torque motors have become more stringent. At present, how to achieve high output torque and low torque ripple has become a research hotspot of torque motors. In the optimization process, it is necessary to increase the output torque while the torque ripple can be reduced, and it is difficult to get a good result with the single-objective optimization. In this paper, a multi-objective optimization method based on the combination of design parameter stratification and support vector machine (SVM) is proposed. By analyzing the causes of torque ripple, the output torque, efficiency, cogging torque, and total harmonic distortion (THD) of back electromotive force (EMF) are selected as the optimization objectives. In order to solve the coupling problem between the motor parameters, the calculation formula of Pearson correlation coefficient is used to analyze the relationship between the design parameters and the optimization objectives, and the design parameters are layered ac-cording to the sensitivity. In order to shorten the optimization cycle of the motor, SVM is used as a fitting method of the mathematical model. The performance between initial and optimal motors is compared, and it can be found that the optimized motor has a higher torque and lower torque ripple. The simulation results verify the effectiveness of the proposed optimization method.

Simulation Based Optimal Tolerancing for Multibody Systems

2007 ◽  
Author(s):  
Henry Arenbeck ◽  
Samy Missoum ◽  
Anirban Basudhar ◽  
Parviz E. Nikravesh
Keyword(s):  
Multibody Systems ◽  
Multibody System ◽  
Failure Detection ◽  
Optimization Method ◽  
Computational Effort ◽  
Efficient Estimation ◽  
Design Parameters ◽  
Support Vector ◽  
Manufacturing Cost ◽  
Analysis Scheme

This paper introduces a new methodology for probabilistic optimal design of multibody systems. Specifically, the effects of dimensional uncertainties on the behavior of a system are considered. The proposed reliability-based optimization method addresses difficulties such as high computational effort and non-smoothness of the system’s responses, for example, as a result of contact events. The approach is based on decomposition of the design space into regions, corresponding to either acceptable or non-acceptable system performance. The boundaries of these regions are defined using Support Vector Machines (SVMs), which are explicit in terms of the design parameters. A SVM can be trained based on a limited number of samples, obtained from a design of experiments, and allows a very efficient estimation of probability of failure, even when Monte Carlo Simulation (MCS) is used. A modularly structured tolerance analysis scheme for automatic estimation of system production cost and probability of system failure is presented. In this scheme, detection of failure is based on multibody system simulation, yielding high computational demand. A SVM-based replication of the failure detection process is derived, which ultimately allows for automatic optimization of tolerance assignments. A simple multibody system, whose performance usually shows high tolerance sensitivity, is chosen as an exemplary system for illustration of the proposed approach. The system is optimally designed for minimum manufacturing cost while satisfying a target performance level with a given probability.

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