Research on Excitation Current Control System of the 50 kA Superconducting Transformer

Zhang, Shuqing; Liu, Huajun; Liu, Fang; Ma, Hongjun; Shi, Yi; Gao, Peng; Zhou, Chao; Qin, Jinggang

doi:10.1109/tasc.2021.3108756

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…As a result, a self-learned adaptive algorithm is available that adjusts the control parameters of the current loop. The structure of such a method is shown in figure 23 [184].…”

Section: Ai For Superconducting Machines and Transformersmentioning

confidence: 99%

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

Yazdani-Asrami

Sadeghi

Song

et al. 2022

Supercond. Sci. Technol.

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More than a century after the discovery of superconductors (SCs), numerous studies have been accomplished to take the advantage of SCs in physics, power engineering, quantum computing, electronics, communications, aviation, health care, and defence-related applications. However, there are still challenges that hinder the full-scale commercialization of SCs, such as the high cost of superconducting wires/tapes, technical issues related to AC losses, the structure of superconducting devices, the complexity and high cost of the cooling systems, the critical temperature, and manufacturing related issues. In the current century, massive advancements have been achieved on artificial intelligence (AI) techniques by offering disruptive solutions to handle engineering problems. Consequently, AI techniques can be implemented to tackle those challenges facing superconductivity and act as a shortcut towards full commercialization of SCs and their applications. AI approaches are capable of providing fast, efficient, and accurate solutions for the technical, manufacturing, and economic problems with a high level of complexity and nonlinearity in the field of superconductivity. In this paper, concept of AI and the widely used algorithms are first given. Then a critical topical review is presented for those conducted studies that used AI methods for improvement, design, condition monitoring, fault detection and location of superconducting apparatuses in large scale power applications, as well as the prediction of critical temperature and the structure of new SCs, and any other related applications. The topical review are presented in three main categories, AI for large-scale superconducting applications, AI for superconducting materials, and AI for physics of superconductors. In addition, the challenges to apply AI techniques to the superconductivity and its applications are given. Eventually, future trends on how to integrate AI techniques with superconductivity towards the commercialization are discussed.

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“…As a result, a self-learned adaptive algorithm is available that adjusts the control parameters of the current loop. The structure of such a method is shown in figure 23 [184].…”

Section: Ai For Superconducting Machines and Transformersmentioning

confidence: 99%

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

Yazdani-Asrami

Sadeghi

Song

et al. 2022

Supercond. Sci. Technol.

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“…To solve the problem of conductor current attenuation in superconducting transformers affected by joint resistance, Zhang et al proposed an adaptive control strategy based on learning rate radial basis functions and neural networks. The results showed that the overall system deviation under this method was less than 0.2% [11]. In response to the current difficulty of electronic transformers performing well in off calibration in power grid control systems, Hu et al scholars proposed a transformer design with a passive compensation loop to reduce phase error by calculating the loop resistance and capacitance.…”

Section: Related Workmentioning

confidence: 99%

Influence of electromagnetic transient physical models considering characterization characteristics on capacitive voltage transformers

Geng,

Yang,

et al. 2024

Archives of Electrical Engineering

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The equivalent circuit of traditional capacitive voltage transformers often faces the problem of complex data calculation and difficulty in grasping the internal nonlinear characteristics of transformers when constructing broadband models, resulting in poor power accuracy and stability. Therefore, with the help of electromagnetic transient physical models, the admittance sub model and nonlinear model are established by considering the frequency and saturation characteristics of the transformer. Based on the characteristics of capacitive voltage transformers, the model is processed in parallel to obtain a broadband coupling transformer model. The results showed that the error between the simulated current peak amplitude and voltage results of the model and the measured values was less than 2% and 1%, respectively. In the fault results, the harmonic error of the load voltage of the improved transformer was relatively small, far less than the error result of 2.73% of the traditional transformer. The proposed transformer model can better characterize its characteristics and has good transient response ability, providing reference tools and value for the operation and state detection of power systems.

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“…In the context of employing a superconducting transformer to furnish the requisite high current for measurement purposes, the accurate regulation of the secondary current generated by the superconducting transformer emerges as a critical consideration. Furthermore, the precise measurement of the secondary current produced by the superconducting transformer constitutes an essential challenge that demands careful attention [12].…”

Section: Introductionmentioning

confidence: 99%

Development of a cryogenic DC transformer using for the measurement of large current

Zhang,

Liu

et al. 2024

J. Inst.

Self Cite

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Superconducting transformer is a typical facility used for inducing ultra-high current for large-scale scientific measurement systems, such as SULTAN-facility at the Centre de Recherches en Physique des Plasma (CRPP) in Switzerland, FRESCA-facility at CERN in Switzerland etc. A new program has been launched at the Institute of Plasma Physics, Chinese Academy of Sciences (ASIPP) to build a new conductor test facility to investigate the transverse pressure dependence of critical current of various superconducting cables using a compact superconducting transformer and cryogenic electromagnetic press systems. The superconducting transformer can induce a secondary current up to 60 kA, at a background magnetic field up to 15 T at 4.2 K. To measure the induced secondary current at cryogenic temperature precisely, three types of NbTi superconducting coils are designed based on the zero-flux principle to characterize the variation of the measured current. In this paper, the design principle of the cryogenic DC transformer and its commissioning results in a preliminary prototype test device are presented. The measurement results show achievements on accuracy of ±0.6% or less and nonlinearity of better than ±0.25%, which demonstrates the feasibility of the cryogenic DC transformer and provides a reliable technology for the secondary current measurement of a 100 kA superconducting transformer used in the program of Super-X test facility at ASIPP.

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Research on Excitation Current Control System of the 50 kA Superconducting Transformer

Cited by 6 publications

References 7 publications

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

Influence of electromagnetic transient physical models considering characterization characteristics on capacitive voltage transformers

Development of a cryogenic DC transformer using for the measurement of large current

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