Detection of Series Faults in High-Temperature Superconducting DC Power Cables Using Machine Learning

Choi, Jaehyun; Park, Chanyeop; Cheetham, Peter; Kim, Chul Han; Pamidi, Sastry; Graber, Lukas

doi:10.1109/tasc.2021.3055156

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…These modelling methods are known as white-box models. To have higher accuracy and lower computation time, AI techniques can be used to create black-box models (BBMs) [124,[194][195][196][197][198]. At the first step, these models are trained with the help of experimental and simulation input data.…”

Section: Faster Modelling Approaches Based On Data-driven Methods Usi...mentioning

confidence: 99%

High temperature superconducting cables and their performance against short circuit faults: current development, challenges, solutions, and future trends

Yazdani-Asrami

Seyyedbarzegar

Sadeghi

et al. 2022

Supercond. Sci. Technol.

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Along with advancements in superconducting technology, especially in high temperature superconductors (HTS), the use of these materials in power system applications is gaining outstanding attention. Due to the lower weight, higher current carrying capability, and the lower loss of HTS cables compared to conventional counterparts, they are among the most focused applications of superconductors in power systems. In near future, these cables will be installed as key elements not only in power systems but also in cryo-electrified transportation units, which take advantage of both cryogenics and superconducting technology simultaneously, e.g. hydrogen-powered aircraft. Given the sensitivity of the reliable and continuous performance of HTS cables, any failures, caused by faults, could be catastrophic, if they are not designed appropriately. Thus, fault analysis of superconducting cables is crucial for ensuring their safety, reliability, and stability, and also for characterising the behaviour of HTS cables under fault currents at the design stage. Many investigations were conducted on the fault characterisation and analysis of HTS cables in the last few years. This paper aims to provide a topical review on all of these conducted studies, It will discuss the current challenges of HTS cables and after that current developments of fault behaviour of HTS cables would be presented, and then we will discuss the future trends and future challenges of superconducting cables regarding their fault performance.

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Section: Faster Modelling Approaches Based On Data-driven Methods Usi...mentioning

confidence: 99%

High temperature superconducting cables and their performance against short circuit faults: current development, challenges, solutions, and future trends

Yazdani-Asrami

Seyyedbarzegar

Sadeghi

et al. 2022

Supercond. Sci. Technol.

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“…In [11], a real-time protective algorithm using symmetrical coordinate method and vector analysis during fault conditions, was investigated for protection of a triaxial HTS power cable. In [12] a method based on transmission characteristics is proposed for detection of series faults on DC SCs during which one or more conductors are damaged or disconnected, while a Machine Learning (ML) model was developed to predict the fault type. All the aforementioned protection schemes have enhanced the reliability of the SCs in power system applications.…”

Section: Introductionmentioning

confidence: 99%

Time-Domain Protection of Superconducting Cables Based on Artificial Intelligence Classifiers

et al. 2022

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Fault detection and protection of Superconducting Cables (SCs) is considered a challenging task due to the effects of the quenching phenomenon of High Temperature Superconducting (HTS) tapes and the prospective magnitude of fault currents in presence of highly-resistive faults and converter-interfaced generation. This paper presents a novel, time-domain method for discriminative detection of faults in a power system incorporating SCs and high penetration of renewable energy sources. The proposed algorithms utilises feature extraction tools based on Stationary Wavelet Transform (SWT), as well as artificial intelligence (AI) classifiers to discriminate between external and internal faults, and other network events. The performance of the proposed schemes has been validated in electromagnetic transient simulation environment using a verified model of SC. Simulation results revealed that the proposed algorithms can effectively and within short period of time discriminate internal faults occurring on SC, while remain stable to external faults and other disturbances. The suitability of the proposed algorithms for real-time implementation has been verified using software and hardware in the loop testing environment. To determine the best options for real-time deployment, two different artificial intelligence classifiers namely Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been deployed. The extensive assessment of their performance revealed that the ANN classifier is advantageous in term of prediction speed.

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“…In particular, ML-based methods have been studied for fault detection and diagnosis (FDD) in different fields with promising results. For instance, ML is used for fault detection in brushless synchronous generators in Rahnama et al (2019), in water distribution network (Quiñones-Grueiro et al, 2021), in age intelligence systems (Liu et al, 2021), and in high-temperature super conducting DC power cables (Choi et al, 2021). In Hajji et al (2021), several supervised ML algorithms are compared for FDD in photovoltaic systems.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis in refrigeration systems using machine learning algorithms

Soltani

Sørensen²,

Leth

et al. 2022

International Journal of Refrigeration

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Detection of Series Faults in High-Temperature Superconducting DC Power Cables Using Machine Learning

Cited by 9 publications

References 24 publications

High temperature superconducting cables and their performance against short circuit faults: current development, challenges, solutions, and future trends

High temperature superconducting cables and their performance against short circuit faults: current development, challenges, solutions, and future trends

Time-Domain Protection of Superconducting Cables Based on Artificial Intelligence Classifiers

Fault detection and diagnosis in refrigeration systems using machine learning algorithms

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