Abstract
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 utilizes 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.
Highlights
I N recent years, the deployment of multi-layer Superconducting Cables (SCs) with High Temperature Superconducting (HTS) tapes with inherent fault current limiting capability has been considered as a promising solution towards the modernization of power systems [1]–[3]
ACCURACY EVALUATION The Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers were tested using the 20% of the pre-simulated dataset of 415 cases, which contain all types of events
The high percentage value of True Positive (TP) predictions indicates the capability of the classifiers to classify correctly the internal faults, while the high percentage value of the True Negative (TN) predictions show that both classifiers can predict correctly the external faults and the load switch events, preventing protection operation for these disturbances
Summary
I N recent years, the deployment of multi-layer Superconducting Cables (SCs) with High Temperature Superconducting (HTS) tapes with inherent fault current limiting capability has been considered as a promising solution towards the modernization of power systems [1]–[3]. Several studies are reported in the literature which propose the combination of ML classifiers (e.g. ANN and SVM algorithms) with WT for fault detection and classification in distribution and transmission networks. The energy content of the detail coefficients form the training data set are fed the ANN classifier Another widely used ML algorithm for fault diagnosis in power systems is the SVM, which has been proven to be a powerful tool for classification problems. A hybrid method based on WT-SVM techniques is presented in [21] for fault detection and discrimination in microgrids, while in [22] the same approach is developed to discriminate highly resistive faults from other transient events (i.e loading conditions, capacitor switching and load switching) in distribution networks.
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