Neural Architecture Search algorithm to optimize Deep Transformer model for Fault Detection in Electrical Power Distribution systems

DOI : https://doi.org/10.1016/j.engappai.2023.105890

This paper proposes a neural architecture search algorithm for obtaining an optimum Transformer model to detect and localize different power system faults and uncertain conditions, such as symmetrical shunt faults, unsymmetrical shunt faults, high-impedance faults, switching conditions (capacitor switching, load switching, transformer switching, DG switching and feeder switching), insulator leakage and transformer inrush current in a distribution system. The Transformer model was proposed to tackle the high memory consumption of the deep CNN attention models and the long-term dependency problem of the RNN attention models. There exist different types of attention mechanisms and feedforward networks for designing a Transformer architecture. Hand engineering of these layers can be inefficient and time-consuming. Therefore, this paper makes use of the Differential Architecture Search (DARTS) algorithm to automatically generate optimal Transformer architectures with less search time cost. The algorithm achieves this by making the search process differentiable to architecture hyperparameters thus making the network search process an end-to-end problem. The proposed model attempts to automatically detect faults in a bus using current measurements from distant monitoring points. The proposed fault analysis was conducted on the standard IEEE 14 bus distribution system and the VSB power line fault detection database. The proposed model was found to produce better performance on the test database when evaluated using F1-Score (99.4% for fault type classification and 97.7% for fault location classification), Matthews Correlation Coefficient (MCC) (99.3% for fault type classification and 97.6% for fault location classification), accuracy and Area Under the Curve (AUC). The architecture transferability of the proposed method was also studied using real-world power line data for fault detection.