An advanced quantum support vector machine for power quality disturbance detection and identification

• Published in: EPJ quantum technology Vol. 11; no. 1; p. 70
• Main Authors: Wang, Qing-Le, Jin, Yu, Li, Xin-Hao, Li, Yue, Li, Yuan-Cheng, Zhang, Ke-Jia, Liu, Hao, Cheng, Long
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Computation; Dimensional analysis; Electric power systems; Hilbert space; Mapping; Nanotechnology and Microengineering; Noise levels; Physics; Physics and Astronomy; Quantum Information Technology; Quantum Physics; Spintronics; Support vector machines; Quantum support vector machine; Power quality disturbance detection; Power quality disturbance; Quantum feature mapping; Power quality disturbance identification
• ISSN: 2662-4400; 2196-0763; 2196-0763
• DOI: 10.1140/epjqt/s40507-024-00283-5

Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of O ( N 2 log ( N ) ) , superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.