TY - GEN
T1 - Quantum Kernel Based Transient Stability Assessment of Power Systems and Its Implementation in NISQ Environment
AU - Sabadra, Trisha
AU - Yu, Sijia
AU - Zhou, Yifan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Transient stability assessment (TSA) in power systems evaluates the system's ability to withstand and recover from disturbances, which can be formulated as a classification problem. This paper presents a quantum machine learning-based TSA approach that leverages quantum embedded kernels (QEKs) to map nonlinear power system features into a high-dimensional Hilbert space, where the data becomes linearly separable. By employing a quantum kernel function and Kernel Target Alignment (KTA), we optimize a variational quantum circuit for the stability classification of power systems. Through extensive experiments on both noise-free quantum simulators and noisy quantum environments, we demonstrated the accuracy and noise resilience of the developed algorithm.
AB - Transient stability assessment (TSA) in power systems evaluates the system's ability to withstand and recover from disturbances, which can be formulated as a classification problem. This paper presents a quantum machine learning-based TSA approach that leverages quantum embedded kernels (QEKs) to map nonlinear power system features into a high-dimensional Hilbert space, where the data becomes linearly separable. By employing a quantum kernel function and Kernel Target Alignment (KTA), we optimize a variational quantum circuit for the stability classification of power systems. Through extensive experiments on both noise-free quantum simulators and noisy quantum environments, we demonstrated the accuracy and noise resilience of the developed algorithm.
KW - Transient stability assessment
KW - kernel machines
KW - noisy intermediate-scale quantum (NISQ) algorithms
KW - quantum embedded kernels
KW - quantum machine learning
UR - https://www.scopus.com/pages/publications/85218046556
U2 - 10.1109/BigData62323.2024.10825774
DO - 10.1109/BigData62323.2024.10825774
M3 - Conference contribution
AN - SCOPUS:85218046556
T3 - Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
SP - 7402
EP - 7406
BT - Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
A2 - Ding, Wei
A2 - Lu, Chang-Tien
A2 - Wang, Fusheng
A2 - Di, Liping
A2 - Wu, Kesheng
A2 - Huan, Jun
A2 - Nambiar, Raghu
A2 - Li, Jundong
A2 - Ilievski, Filip
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Big Data, BigData 2024
Y2 - 15 December 2024 through 18 December 2024
ER -