Voltage Sag Analysis Using Quantum Computing Based on Gram Matrix Method

Voltage sag is a critical power quality issue in modern power systems, causing operational disruptions and economic losses. Traditional detection methods, such as RMS voltage calculation and wavelet transforms, often face challenges like noise sensitivity, high computational complexity and limited ability to capture nonlinear signal relationships. The paper proposes Gram matrix based approach for voltage sag detection by using quantum inspired linear algebra and signal processing techniques. The gram matrix captures pairwise similarities between voltage signal samples in a high dimensional feature space, enabling robust and efficient sag detection. The proposed method involves dividing the voltage signal into overlapping windows, computing the RMS voltage for each window and constructing the Gram matrix to analyze the signal’s intrinsic structure. Eigen value decomposition of the gram matrix then performed, with significant drops in largest eigenvalue indicating voltage sags. The method is validated through simulations, demonstrating superior performance in terms of detection accuracy, noise robustness and computational efficiency compared to traditional techniques. The result analysis shows that an 17% increase in detection accuracy, a 9.064% reduction in false positives and 31% decrease in computational time. The model has been tested using Python programing under Qiskit environment.