| Title |
Analysis of Series Arc Detection Using PCA |
| Authors |
윤민호(Min-Ho Yoon) ; 박찬묵(Chan-Muk Park) ; 조유정(Yu-Jung Cho) ; 임성훈(Sung-Hun Lim) |
| DOI |
https://doi.org/10.5370/KIEE.2026.75.2.376 |
| Keywords |
series arc fault; Principal Component Analysis (PCA); time-domain features; anomaly score |
| Abstract |
The risk of series arc faults presents a growing safety concern, as they are inherently undetectable by conventional overcurrent circuit breakers. This paper proposes a real-time series-arc detection technique based on principal component analysis(PCA). From current signals sampled at 100[kHz], we extract nine time-domain features once per 60[Hz] cycle (mean, variance, skewness, kurtosis, maximum, minimum, interquartile range, RMS, and peak-to-peak). These features are z-score standardized using parameters derived from a baseline of normal operational data and then projected onto the top three principal components. We define the Q-statistic (PCA residual variance) as the anomaly score and declare a series arc when it exceeds a predefined threshold for three consecutive cycles. Experiments show that incorporating RMS current variation enables reliable discrimination between series arcs and inrush currents demonstrating robust performance suitable for practical deployment. |