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<p>This paper presents a statistical framework for early warning change-point detection in electrical grid frequency time series. Frequency deviations outside the tolerance band of 49.85–50.15&#xa0;Hz are treated as error events. A high-volatility (HV) measure is computed using a rolling-window approach and compared against a Hoeffding-bound threshold to identify significant transitions that may precede hazardous excursions. A dataset of 1250 error-event sequences collected over six months is divided into training (34%), validation (33%), and testing (33%) subsets. To improve efficiency, k-means clustering and dynamic time warping (DTW) are used to select representative training sequences, and a mapping-with-regression procedure is applied to generate warning signals. Experimental results show that the proposed method achieves 98.04% accuracy and an F1-score of 98.06%, while maintaining a false-negative rate of 1.1%. Lead-time evaluation confirms consistent early detection, and baseline comparison against deep learning approaches, demonstrates competitive performance with low computational cost.</p>

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Change-point detection and early warning systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

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  • Md. Shahidul Islam,
  • Rakibul Hossain,
  • Imran chowdhury

摘要

This paper presents a statistical framework for early warning change-point detection in electrical grid frequency time series. Frequency deviations outside the tolerance band of 49.85–50.15 Hz are treated as error events. A high-volatility (HV) measure is computed using a rolling-window approach and compared against a Hoeffding-bound threshold to identify significant transitions that may precede hazardous excursions. A dataset of 1250 error-event sequences collected over six months is divided into training (34%), validation (33%), and testing (33%) subsets. To improve efficiency, k-means clustering and dynamic time warping (DTW) are used to select representative training sequences, and a mapping-with-regression procedure is applied to generate warning signals. Experimental results show that the proposed method achieves 98.04% accuracy and an F1-score of 98.06%, while maintaining a false-negative rate of 1.1%. Lead-time evaluation confirms consistent early detection, and baseline comparison against deep learning approaches, demonstrates competitive performance with low computational cost.