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<p>This paper proposes a novel hybrid ensemble machine learning framework integrated with Explainable AI (XAI) to overcome the fundamental trade-off between non-detection zones (NDZ) and false positives in conventional islanding detection methods. The approach introduces a cascaded dual-stage architecture: first, an Isolation Forest algorithm serves as a high-sensitivity anomaly trigger to minimize the NDZ by detecting nearly all operational deviations; second, an optimized multi-class XGBoost classifier, activated only upon an anomaly, precisely discriminates islanding events from other disturbances like faults and switching operations. Validated on a comprehensive public dataset, the framework demonstrated a high islanding detection rate of 96.0% while reducing false positives to a near-zero rate of 2.7% through adaptive confidence thresholding. Integrated SHAP-based interpretation provides operational transparency by quantifying feature contributions to each decision. This work introduces a novel cascaded framework that uniquely integrates an unsupervised anomaly detector (Isolation Forest) with a multi-class XGBoost classifier and SHAP-based explainability for islanding protection. Unlike prior hybrid methods, the proposed architecture explicitly decouples sensitivity near-zero NDZ and specificity (near-zero false positives) while providing full decision transparency, offering a more reliable and interpretable solution compared to conventional single-model approaches.</p>

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An ensemble hybrid and explainable AI (XAI) framework for zero false-positive islanding detection in distribution networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月13日

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  • Samiksha K. Shahade,
  • Anjali U. Jawadekar,
  • Aniket K. Shahade

摘要

This paper proposes a novel hybrid ensemble machine learning framework integrated with Explainable AI (XAI) to overcome the fundamental trade-off between non-detection zones (NDZ) and false positives in conventional islanding detection methods. The approach introduces a cascaded dual-stage architecture: first, an Isolation Forest algorithm serves as a high-sensitivity anomaly trigger to minimize the NDZ by detecting nearly all operational deviations; second, an optimized multi-class XGBoost classifier, activated only upon an anomaly, precisely discriminates islanding events from other disturbances like faults and switching operations. Validated on a comprehensive public dataset, the framework demonstrated a high islanding detection rate of 96.0% while reducing false positives to a near-zero rate of 2.7% through adaptive confidence thresholding. Integrated SHAP-based interpretation provides operational transparency by quantifying feature contributions to each decision. This work introduces a novel cascaded framework that uniquely integrates an unsupervised anomaly detector (Isolation Forest) with a multi-class XGBoost classifier and SHAP-based explainability for islanding protection. Unlike prior hybrid methods, the proposed architecture explicitly decouples sensitivity near-zero NDZ and specificity (near-zero false positives) while providing full decision transparency, offering a more reliable and interpretable solution compared to conventional single-model approaches.