Academic

Power Interpretable Causal ODE Networks: A Unified Model for Explainable Anomaly Detection and Root Cause Analysis in Power Systems

arXiv:2602.12592v1 Announce Type: new Abstract: Anomaly detection and root cause analysis (RCA) are critical for ensuring the safety and resilience of cyber-physical systems such as power grids. However, existing machine learning models for time series anomaly detection often operate as black boxes, offering only binary outputs without any explanation, such as identifying anomaly type and origin. To address this challenge, we propose Power Interpretable Causality Ordinary Differential Equation (PICODE) Networks, a unified, causality-informed architecture that jointly performs anomaly detection along with the explanation why it is detected as an anomaly, including root cause localization, anomaly type classification, and anomaly shape characterization. Experimental results in power systems demonstrate that PICODE achieves competitive detection performance while offering improved interpretability and reduced reliance on labeled data or external causal graphs. We provide theoretical resu

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Yue Sun, Likai Wang, Rick S. Blum, Parv Venkitasubramaniam · · 1 min read · 2 views
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arXiv:2602.12592v1 Announce Type: new Abstract: Anomaly detection and root cause analysis (RCA) are critical for ensuring the safety and resilience of cyber-physical systems such as power grids. However, existing machine learning models for time series anomaly detection often operate as black boxes, offering only binary outputs without any explanation, such as identifying anomaly type and origin. To address this challenge, we propose Power Interpretable Causality Ordinary Differential Equation (PICODE) Networks, a unified, causality-informed architecture that jointly performs anomaly detection along with the explanation why it is detected as an anomaly, including root cause localization, anomaly type classification, and anomaly shape characterization. Experimental results in power systems demonstrate that PICODE achieves competitive detection performance while offering improved interpretability and reduced reliance on labeled data or external causal graphs. We provide theoretical results demonstrating the alignment between the shape of anomaly functions and the changes in the weights of the extracted causal graphs.

Executive Summary

The article introduces Power Interpretable Causality Ordinary Differential Equation (PICODE) Networks, a novel machine learning model designed for anomaly detection and root cause analysis in power systems. Unlike traditional black-box models, PICODE provides explanations for detected anomalies, including root cause localization, anomaly type classification, and anomaly shape characterization. The model demonstrates competitive performance in power systems while reducing the need for labeled data or external causal graphs. Theoretical results support the alignment between anomaly functions and changes in causal graph weights, enhancing interpretability.

Key Points

  • ▸ PICODE Networks offer a unified approach to anomaly detection and root cause analysis.
  • ▸ The model provides explanations for detected anomalies, improving interpretability.
  • ▸ Experimental results show competitive performance in power systems.
  • ▸ Theoretical results support the alignment between anomaly functions and causal graph weights.

Merits

Enhanced Interpretability

PICODE Networks provide detailed explanations for detected anomalies, which is crucial for safety and resilience in power systems. This interpretability helps stakeholders understand the root causes and types of anomalies, enabling more informed decision-making.

Reduced Reliance on Labeled Data

The model's ability to function with reduced reliance on labeled data or external causal graphs makes it more practical for real-world applications where labeled data may be scarce or expensive to obtain.

Competitive Performance

The experimental results demonstrate that PICODE achieves competitive detection performance, making it a viable option for practical deployment in power systems.

Demerits

Complexity of Implementation

The integration of causality-informed architecture and ordinary differential equations may increase the complexity of implementation, requiring specialized knowledge and resources.

Limited Scope of Experimental Validation

While the results are promising, the experimental validation is limited to power systems. Further research is needed to assess the model's performance in other cyber-physical systems.

Theoretical Assumptions

The theoretical results rely on certain assumptions about the alignment between anomaly functions and causal graph weights, which may not hold in all scenarios.

Expert Commentary

The introduction of PICODE Networks represents a significant advancement in the field of anomaly detection and root cause analysis for power systems. The model's ability to provide interpretable results is particularly noteworthy, as it addresses a critical gap in traditional machine learning approaches. The reduced reliance on labeled data and external causal graphs makes the model more practical for real-world applications. However, the complexity of implementation and the limited scope of experimental validation are areas that require further attention. The theoretical results provide a strong foundation, but their applicability in diverse scenarios needs to be thoroughly explored. Overall, the article offers valuable insights and sets the stage for future research in explainable AI and causal inference within cyber-physical systems.

Recommendations

  • ✓ Further research should focus on validating the model's performance in other cyber-physical systems beyond power systems.
  • ✓ Efforts should be made to simplify the implementation process to make the model more accessible to a broader range of practitioners.

Sources

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