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Balancing Performance and Fairness in Explainable AI for Anomaly Detection in Distributed Power Plants Monitoring

Corneille Niyonkuru, Marcellin Atemkeng, Gabin Maxime Nguegnang, Arnaud Nguembang Fadja

Abstract

Reliable anomaly detection in distributed power plant monitoring systems is essential for ensuring operational continuity and reducing maintenance costs, particularly in regions where telecom operators heavily rely on diesel generators. However, this task is challenged by extreme class imbalance, lack of interpretability, and potential fairness issues across regional clusters. In this work, we propose a supervised ML framework that integrates ensemble methods (LightGBM, XGBoost, Random Forest, CatBoost, GBDT, AdaBoost) and baseline models (Support Vector Machine, K-Nearrest Neighbors, Multilayer Perceptrons, and Logistic Regression) with advanced resampling techniques (SMOTE with Tomek Links and ENN) to address imbalance in a dataset of diesel generator operations in Cameroon. Interpretability is achieved through SHAP (SHapley Additive exPlanations), while fairness is quantified using the Disparate Impact Ratio (DIR) across operational clusters. We further evaluate model generalization using Maximum Mean Discrepancy (MMD) to capture domain shifts between regions. Experimental results show that ensemble models consistently outperform baselines, with LightGBM achieving an F1-score of 0.99 and minimal bias across clusters (DIR $\approx 0.95$). SHAP analysis highlights fuel consumption rate and runtime per day as dominant predictors, providing actionable insights for operators. Our findings demonstrate that it is possible to balance performance, interpretability, and fairness in anomaly detection, paving the way for more equitable and explainable AI systems in industrial power management. {\color{black} Finally, beyond offline evaluation, we also discuss how the trained models can be deployed in practice for real-time monitoring. We show how containerized services can process in real-time, deliver low-latency predictions, and provide interpretable outputs for operators.

Balancing Performance and Fairness in Explainable AI for Anomaly Detection in Distributed Power Plants Monitoring

Abstract

Reliable anomaly detection in distributed power plant monitoring systems is essential for ensuring operational continuity and reducing maintenance costs, particularly in regions where telecom operators heavily rely on diesel generators. However, this task is challenged by extreme class imbalance, lack of interpretability, and potential fairness issues across regional clusters. In this work, we propose a supervised ML framework that integrates ensemble methods (LightGBM, XGBoost, Random Forest, CatBoost, GBDT, AdaBoost) and baseline models (Support Vector Machine, K-Nearrest Neighbors, Multilayer Perceptrons, and Logistic Regression) with advanced resampling techniques (SMOTE with Tomek Links and ENN) to address imbalance in a dataset of diesel generator operations in Cameroon. Interpretability is achieved through SHAP (SHapley Additive exPlanations), while fairness is quantified using the Disparate Impact Ratio (DIR) across operational clusters. We further evaluate model generalization using Maximum Mean Discrepancy (MMD) to capture domain shifts between regions. Experimental results show that ensemble models consistently outperform baselines, with LightGBM achieving an F1-score of 0.99 and minimal bias across clusters (DIR ). SHAP analysis highlights fuel consumption rate and runtime per day as dominant predictors, providing actionable insights for operators. Our findings demonstrate that it is possible to balance performance, interpretability, and fairness in anomaly detection, paving the way for more equitable and explainable AI systems in industrial power management. {\color{black} Finally, beyond offline evaluation, we also discuss how the trained models can be deployed in practice for real-time monitoring. We show how containerized services can process in real-time, deliver low-latency predictions, and provide interpretable outputs for operators.
Paper Structure (27 sections, 4 equations, 24 figures, 3 tables)

This paper contains 27 sections, 4 equations, 24 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Dataset and Methodology
  4. Data Description
  5. Data Preparation and Processing
  6. Feature Engineering
  7. Class Labeling
  8. Feature Importance
  9. Distribution of Data
  10. Correlation
  11. Methodology
  12. Model Development
  13. Performance Evaluation
  14. Explainability and Interpretation
  15. Results
  16. ...and 12 more sections

Figures (24)

  • Figure 1: This flowchart illustrates a data labeling process for classifying anomalies in a dataset. It evaluates conditions related to running time and fuel consumption to categorize data points into four classes: Class 1, Class 2, Class 3 (anomalies), or Class 0 (normal).
  • Figure 2: A visualization of generator performance anomalies across multiple clusters, highlighting instances of extended runtime (>24 hours) and fuel overconsumption.
  • Figure 3: Frequency distribution of normal (Class 0) and anomalous (Classes 1-3) generator events across multiple regional clusters.
  • Figure 4: Feature importance from a RF classifier, showing Running time per day and consumption per day within a period as the most critical predictors for the model's decisions.
  • Figure 5: Distribution of Generator Data Across Clusters: A bar chart showing a skewed distribution of samples, with MEIGANGA 1 (577) and NGOUNDERI (539) having the highest counts, followed by a gradual decline to BANYO and WAZA ( 146-166)
  • ...and 19 more figures