Abstract

The increasing penetration of renewable energy, especially solar generation, has introduced higher variability into power system loading, making early detection of electrical faults more challenging yet more essential for maintaining network reliability. This research presents a machine-learning–based framework for early electrical fault detection using a Random Forest model developed in DataRobot. Due to confidentiality constraints on DEWA operational data, a Kaggle dataset was adopted and enriched with simulated solar irradiance variability to mirror real network conditions in the UAE. After comprehensive data preprocessing and feature engineering, the Random Forest model demonstrated strong generalization performance, accurately distinguishing early fault signatures from normal load fluctuations. Key findings show that the model can identify subtle precursors of faults before escalation, with performance significantly exceeding baseline models. Interpretability results from SHAP and RuleFit analyses revealed that features such as current imbalance, voltage deviations, phase-wise loading differences, and irradiance volatility were the strongest contributors to early fault prediction. Rule-based explanations indicated that specific combinations of rising current spikes and falling voltage levels consistently preceded fault events, offering actionable insights for grid operators. Overall, the study confirms that integrating machine learning into power system monitoring can meaningfully reduce missed detections, support proactive maintenance, and improve the operational reliability of distribution networks, particularly under renewable-induced variability.

Library of Congress Subject Headings

Electric power systems--Reliability--Data processing; Fault location (Engineering)--Data processing; Predictive analytics

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Boutheina Tlili

Campus

RIT Dubai

Plan Codes

PROFST-MS

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