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Advanced AI-driven techniques for fault and transient analysis in high-voltage power systems
Abdul Aziz1,2, Muhammad Zain Yousaf3,4,5, Feng Renhai6
1CECOS University of IT and Emerging Sciences Peshawar Kpk Pakistan, Peshawar, Pakistan.
Scientific Reports
|February 15, 2025
Summary
This study simulates a 500kV substation using ETAP software to analyze faults. Artificial intelligence, particularly Catboost, accurately classifies normal and faulty conditions, improving power system dependability.
Area of Science:
- Electrical Engineering
- Power Systems Analysis
- Artificial Intelligence in Power Systems
Background:
- Substations are critical for power system operation, but face hazards like thermal stress and radiation.
- Failures in substation components (cables, circuit breakers, fuses) reduce dependability and cause transmission losses.
- Optimizing distributed generation (DG) is essential for enhancing voltage profiles.
Purpose of the Study:
- To perform a comprehensive simulation of a 500kV substation using ETAP software.
- To analyze faults and transients within the substation and their impact on system performance.
- To optimize substation parameters using artificial intelligence techniques for improved dependability.
Main Methods:
- Utilized Electrical Transient Analyzer Program (ETAP) for detailed load flow and short circuit analysis of a 500kV substation.
- Conducted multiple simulations using real-time data over eighteen months, including normal and faulty conditions.
- Applied Artificial Intelligence (AI) techniques, including Catboost, Support Vector Machine (SVM), and Logistic Regression, for fault classification.
Main Results:
- Catboost achieved 98% accuracy in classifying normal versus faulty conditions, outperforming SVM (96%) and Logistic Regression (93%).
- For identifying specific fault types (line-to-line, line-to-ground, double line-to-ground), Catboost reached 97% accuracy, followed by SVM (95%) and Logistic Regression (92%).
- The AI models demonstrated strong performance in analyzing substation operational states.
Conclusions:
- AI techniques, especially Catboost, are highly effective for accurate fault detection and classification in electrical substations.
- Simulation using ETAP provides valuable insights into substation behavior under various conditions.
- The findings contribute to enhancing the dependability and efficiency of electrical power systems through advanced analysis and AI.
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