Detect grid faults in real time — with AI.
An AI-based system that detects and classifies faults in electrical power transmission lines as they happen, using machine learning and signal processing to make diagnosis faster, more accurate, and more reliable than conventional protection.
Beyond conventional protection
Traditional fault-protection methods are proven — but AI can push speed, accuracy, and reliability further.
Conventional protection
- Relies on fixed thresholds and settings
- Limited ability to classify fault types
- Can be slower to adapt to complex conditions
AI-based detection
- Learns patterns from signal data
- Detects and classifies faults in real time
- Aims for higher speed, accuracy & reliability
From signal to diagnosis
Signal processing and machine learning combine to catch and classify faults the moment they occur.
Acquire signals
Continuously capture voltage and current signals from the transmission line.
Process & extract
Signal-processing techniques clean the data and extract the features that reveal faults.
Detect with ML
Machine-learning models detect faults in real time and classify their type.
Diagnose fast
Deliver a fast, reliable diagnosis to support quicker, better-informed protection.
Built for the modern grid
Real-time detection
Identifies faults as they happen on power transmission lines, without waiting for manual analysis.
Fault classification
Distinguishes between fault types, giving operators clearer, more actionable information.
Machine learning core
ML models learn fault signatures from data, improving on rigid, threshold-based logic.
Signal processing
Extracts meaningful features from voltage and current waveforms for accurate diagnosis.
Speed & accuracy
Aims to improve both how fast and how precisely faults are diagnosed versus conventional methods.
Reliable protection
More dependable diagnosis supports safer, more resilient operation of the power grid.
د. عماد بريك
Dr. Emad Breik leads this project developing an AI-based system for the real-time detection and classification of faults in electrical power transmission lines. By combining machine learning with signal-processing techniques, the work aims to improve the speed, accuracy, and reliability of fault diagnosis compared to conventional protection methods.
Smarter protection for power systems
Interested in the research, a collaboration, or a pilot with grid operators? Let’s connect.
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