A5033446500- Power Systems Fault Detection
- Electrical Fault Detection and Protection
- Power System Reliability and Maintenance
- Power Quality and Harmonics
- Electricity Theft Detection Techniques
- Smart Grid Security and Resilience
- Power Transformer Diagnostics and Insulation
- Islanding Detection in Power Systems
- Network Security and Intrusion Detection
- Machine Fault Diagnosis Techniques
Saudi Aramco (Saudi Arabia)
2019-2024
King Fahd University of Petroleum and Minerals
2018
The emergence of power system digitalization initiatives is revolutionizing the way electricity grids are monitored and protected. However, integration cyber physical electrical infrastructures leads to an increase in risk intrusions. Attackers can gain access smart grid inject falsified data, leading protection schemes activate unnecessary outage actions. Such outages be devastating end users. In this paper, intrusion detection mitigation (IDMS) proposed using deep learning neural networks...
Power quality disturbances became a major issue in modern commercial distribution grids; hence, an innovative attempt to diagnose the faults is necessary for their optimal management. This paper presents hybrid approach using Stockwell Transform (ST) and Machine Learning Techniques (MLT) detect, classify locate single-line-to-ground (SLG) modeled feeder laboratory scale. The three-phase current signals are processed with ST extract useful features whereas including Multilayer Perceptron...
Power quality disturbances become a major issue in modern commercial distribution grids, hence an innovative attempt to diagnose the faults is necessary for optimal management of power grids and associated assets. This paper presents hybrid approach using Stockwell transform (ST) multilayer perceptron neural network (MLP-NN) detect, classify simulated IEEE 13-node test feeder Real Time Digital Simulator (RTDS). In proposed technique, three-phase current waveforms are measured from different...
Industrial processes require continuous operation of low voltage Motor Power Distribution Centers (MPDC). Hence, lowering the interruption rates via pioneering a fault analysis scheme is necessary for optimum reliability such systems. This article presents an advanced approach using Artificial Neural Networks (ANN) and Stockwell Transform (ST) to detect classify Single Line Ground (SLG) faults in simulated MPDC. The virtual faulty healthy three-phase current signals were measured from...