Emad Abukhousa

Akib Mostabe Refat, Mohammed F. Al-Mashdali, Alan Cordic et al.

The large-scale Electric Vehicle (EV) integration into the electricity grid has initiated significant challenges to grid stability issues due to dynamic loadability events. Although electric vehicle impacts on distribution systems are well studied, transmission-level investigations remain limited. In this research paper, case scenarios of EV load models as charging stations have been considered for stability analysis (Voltage and Frequency Stability) to address EV operation on the transmission grid. It is also noted that the operation of EV stations due to their high loadability causes more stability complexities to the grid compared to other loads in a power network. Simulations have been conducted on two different power networks of the IEEE-9 and IEEE-39 bus test systems, respectively.

Emad Abukhousa, Saman Zonouz, A. P. Sakis Meliopoulos

This work introduces a latency-aware benchmarking framework for evaluating deep learning models in power system anomaly detection using high-fidelity, time-domain signals generated from an industry-grade electromagnetic transient simulator. Eight neural network architectures, ranging from MLPs to Transformers, were systematically evaluated on streaming datasets representing both physical faults and cyber-attacks in inverter-dominated networks. All models successfully classified two representative multi-event sequences in real time with sub-cycle response times below 15 ms. However, although classification decisions occurred within one cycle, the end-to-end inference latency consistently exceeded three cycles, ranging from 50 to 90 ms. These results highlight a critical gap between algorithmic capability and protection-grade deployment, pointing to the need for further optimization and hardware acceleration. The findings establish a reproducible benchmark for sub-cycle anomaly detection and provide guidance for transitioning machine learning methods from research prototypes to real-world protection applications.

Emad Abukhousa, Syed Sohail Feroz Syed Afroz, Fahad Alsaeed et al.

This paper presents a high-fidelity evaluation framework for machine learning (ML)-based classification of cyber-attacks and physical faults using electromagnetic transient simulations with digital substation emulation at 4.8 kHz. Twelve ML models, including ensemble algorithms and a multi-layer perceptron (MLP), were trained on labeled time-domain measurements and evaluated in a real-time streaming environment designed for sub-cycle responsiveness. The architecture incorporates a cycle-length smoothing filter and confidence threshold to stabilize decisions. Results show that while several models achieved near-perfect offline accuracies (up to 99.9%), only the MLP sustained robust coverage (98-99%) under streaming, whereas ensembles preserved perfect anomaly precision but abstained frequently (10-49% coverage). These findings demonstrate that offline accuracy alone is an unreliable indicator of field readiness and underscore the need for realistic testing and inference pipelines to ensure dependable classification in inverter-based resources (IBR)-rich networks.