Recent research:
The research focuses on advancing the resilience, efficiency, and reliability of modern electrical power systems, with a particular emphasis on smart microgrids and distribution networks. A core area of work involves the development of sophisticated optimization frameworks for energy management, integrating stochastic modeling to handle uncertainties in renewable energy generation, load demand, and market pricing. These studies leverage advanced computational techniques, including mixed-integer linear programming and two-stage stochastic optimization, to enhance operational cost-effectiveness and support demand response strategies.

Significant contributions have been made to power quality and signal processing, where novel compression techniques—utilizing wavelet transforms and sparse dictionary learning—have achieved high-efficiency data representation for electrical signals. Furthermore, the research addresses critical infrastructure challenges through robust fault detection and diagnostic methodologies. By applying Principal Component Analysis (PCA), sliding mode observers, and wavelet-based signal processing, these methods enable the identification of sensor faults, motor anomalies, and transmission line disturbances with high precision. Additionally, the work explores the integration of distributed energy resources, including battery storage and photovoltaic systems, through optimal siting and sizing models that improve grid stability. Finally, the research extends into practical network planning, such as the optimization of wireless sensor networks for agricultural monitoring and the implementation of adaptive control strategies to improve power factor and voltage profiles in distribution feeders.