Recent research:
The body of work consolidates advanced power systems optimization, control, and digitalization to enhance stability, efficiency, and sustainability of modern grids. Across the publications, the author combines traditional optimization (linear, nonlinear, and mixed-integer programming) with modern heuristics (genetic algorithms, Bat Algorithm, Artificial Bee Colony) and data-driven techniques (FVSI analysis, neural networks) to address concrete challenges such as subsynchronous resonance mitigation with fuzzy–PI STATCOM in DFIG-ready networks; multi-level inverter modulation with PSO-assisted SHE-PWM; residential microgrid demand response using simplex-based optimization and IoT integration; optimal DG placement and sizing for voltage stability and multi-area dispatch under contingencies; distribution system reconfiguration for loss minimization; multi-area economic dispatch and multi-region energy exchange; and reactive power planning via deep learning for voltage profile improvement. Methodologically, the work emphasizes case-study validation (IEEE 13/33-bus, 14/30/118-bus, CHB-MLI, IEEE 34/69/136-bus), contingency analysis, and comparisons against baseline or standard methods. The impact lies in providing data-driven, computationally tractable tools that improve voltage stability, reduce harmonic distortion and losses, enhance reliability, promote renewable integration, and guide policy-relevant planning in smart grids, microgrids, and distributed generation contexts.

Key Topics:

• Power Systems, Renewable Energy & Microgrids
• Optimization, Operations Research & Decision Support
• Smart Grids, IoT, Edge/Fog & Industry 4.0
• Control Systems, Automation & Industrial Control (SCADA/PLC)
• Artificial Intelligence, Machine Learning & Data Science
• Energy Management & Demand Response