Renewable Energy Integration
Grid integration of solar PV, wind, hydro and hybrid renewable energy systems with emphasis on dynamic performance and stability.
View work →ReX Lab is a research and experimental platform for renewable energy conversion, grid integration, advanced control, power electronics, and intelligent energy systems.
The Renewable Energy Experimental Lab (ReX Lab) is a research laboratory under the Department of Electrical Engineering at NIT Arunachal Pradesh.
Our work connects analytical modelling, simulation, laboratory experimentation and hardware validation to address practical challenges in renewable-energy-based power systems.
Interdisciplinary research across renewable generation, converters, control, grid stability and intelligent energy management.
Grid integration of solar PV, wind, hydro and hybrid renewable energy systems with emphasis on dynamic performance and stability.
View work →Advanced converter topologies, LCL filters, active damping, modulation and power-quality enhancement.
Model predictive control, virtual synchronous generator control, robust control and grid-forming strategies.
Metaheuristic optimization, reinforcement learning and intelligent energy-management methods for complex power systems.
Experimental investigation of asynchronous generators, hybrid excitation, load sharing and rural electrification.
Weak-grid operation, synchronization, transient stability, grid codes and coordinated renewable energy control.
dSPACE MicroLabBox · OPAL-RT · Real-time control validation
Power analyzers · Oscilloscopes · Voltage/current characterization
PV · Wind · Micro-hydro · Induction generator experimental platforms
Grid-connected converters · LCL filters · PLL · Grid-forming control
Selected themes that represent the laboratory's experimental and computational research programme.
Parallel-connected SEIGs, hybrid excitation, voltage regulation, load sharing and rural electrification.
Advanced control architectures for stable renewable-energy integration under weak-grid conditions.
Hybrid optimization and learning-based approaches for robust PLL and converter control.
Peer-reviewed work spanning micro-hydro power, self-excited induction generators, grid-forming inverters and AI-driven control — translating laboratory experiments into deployable renewable-energy systems.
Deep reinforcement learning and adaptive predictive control for grid-forming inverters and virtual synchronous generators under weak-grid conditions.
Edge-of-grid micro-hydro schemes and parallel self-excited induction generators with experimental hardware validation for rural electrification.
Metaheuristic and gradient-hybrid methods (PSO-GWO, GWO-DE) for optimal design of filters, converters and renewable-integration controllers.
Explore the complete, year-by-year list of journal articles, conference papers and book chapters.
Faculty, researchers and students working across modelling, control, power electronics and renewable energy systems.
Associate Professor
Department of Electrical Engineering
NIT Arunachal Pradesh
Real laboratory environments, experimental platforms, instrumentation and student research activities.






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