Research & Publications

Research, publications & teaching

Peer-reviewed work in inverter control, real-time simulation and microgrid power sharing, alongside graduate teaching in physics.

Publications

Two co-authored papers, both published with resolving DOIs. Each entry below gives the citation, the research problem the paper addresses, and a link to the publisher record.

Published2026

Deep Reinforcement Learning-Based Current Control of Grid-Following Inverters With Digital Real-Time Simulation

K. Pokharel, K. K.C., D. Hummels, H. Li

Cyber-Physical Energy Systems (Elsevier)

Grid-following inverters must track a current reference accurately as grid conditions shift, and conventional tuned controllers are hard to retune across operating points. The paper applies deep reinforcement learning to that current-control loop and evaluates it in digital real-time simulation rather than offline only.

Funded by U.S. National Science Foundation, award OIA-2316399
Open access

DOI: 10.1016/j.cpes.2026.04.005

›Full citation

K. Pokharel, K. K.C., D. Hummels, and H. Li, "Deep Reinforcement Learning-Based Current Control of Grid-Following Inverters With Digital Real-Time Simulation," Cyber-Physical Energy Systems, Aug. 2026, doi: 10.1016/j.cpes.2026.04.005.

Published2024

An Adaptive Droop Control for Power Sharing Between Three-Phase Parallel Inverters in an Autonomous Microgrid

A. Parajuli, K. Bista, K. K.C., B. Bhandari

KEC Journal of Science and Engineering, vol. 8, no. 1, pp. 27–32

When several three-phase inverters run in parallel in an autonomous microgrid, fixed droop settings share real and reactive power unevenly as line impedances and loading change. The paper proposes an adaptive droop control to improve that power sharing.

DOI: 10.3126/kjse.v8i1.69261

›Full citation

A. Parajuli, K. Bista, K. K.C., and B. Bhandari, "An Adaptive Droop Control for Power Sharing Between Three-Phase Parallel Inverters in an Autonomous Microgrid," KEC Journal of Science and Engineering, vol. 8, no. 1, pp. 27–32, 2024, doi: 10.3126/kjse.v8i1.69261.

Research areas

The topics covered by the publications above and by the real-time microgrid work at the University of Maine.

Grid-following inverter control
Deep reinforcement learning for power electronics
Digital real-time simulation
Adaptive droop control
Parallel inverters
Autonomous microgrids
Dynamic system identification

Graduate teaching

As a Graduate Teaching Assistant at the University of Maine, led weekly recitation sessions for PHY 121 and laboratory sessions for PHY 122, and provided individual tutoring through the Physics Learning Center.