TY  - JOUR
AU  - Ngoc-Hung, Truong
PY  - 2024/06/18
Y2  - 2025/01/01
TI  - Optimising Damping Control in Renewable Energy Systems through Reinforcement Learning within Wide-Area Measurement Frameworks
JF  - Elektronika ir Elektrotechnika
JA  - ELEKTRON ELEKTROTECH
VL  - 30
IS  - 3
SE  - 
DO  - 10.5755/j02.eie.36385
UR  - https://eejournal.ktu.lt/index.php/elt/article/view/36385
SP  - 32-45
AB  - <p>This paper introduces a reinforcement learning-based controller, utilising the deep deterministic policy gradient (DDPG) method, to mitigate low-frequency disturbances in electrical grids with renewable energy sources. It features a novel reward function inversely related to the control error and employs a state vector comprising absolute and integral errors to enhance error reduction. The controller, tested on a dual-region system with solar power, utilises phasor measurement unit (PMU) data for global inputs. Its performance is validated through time-domain simulations, pole-zero mapping, modal analysis, frequency response, and participation factor mapping, using a custom MATLAB and Simulink toolkit. The design accounts for communication delays and adapts to variable conditions, which proves to be effective in reducing oscillations and improving system stability.</p>
ER  -