Optimising Damping Control in Renewable Energy Systems through Reinforcement Learning within Wide-Area Measurement Frameworks
| Autor: | Truong Ngoc-Hung |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Elektronika ir Elektrotechnika, Vol 30, Iss 3, Pp 32-45 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1392-1215 2029-5731 |
| DOI: | 10.5755/j02.eie.36385 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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