Performance enhancement of PI and PIDn controllers through deep reinforcement learning for frequency regulation in renewable-integrated power systems
Bibliographic record
Abstract
Maintaining frequency stability has become a major difficulty for contemporary power systems when renewable energy sources – especially photovoltaic (PV) and wind power – are included into them. The intrinsic unpredictability of the supply of renewable energy causes this instability, which emphasises the great requirement of efficient load frequency management to guarantee stability of the power system. This work offers a new hybrid control architecture to improve frequency control in renewable-integrated power systems by means of deep reinforcement learning. Within a two-area power system encompassing three energy units: PV, wind, and thermal, the proposed approach consists in the installation of a deep deterministic policy gradient (DDPG) algorithm as a supplementary control mechanism in combination with optimised PI and PIDn controllers. Comprehensive simulations under many operating conditions – including step load changes, communication time delays, system parameter uncertainty, intermittent renewable energy supply, and random load disturbances – allow one to fully evaluate the effectiveness of the proposed control structure. Achieving up to 75% improvement in integral time-weighted absolute error values while preserving strong performance over several operating situations, the comparison analysis shows that the DDPG-assisted control system much beats conventional controllers. OPAL-RT based real-time simulations evaluate the practical feasibility of the proposed method by verifying its possibility for actual application in contemporary power systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".