A deep asynchronous actor‐critic learning‐based event‐triggered decentralized load frequency control of power systems with communication delays
Bibliographic record
Abstract
Abstract This article proposes a novel asynchronous advantage actor‐critic (A3C) learning‐based dynamic event‐triggered mechanism for the decentralized load frequency regulation to alleviate the local‐area communication burden and influence of the load fluctuations. The proposed dynamic event‐triggered mechanism applies the A3C algorithm to optimally adjust the threshold of the event‐triggered function in real time. In the A3C algorithm framework, the long short‐term memory (LSTM) network is used to estimate the policy function and value function. First, for each control area, a novel model of the decentralized load frequency control (LFC) system is established to design the event‐triggered communication mechanism and deal with the communication delay simultaneously. Then, based on the Lyapunov stability theory, the controller gain parameters of the decentralized LFC system and the margins of the even‐triggering thresholds are derived by solving a series of linear matrix inequalities (LMIs). Finally, a three‐area and four‐area power systems are used to evaluate the proposed decentralized LFC method. Simulation results show that the proposed method can greatly reduce the data transmission times and preserve a satisfactory system performance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".