Optimal Control of Wind Energy Conversion System Output Based on Adaptive Dynamic Programming
Bibliographic record
Abstract
In today's energy sector, wind power has risen to prominence as a key renewable resource, appreciated for its eco-friendliness, sustainability, and minimal environmental footprint.Over recent decades, its adoption has surged, making it the fastest-growing option among renewable energy sources.Wind energy is captured and transformed into electricity via Wind Energy Conversion Systems (WECS), which predominantly rely on wind turbines.WECS can be divided into two main categories based on their interaction with power grids: grid-connected systems and stand-alone wind energy systems.Currently, most wind energy installations are grid-connected, facilitating efficient integration and distribution of the generated electricity.The control systems for WECS are intricate, necessitating advanced optimization methods to improve the effectiveness of wind energy conversion.This paper introduces an innovative approach to crafting an adaptive optimal controller based on Adaptive Dynamic Programming (ADP), aimed at enhancing energy conversion efficiency for grid-connected wind energy systems that use Permanent Magnet Synchronous Generators (PMSG).The proposed controller leverages reinforcement learning techniques along with Lyapunov stability analysis to guarantee dependable performance across a range of operational scenarios.To demonstrate the controller's effectiveness, we implemented and simulated a complete system comprising both the WECS and the controller in the Matlab and Simulink environments.The outcomes of these simulations will be evaluated to determine the controller's performance and its potential to boost the overall operational efficiency of wind energy 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".