A Novel Control Application for Robust and Optimal Energy Management in a Grid-Interfaced Hybrid Renewable Energy System: AGOA-GBDT Control Approach
Bibliographic record
Abstract
In this study, we modeled and designed a novel, efficient controller for a hybrid renewable energy system with an integrated converter and associated grid interface.The proposed grid-interfaced Hybrid Renewable Energy System (HRES) model is built by properly connecting the photovoltaic (PV) system, Wind Energy Conversion System (WECS), battery, DC/DC converter, Maximum Power Point Tracking (MPPT) controller, microgrid, and load to obtain the desired output.The integrated converter used in this is a modified high-conversion ratio converter for improving conversion efficiency.A maximum power point tracker is used to track the maximum power from renewable energy sources.We have designed an efficient controller with a successful control strategy to provide optimal switching for the grid-side inverter to achieve optimal energy management in the system.A novel control method is developed by combining the Adaptive Grasshopper Optimization Algorithm (AGOA) and the Gradient Boosting Decision Tree Algorithm (GBDT), which is named the AGOA-GBDT method.The proposed approach uses AGOA as an evaluation technique to develop accurate command signals and enrich the command signal database for offline use.The data sets collected from the sensors are also used to build a control system with fast feedback for online training of the GBDT system.The main purpose of using and proposing this novel control technique is to improve efficiency by achieving optimal energy management in the model.The formulation of the problem considers several constraints, such as the intermittent nature of renewable energy sources, the state of charge of the storage components, and the power demand.The MATLAB/Simulink platform simulates the proposed system model with the proposed controller and other controllers.This proposed control technique proved to be the best in efficacy and convergence properties when compared with other existing control techniques in the literature.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".