A Novel Reduced-Order Modeling Approach of a Grid-Tied Hybrid Photovoltaic–Wind Turbine–Battery Energy Storage System for Dynamic Stability Analysis
Bibliographic record
Abstract
This paper presents a novel reduced-order modeling approach for efficient modeling and dynamic stability analysis of a utility-scale hybrid grid-tied system comprising a photovoltaic (PV) array, wind turbine (WT), battery energy storage system (BESS) and the associated power electronic converters and control systems. Utilizing the singular perturbation analysis, the time-domain nonlinear model (TDNLM) of the grid-tied hybrid PV-WT-BESS system is linearized to construct the linearized state-space full-order model (LSSFOM). Categorizing the dynamics of the LSSFOM into fast and slow states based on their weighted dynamics utilizing the participation factor analysis and the residue-based method, the model is further reduced to the linearized state-space reduced-order model (LSSROM), focusing on dominant slow-dynamic states that characterize the overall system dynamics. The LSSROM is employed to investigate dc and ac dynamic interactions under various operational conditions, including all PV, WT, and BESS operating regions and grid stiffness conditions. The proposed reduction approach reduces the computational burden with simplicity and efficiency, facilitating the development of reliable reduced-order models capturing the essential features of the original detailed full-order model with a high degree of acceptable accuracy for dynamic and stability analyses across diverse operating conditions while ensuring versatility. Detailed offline and real-time simulation results validate the analytical results, demonstrating the efficiency of the proposed approach across different operational scenarios.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".