Interaction Dynamics Analysis and Active Stabilization of a Weak Grid-Tied Hybrid PV-Wind Power Generator
Bibliographic record
Abstract
This paper addresses the dynamic stability of a hybrid photovoltaic (PV) and wind turbine (WT) system tied to a weak grid by back-to-back voltage-source converters (VSCs). The overall system stability is assessed by developing a detailed time-domain nonlinear model (TDNLM) and a linearized state-space model (LSSM). The dynamic interactions at both the peak-power point (PPP) and off-PPP operation of the PV and WT power characteristics curves have been examined. The results reveal that the complete system is stable under PV-only and WT-only generation scenarios at the PPP and off-PPP operation regions. However, the PV-WT hybrid system experiences low-and high-frequency fluctuation instabilities in the current-limited region (CLR) of the PV characteristic due to the interaction between the high dynamic resistances of the PV-WT source and the weak grid. Conversely, the system remains stable at the PPP and voltage-limited region (VLR) of the PV characteristic and in all operational regions of the WT characteristic. Motivated by these challenges, a novel active damping method is proposed to mitigate the interaction dynamics, maintaining overall system stability by relocating unstable eigenmodes and reshaping the dc-link transfer function. The proposed damping method offers several benefits: 1) it is simple yet efficient and can be implemented and modeled using linear analysis tools; 2) it allows for dynamic and stable operation across various operating regions; 3) it does not interfere with steady-state performance in both stiff and weak grid operations, 4) it facilitates successful low-voltage-ride-through (LVRT) at different operating regions; and 5) it effectively addresses its shortcomings by eliminating the need for additional voltage or current sensors, thereby enhancing simplicity and reducing cost in comparison to other approaches. Detailed offline and real-time simulations validate the analytical findings and the effectiveness of the proposed stabilization method under different operational scenarios.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".