Fault-Tolerant Observer-Based Control of Dynamic Virtual Power Plants
Bibliographic record
Abstract
Utilizing more inverter-based resources (IBRs) in modern power systems decreases their overall inertia and stability margin. Additionally, IBRs typically generate less power compared to traditional fossil-fuel-based plants. This forces them act as price followers in the energy market. These challenges are addressed by utilizing the concept of a dynamic virtual power plant (DVPP). On the other hand, the performance of DVPP is explicitly influenced by the sources' controllers, system uncertainties, and faults. This paper suggests a novel hierarchical observer-based fault-tolerant controller for the DVPPs. The proposed approach comprises three parts of setpoint design, state and fault observer, and robust controller. The setpoint design part allows for choosing the proper operating state and input for each source from its dynamical reference model. The state and fault observer facilitates estimating the system information from its measurable outputs. And, the controller is robust against external disturbance. The controller and observer gains are computed by solving a set of linear matrix inequalities (LMIs). Compared to state-of-the-art methods, the proposed approach does not integrate the dynamical reference and the source models, which reduces the complexity of the design procedure and eases the expandability of the DVPP. Moreover, in contrast to recent works, this paper involves the effect of an actuator fault in the design procedure. The effectiveness of the approach is validated through simulation studies to improve the reliability of the faulty DVVP in generating expected power.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".