Stochastic Modelling and Stability Analysis of Large-Scale Wind Power Generation System with Dynamic Loads
Bibliographic record
Abstract
As a result of dwindling fossil fuel reserves and the negative impact of greenhouse gases (GHGs) on the environment, it is important that the search for a power grid that will comprise of only variable renewable energy (VRE) generation sources such as wind or solar energy which is available everywhere for free is given much attention. The main challenge associated with these sources of energy is their variability and random nature. It is as a result of instability introduced by the VRE generation sources, that is why there is a strict control measures put in place by the regulators as to how much VRE generation sources can feed into the power grid in the case of its integration into an existing power grid. It becomes imperative to consider implications for grid stability and reliability when considering a power grid that will consist of VRE generation only. Eigenvalue approach was used to analysed the performance of anticipated large-scale VRE grid to ascertain its behaviour. Eigenvalue approach is one of the methods to examine the stability of a power system’s dynamic performance. The results indicated that it is possible to attain the necessary stability provided the assumptions made during the modelling stage is revised to improve upon the model.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".