Intelligent Wind Generator models for power flow studies in PSS®E and PSS®SINCAL
Bibliographic record
Abstract
Summary form only given. Wind generator (WG) output is a function of wind speed and three-phase terminal voltage. Distribution systems are predominantly unbalanced. A WG model that is purely a function of wind speed is simple to use with unbalanced three-phase power flow analysis but the solution is inaccurate. These errors add up and become pronounced when a single three-phase feeder connects several WGs. Complete nonlinear three-phase WG models are accurate but are slow and unsuitable for power flow applications. This paper proposes artificial neural network (ANN) models to represent type-3 doubly-fed induction generator and type-4 permanent magnet synchronous generator. The proposed approach can be readily applied to any other type of WGs. The main advantages of these ANN models are their mathematical simplicity, high accuracy with unbalanced systems and computational speed. These models were tested with the IEEE 37-bus test system. The results show that the ANN WG models are computationally ten times faster than nonlinear accurate models. In addition, simplicity of the proposed ANN WG models allow easy integration into commercial software packages such as PSS®E and PSS®SINCAL and implementations are also shown in this paper.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".