Hybrid Machine Learning-based Intelligent Distance Protection and Control Schemes with Fault and Zonal Classification Capabilities for Grid-connected Wind Farms
Bibliographic record
Abstract
This paper presents the analysis of several hybrid intelligent protection and control algorithms to improve the reliability of doubly-fed induction generator (DFIG)-based wind farms during faults, and other dynamic operating conditions. First, a decision tree (DT) classification algorithm is developed as a fault classifier for the purpose of distinguishing between different types of faults, as well as normal operation and grid disturbances. Next, a support vector machine (SVM) as a fault location estimator and zonal protection scheme is proposed to assist with the decision-making process of distance relay by detecting the location of any type of fault on the transmission line, and precise line zoning protection with a high reliability. Lastly, a combined direct PI control-based scheme is developed for both rotor and grid side converters of the DFIG based wind energy conversion system (WECS). This scheme avoids extra PI based current loop to achieve robust performance at the time of grid side voltage dip as well as normal operating condition. In this research, MATLAB and WEKA software are used for developing, training and testing the proposed machine learning algorithms and designing proposed control scheme, while ETAP and PSCAD software are used for design, modelling, fault analysis and data acquisition of the wind farm, as well as testing the operation of distance relays for various conditions. The analysis of the proposed intelligent protection and control schemes exhibits satisfactory results in improving the reliability and stability of grid-connected wind farms.
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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) | 0.000 | 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 | 0.001 |
| 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".