A protection scheme for microgrids using intelligent relays
Bibliographic record
Abstract
Microgrids are emerging as an important part in modern power distribution systems due to significant development in distributed generation technologies. Microgrids operate with a high level of inverter interfaced distributed generation (IIDG) penetration such as fuel cells, solar cell, and battery storage etc. The problem of microgrid protection becomes challenging as the fault current varies widely depending upon the operating conditions such as grid-connected and islanded mode in presence of IIDG. Fast and accurate fault detection coupled with a clearing mechanism is required for a safe and secured micro-grid operation.This thesis presents a microgrid protection scheme using intelligent relays. The proposed intelligent relay is developed by combining a Wavelet Transform and Decision Tree models. The relay detects and classifies faults using local measurements irrespective of the operating mode of the micro-grid. The process starts by measuring and pre-processing the current signal at the relaying point using the Wavelet Transform. Time-frequency features such as change in energy, entropy and standard deviations are calculated using the wavelet coefficients. Cases representing various faulted and normal conditions are simulated to generate the complete data set containing the above mentioned features. This data set is used to train the decision tree for fault detection. The fault classification data set contains line current negative and zero sequence components along with the wavelet based features from current signals for the faulted cases only. The detection and classification decision trees are extensively tested on a large unseen data set and the test results indicate that the proposed relaying scheme can reliably protect the micro-grid against faulty conditions under wide variations in operating conditions. The performance is superior to that of instantaneous over current relays. The performance of the decision tree model is compared with another data mining model known as the random forest model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".