Fault Analysis and Control of DFIGs for Grid Code Compliance and Protection of Power System
Bibliographic record
Abstract
Inverter-based resources (IBRs) are growing at exponential rates in today's power systems. Therefore, a sizable portion of the measurements of relays is expected to come from IBRs. However, the fault current characteristics of IBRs put the operation of the relays in jeopardy as they are different than that of synchronous generators' (SGs) based on which the relays' operating principles are developed. Therefore, different countries have progressively revised their grid codes (GCs) to reduce the likelihood of protection malfunctions and ensure stable and continuous operation of power systems. \n\nSimilar to emerging regional GCs, the recently approved IEEE 2800 Standard mandates that IBRs generate negative-sequence current during low-voltage ride-through (LVRT) conditions. The 2800 Standard requires that the IBRs' negative-sequence current lead the negative-sequence voltage by 90-100 degrees to emulate SGs and reduce the likelihood of protection malfunction. However, the limitations of existing doubly-fed induction generators (DFIGs) led the Standard to exempt the DFIGs from this requirement and allow a wider range for their negative-sequence current angle. Meanwhile, the 2800 Standard also acknowledged that this exemption had unidentified and potentially negative impacts on protective relays. This dissertation, for the first time, (i) sheds light on several so-far-unknown DFIG characteristics that impact the angle of the negative-sequence current during LVRT, (ii) reveals the impacts of the above DFIG exemption on industrial relays, and (iii) develops a solution to prevent the need for this exemption in the future revisions of the IEEE 2800 Standard.\n\nThis dissertation also investigates the challenges brought about by the DFIGs during the crowbar connection and rectification mode of operation, i.e., interrupted control of the DFIG's converters, now affecting the performance of distance relays that are installed at a DFIG-based wind farm substation. The focus is on the relays implemented using the apparent impedance approach and the commercially developed reactance method. It is revealed that the phase elements of a distance relay that uses these methods are prone to under-/over-reach in the systems with DFIGs. The exclusive fault behavior of DFIGs along with different units of a distance relay is scrutinized to identify the root causes. To address the relay problems, a communication-assisted method with minimal bandwidth requirement is developed, which provides non-delayed fast tripping over the entire length of the line.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".