Guideline for Verifying IEEE Harmonic Compliance
Bibliographic record
Abstract
With the increase in generation from inverter-based resources (IBRs), harmonic distortion is becoming a larger issue than it has been in the past. Harmonic distortion must be limited in the power grid because it can cause many negative impacts, such as decreased efficiency and equipment damage. The IEEE 519 [1], IEEE 1547 [2], and IEEE 2800 [3] standards describe harmonic distortion limits that various systems should meet. The limits differ based on several factors, including voltage level, interconnection location, IBRs, etc. The organization and complexity of these IEEE standards can make it difficult and time-consuming to identify if a system is compliant with the standards properly. The main purpose of this paper is to act as a guideline for establishing IEEE harmonic compliance, which can be summarized in Fig. 4. There are three steps to confirm system compliance with the IEEE standards. First, data must be measured at the project’s point of interconnection (POI). Second, a statistical evaluation must be performed on the data. Third, these results must be compared to the correct IEEE limits. Each of these three steps can be either unclear or confusing within the IEEE standards for harmonic distortion. This paper discusses these difficulties in detail and establishes a flowchart for identifying IEEE harmonic compliance.
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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 | 0.032 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.019 |
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".