Editorial for the special issue on power quality conditioning in modern power grids integrated emerging power electronic systems
Bibliographic record
Abstract
DUE to the environment concerns, energy risks, fossil fuel problems, and the evolution of smart grid, the penetration of renewable energy systems and electric transportation systems is booming with a fast pace. Besides, with the proliferation and increased use of motor loadings and power electronic devices into all kinds of energy processing systems, the power quality (PQ) problems such as: reactive power and harmonics become more serious. This kind of PQ problems distort the source, lower the efficiency, and may cause instability, thus it makes negative impacts on the performance and reliability of grid or microgrids. In this regard, to ensure secure, reliable, and efficient power supply and transmission in power-electronics-controlled systems, PQ issues should be properly taken care of. That is, the desires to develop PQ-free or PQ-compensated power electronic systems and infrastructures are of high interest and importance. Hence, we organized this Special Issue on Power Quality Conditioning in Modern Power Grids Integrated Emerging Power Electronic Systems for targeting the analysis, topology and control strategy of PQ-free or PQ-compensated power converters in renewable energy systems, electric transportation systems, PQ conditioning systems, industrial automation systems, lighting systems, etc., which also includes the detection, characterization and analysis methods of the PQ issues.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.028 | 0.016 |
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".