A comprehensive systematic and bibliometric review of technologies and measurement tools for power quality events detection, classification, and fault location in smart grids
Bibliographic record
Abstract
Integrating inverter-based resources (IBRs) into smart grids (SGs) introduces new technical challenges for power quality (PQ) maintenance as well as fault detection and system reliability. Several recent studies have explored various aspects of SGs to enhance power quality, as well as fault detection, localization, and classification. However, several factors still require further improvement. This review paper employs systematic review and bibliometric analysis to examine advanced SG technologies such as automatic voltage regulation (AVR), advanced metering infrastructure (AMI), automatic generation control (AGC), and wide area measurement systems (WAMS) before comparing their effectiveness at addressing operational problems such as voltage regulation as well as outage management and data processing. The study examines measurement tools such as phasor measurement units (PMUs), smart meters (SMs), digital measurement units (DMUs), and waveform measurement units (WMUs) to understand their roles in PQ events detection, classification, and location identification. Research trends and emerging technologies along with current research gaps were identified through a bibliometric study of peer-reviewed articles from Web of Science (2013–2024) using VOS Viewer visualization techniques. A combined analysis delivers an integrated view that shows how smart grid innovations and measurement solutions boost monitoring capabilities while simultaneously improving event analysis and grid resilience in contemporary power systems. • Systematic and bibliometric review of smart grid technologies. • Comparative survey of measurement tools in smart grids: PMUs, WMUs, SMs, DMUs. • Power quality events detection, classification, and fault location methods. • Analysis of technology devices supporting grid monitoring and resilience. • Research gaps and future directions for next-generation smart grids.
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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 | 0.014 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.089 | 0.115 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".