A Comprehensive Review on STATCOM: Paradigm of Modeling, Control, Stability, Optimal Location, Integration, Application, and Installation
Bibliographic record
Abstract
The Static synchronous compensator (STATCOM) is a renowned FACTS (flexible alternating current transmission system) device used in power grids to cope with protean conditions. This article provides a comprehensive bibliographic review of various aspects of STATCOM developed over the last 16 years. The paper includes a detailed study presenting different models, test systems, and results of various researches. The study covers the modelling, control technology, stability, optimal location, applications, and installation of STATCOM. The modelling of STATCOM is diverse and include various models such as voltage source converters, software-based models, load flow models, pulse width modulation, and modular multilevel converters. The control technology includes a PI/PID controller, fuzzy-based, feedback controller, model predictive controller, sliding mode controller, and miscellaneous controller based on different schemes, algorithms, and controllers. In studying the stability analysis, all stability aspects such as steady-state stability, dynamic stability, and transient stability are considered for the stability analysis. The genetic algorithm, heuristic algorithm, probabilistic technique and branch and bound approach are mainly used for the optimal placement of STATCOM as described in the available research paper. Various applications of STATCOM in power system are identified such as optimal power flow, voltage stability, voltage fluctuation mitigation, fault analysis and total harmonic distortion. This paper, also mentions the current installation of STATCOM is also mentioned with their different locations such as China (2018), and Canada (2019). The current functions of STATCOM still have many limitations. So there is still a lot of room for improvement. This paper will help the researcher finding the reference of a particular topic of interest in different aspects of STATCOM. The finding of different sections of the paper can help the researchers to develop new ideas and work on different prospective of the device.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".