Modeling and Control of Power System Containing PV System and SMES using Sliding Mode and Field Control Strategy
Bibliographic record
Abstract
Although the great advance in power system production and operation, storage energy technologies and its control techniques can be considered as one of the most important and critical topics of power companies , government and consumers, especially when the power system containing renewable source and storage system simultaneously. In this paper, a novel electrical grid structure including photovoltaic system and storage system based on Superconducting Magnetic Energy Storage (SMES) has been proposed and investigated. The SMES produced power is injected in power system during specific time or when it required. Two control strategies for exchanged power Grid- SMES have been proposed and analyzed, the first uses sliding mode and the second uses field oriented control based on PI controller, also the injected SMES power is controlled by PID controller. In addition, the photovoltaic system operates at the MPP employing PID MPPT method. The proposed control strategies have been tested successfully in which many scenarios have been studied: standby and discharging of SMES, injection of SMES storage energy for variable and constant load and control of grid containing PV system. In addition, a comparative study of exchanged power Grid- SMES control using Sliding mode and field oriented control based on PI controller has been presented and discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".