SCADA system for remote control and monitoring of grid connected inverters
Bibliographic record
Abstract
This thesis presents a development of a supervisory control and data acquisition (SCADA) system for remote control and monitoring of grid-connected inverters. Since the number of battery energy storages connected to the grid is increasing the number of inverters connected to the power system is also rapidly growing. Utilities need to have the ability to monitor and control those inverters connected to the grid to maintain the stability of the network, to improve the quality of the power supplied and to stabilize the energy prices. After recognizing the requirement for a low-cost SCADA system for grid-tied inverters, essential features that needs to be embedded in the system have been identified by analyzing SCADA systems in the Wind Energy Institute Canada (WEICAN). Based on available options to fulfill the requirement selected SCADA systems were tested during the research. Based on the test results an Internet of Things (IoT) based server has been kept as the core of the developed SCADA system, and a SCADA development has been carried out to improve the system to deliver features identified. A requirement was recognized to embed an automatic control algorithm to the SCADA system for optimal control of the inverter to maximize the economic benefits out of it by considering the energy price variation and the renewable energy variation through a specific period. Results illustrate that the developed SCADA system has been able to deliver features identified during the research and the wind prediction algorithm has been able to maximize the economic benefits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".