Analysis and compensation of multiple PMU data delays for frequency control in islanded microgrids
Bibliographic record
Abstract
For large-scale interconnected multiple-area power systems, phasor measurement units (PMUs or called synchrophasor) are extensively used in the widearea control, monitoring, and protections. A PMU can provide simultaneous measurements of phasors across a wide area of the power systems. These PMU measurements are synchronized from a common time source provided by a global positioning system (GPS) radio clock. Recently, micro-PMUs or microsynchrophasors are designed and installed in microgrids to feed synchronized and time-stamped measurement for microgrid control centers. With the synchronized voltage frequency and magnitude data, the secondary control of a microgrid can provide the accurate and real-time power sharing and system monitoring. Communication networks are embedded to facilitate the information exchange between a microgrid centralized controller, PMUs, and local controllers in microgrids. When PMU data are transmitted, they may be delayed as network-induced delays are found in most networks. Therefore, it is indispensable to study the impact of the PMU data delay on the microgrid performance. This chapter investigates the effect of multiple PMU communication delays on the secondary frequency control of an islanded microgrid with multiple distributed generators (DGs). A small-signal model is obtained for the microgrid to analyze the relationship between the secondary frequency control gains and the PMU delays. To improve the performance of the microgrid when multiple PMU delays exist, a gain scheduling approach is also proposed. Both eigen-based theoretical analysis and time-domain simulations on the Canadian urban distribution system have verified that PMU delays can adversely affect the microgrid secondary frequency control and the designed gain schedulers for each DG can improve the robustness of the microgrid secondary frequency controller to communication delays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".