Dynamic Phasor-Based Modeling and Analysis of Selective Harmonic Compensated Single-Phase Grid-Forming Inverter Connected to Nonlinear and Resistive Loads
Bibliographic record
Abstract
The presence of nonlinear loads in islanded microgrids causes significant deterioration of the quality of power supplied to consumers. Selective harmonic compensators (SHCs) such as proportional-resonant and harmonic controllers are conventionally used to reduce the level of total harmonic distortion (THD) caused by nonlinear loads. To design SHCs that can attenuate undesired harmonics, high-fidelity models and simulations are essential. Detailed converter models found in electromagnetic transients (EMT) simulators are accurate but demand significant computational resources and time. This paper proposes the dynamic phasor (DP) model of a single-phase grid-forming inverter connected to nonlinear (diode-bridge rectifier) and resistive loads. Results obtained from simulating the DP model are validated with results from detailed model simulation and experiment. Simulation results show that the DP model is 143 times faster than a corresponding detailed model. Experimental results confirm the high fidelity of the DP model in capturing harmonic currents and voltages in the case study system. The proposed DP model will be useful to researchers/engineers interested in conducting fast-paced and accurate system-level study of large power electronics-dominated grids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".