Neural Network-Based Control Algorithm for DSTATCOM Under Nonideal Source Voltage and Varying Load Conditions
Bibliographic record
Abstract
Distribution static compensator (DSTATCOM) is the optimal choice of power quality (PQ) compensator in a three-phase four-wire distribution system for the mitigation of PQ problems. The performance of the PQ compensator under varying load and nonideal source conditions relies on the control strategy. A neural network-basedp-qcontrol algorithm is proposed in this paper for the DSTATCOM, which comprises of a four-leg voltage-source converter with a dc capacitor. The proposed control strategy implements five artificial neural network controllers for, the conversion of nonideal voltage source into ideal sinusoidal voltage, the extraction of dc component$\overline p $of load real power supplied to the load, maintenance of the voltage across the capacitor, and mitigation of neutral current. The performance of the proposed neural network-basedp-qcontrol strategy for DSTATCOM is evaluated under various possible source and load conditions by simulating in MATLAB/Simulink environment, and the results obtained through the simulation are validated experimentally by a prototype developed in the laboratory. Both the experimental and simulation results prove that the performance of the proposed neural network-based control strategy is superior to the conventional method.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".