Wide-Area-Based Adaptive Neuro-Fuzzy SVC Controller for Damping Interarea Oscillations
Bibliographic record
Abstract
Low-frequency interarea oscillation is a major problem in interconnected power systems with weak tie-lines that causes several stability problems if not damped. Fuzzy logic controller can generate human knowledge-based control rules to solve complex nonlinear problems. Unlike a neural network, fuzzy systems cannot learn from data, and it takes a long time to modify the membership functions. The adaptive neuro-fuzzy inference system (ANFIS) is a robust and intelligent system that integrates the capabilities of fuzzy logic and neural networks with several advantages such as adaptability, robustness, rapidity, and flexibility. In this paper, an ANFIS-based controller is proposed for controlling the reactive power provided by static var compensator to damp interarea oscillations. The controller input is a remote signal provided by a wide-area measurement system, and it is calculated as the center-of-inertia difference of generator rotor speed deviations. Moreover, a proportional-plus-derivative time-delay compensator with adaptive parameters is added to the controller to reduce the influence of time delay. A two-area four-machine test system is used and simulated with a Simulink-based package developed for the work of this paper. The time-domain simulations and frequency response analysis demonstrate the capability of the proposed controller to effectively damp interarea oscillations, under a small- and large-scale disturbances and against a wide range of time delays and load uncertainty.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".