Wide-Area-Based Adaptive Neuro-Fuzzy SVC Controller for Damping Interarea Oscillations
Why this work is in the frame
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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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it