Power System Controller Design by Optimal Eigenstructure Assignment
Bibliographic record
Abstract
In this thesis the eigenstructure (eigenvalues and eigenvectors) assignment technique based algorithm has been developed for the design of controllers for power system applications. The application of the algorithm is demonstrated by designing power system stabilizers (PSSs) that are extensively used to address the small-signal rotor angle stability problems in power systems. In the eigenstructure assignment technique, the critical eigenvalues can be relocated as well as their associated eigenvectors can be modified. This method is superior and yield better dynamical performance compared to the widely used frequency domain design method, in which only the critical eigenvalues are relocated and no attempt is made to modify the eigenvectors. The reviewed published research has demonstrated successful application of the eigenstructure assignment technique in the design of controllers for small control systems. However, the application of this technique in the design of controllers for power systems has not been investigated rigorously. In contrast to a small system, a power system has a very large number state variables compared to the combined number of system inputs and outputs. Therefore, the eigenstructure assignment technique that has been successfully applied in the design of controllers for small systems could not be applied as is in the design of power system controllers. This thesis proposes a novel approach to the application of the eigenstructure assignment technique in the design of power system controllers. In this new approach, a multi-objective nonlinear optimization problem (MONLOP) is formulated by quantifying different design objectives as a function of free parametric vectors. Then the MONLOP is solved for the free parametric vectors using a nonlinear optimization technique. Finally, the solution of the controller parameters is obtained using the solved free parametric vectors. The superiority of the proposed method over the conventional frequency domain method is demonstrated by designing controllers for three different systems and validating the controllers through nonlinear transient simulations. One of the cases includes design of a PSS for the Manitoba Hydro system having about 29,000 states variables, which demonstrates the applicability of the proposed algorithm for a practical real-world system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".