Robust Dynamic State Estimation of Power System Using Imperialist Competitive Algorithm
Bibliographic record
Abstract
Robust real-time state estimation in power systems is regarded as the first and foremost exigency in controlling and managing a safety network. Using prediction operations, dynamic state estimation (DSE) is considered as an applicable and efficient means for online tracking and monitoring of a network. In this paper, a new criterion has been introduced for DSE of power system to maximize the posterior probability density function. Furthermore, a comprehensive dynamic model consistent with information available from the network will be recommended. The dynamic state estimator proposed in this paper applies an imperialist competitive algorithm (ICA) to minimize the presented criterion, which is considered as constraints of the dynamic model of the network. The presented method is more advantageous than other state estimation methods, which are based on Kalman and statistical methods including independence to system linearity, Gaussian noise, and biopsy procedure. Moreover, according to considerations recommended for forming imperialists in ICA and predictable property of the suggested method, this state estimator is robust against data falsification. To compare, the presented method and other dynamic state estimators were implemented for an IEEE 9-bus system. Simulation results proved efficiency and superiority of the proposed method in robust estimation and fast tracking of network states.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".