Determining the intensity and occurrence location of faults in transformers using frequency response analysis (FRA) with novel multistage optimization algorithm and SVMD decomposition technique
Bibliographic record
Abstract
frequency response analysis (FRA) has become a worldwide accepted technique for detecting winding and core deformation in transformers. The main weakness of this technique is its reliance on the level of expertise and experience of personnel and the lack of standards and automatic codes. It is necessary to create reliable FRA interpretation codes for the high frequency transformer model that can implement the frequency characteristics of real transformers in a wide frequency range. This paper presents an artificial intelligence method to estimate these parameters from the FRA diagram of the transformer. In the proposed method, a three-step optimization algorithm is implemented on the real data of a 33 kV disc winding to find intensity and occurrence location of faults. At first, the frequency response amplitude signal is decomposed into oscillating modes using SVMD (Successive Variational Mode Decomposition), the output of which is much less complicated than the original signal. In the next stage and in the optimization process, whose decision variables are the RLC values of the detailed (lumped) model of the transformer, the frequency response of the modeled circuit decomposition is also obtained, and the new meta-heuristic algorithm of SSO (Shark Smell Optimization) based on the ability to hunt sharks in nature, it will search for the optimal solution by minimizing the error between the actual and modeled winding frequency response. This process is implemented gradually and with the addition of each oscillatory mode in each stage. The accuracy of the proposed method is evaluated with the data of the tests performed on a 33 kV high voltage disc winding to estimate the parameters of their high frequency electrical equivalent circuit in normal and fault conditions. The results show that the proposed method can estimate the parameters of the equivalent circuit with high accuracy and help to interpret the FRA diagram based on the numerical changes of these parameters.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".