Wavelets and artificial neural networks in power system transient classification and short-term power load prediction
Bibliographic record
Abstract
This thesis focuses on applications of wavelets and artificial neural networks in power system transients analysis, modelling, classification, and short-terrn power load prediction.A power system transient classification system framework based on wavelet transform preprocessing and probabilistic neural networks (PbfN) is proposed in the thesis.A new type of neural networks, resource allocating networks (RAN), which can adjust its computing structure dynamically, is also investigated for the short-terrn power load prediction.Wavelet modelling of power system transients is studied by examining (i) the capability of multiresolution analysis, (ii) time-scale representation of transient signals, and (iii) accurate detection and compact representation of transient signals, which are useful for power system tran- sient recording, storing, and classification.The PNN is used as a classifier in the proposed transient classification system.Experimental results show that the PNN has a great speed advantage in its training over backpropagation neural networks (BPN), which makes it a good candidate for real-time usage.The wavelet trans- form preprocessing of the transient signals improved the performance of the PNN, and demon- strated the feasibilitv of the real-time transients recordins and.automatic classification.The RAN is studied for short-terrn power load prediction because of the capability of RAN's adjusting its computing structure dynamically, which makes it a good candidate for mod- elling nonstationary signals.Experimental results on real data from Manitoba Hydro revealed that the short-tenn power load prediction is more accurate than other published results.
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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 | 0.000 |
| 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 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".