Extreme outage prediction in power systems using a new deep generative Informer model
Bibliographic record
Abstract
• We propose a deep generative model for power data rebalancing and outage prediction. • We propose a deep generative model for data rebalancing in power systems. • We propose a deep model embedded via a classification-specific loss function. • Two-step classification approach has been used in the proposed outage predict. • Proposed method enhances the prediction accuracy considering its imbalanced data. Extreme weather events have made growing concerns over electric power grid infrastructure as well as the residents living in disaster areas. Moreover, the potential damages due to the extreme events can make serious challenges for supply reliability and security, leading to widespread power outages in power systems. This paper proposes a deep learning-based framework for power data rebalancing and outage prediction in power systems to cope with the extreme events. To this end, we propose an Adaptive Wasserstein Conditional Generative Adversarial Network for data generation. Also, we propose a new Wasserstein Bidirectional Generative Adversarial Network with the Informer model, embedded in both the Generator and Discriminator Networks, plus an Encoder Network for the outage prediction in power systems. Two-step classification approach has been used in the proposed outage prediction model: classifying the power grid components into impacted and non-impacted categories and classifying the impacted category into in-service and out-of-service categories. In addition, a new classification-specific loss function is proposed for the minimax objective function of the Vanilla Generative Adversarial Network to improve the prediction performance in the latent space. Evaluation results of the proposed model and 15 comparative models in three groups using six evaluation metrics on a real-world test case demonstrate the superiority of the proposed model compared to all comparative models. These results confirm that the proposed outage prediction model can be effectively employed for accurately predicting extreme outages in power systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".