A review of recent AI applications in next-generation power electronics
Bibliographic record
Abstract
Power electronics (PELS) is an important part of modern technology by enabling energy conversion, control, and management, which is essential for powering renewable energy systems (RES), electric vehicles (EVs), industrial automation, and advanced consumer electronics, thereby driving sustainability and innovation. Consequently, the efficiency and performance of PELS components are always in priority. To this end, the potential applications of generative and non-metaheuristic AI-driven algorithms in the control, maintenance, and design of PELS systems are reviewed in this paper. Covering a spectrum of models, including generative adversarial networks (GANs), neural networks (NNs), quantum neural networks (QNNs), fuzzy interfaces, and reinforcement learning (RL), this paper investigates their applications in control, maintenance, and design of PELS. The review also provides the implications of big data, generative, and non-metaheuristic AI-driven algorithms in efficiency enhancement, reliability, and sustainability of PELS-integrated energy infrastructure. Also, some of the recently funded projects in the U.S. and Europe, as well as the associated challenges and future research directions, are investigated in this emerging sector.
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 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".