Summary of Pre-insertion Resistors Temperature Prediction Based on Improved Woa-svr, by Honghe Dai et al.
Pre-insertion resistors temperature prediction based on improved WOA-SVR
by Honghe Dai, Site Mo, Haoxin Wang, Nan Yin, Songhai Fan, Bixiong Li
First submitted to arxiv on: 7 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Applied Physics (physics.app-ph)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The pre-insertion resistors (PIR) in high-voltage circuit breakers are crucial components that generate heat when an electric current flows through them. Elevated temperatures can lead to temporary closure failure or even PIR rupture. To accurately predict PIR temperature, this study combines finite element simulation techniques with Support Vector Regression (SVR) optimized by the Improved Whale Optimization Algorithm (IWOA). The IWOA includes Tent mapping, a convergence factor based on the sigmoid function, and the Ornstein-Uhlenbeck variation strategy. Compared to SSA-SVR and WOA-SVR models, the IWOA-SVR model achieved 90.2% and 81.5% prediction accuracy (above 100°C) in the 3°C temperature deviation range and 96.3% and 93.4% accuracy (above 100°C) in the 4°C temperature deviation range. This research demonstrates the proposed method can be used for online PIR temperature monitoring, preventing thermal faults, and facilitating circuit breaker opening and closing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps predict the temperature of pre-insertion resistors (PIR) in high-voltage circuit breakers. PIR generates heat when electric current flows through it, which can cause problems if temperatures get too high. To solve this problem, researchers combined special computer simulations with a machine learning technique called Support Vector Regression (SVR). They then used an optimization algorithm to make the SVR more accurate. The results show that their method is better than others at predicting PIR temperature. This research helps prevent overheating and allows for safe opening and closing of circuit breakers. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Regression * Sigmoid * Temperature
