The pre-insertion resistors (PIR) within high-voltage circuit breakers are critical components and warm up by generating Joule heat when an electric current flows through them. Elevated temperature can lead to temporary closure failure and, in severe cases, the rupture of PIR. To accurately predict the temperature of PIR, this study combines finite element simulation techniques with Support Vector Regression (SVR) optimized by an Improved Whale Optimization Algorithm (IWOA) approach. The IWOA includes Tent mapping, a convergence factor based on the sigmoid function, and the Ornstein-Uhlenbeck variation strategy. The IWOA-SVR model is compared with the SSA-SVR and WOA-SVR. The results reveal that the prediction accuracies of the IWOA-SVR model were 90.2% and 81.5% (above 100$^\circ$C) in the 3$^\circ$C temperature deviation range and 96.3% and 93.4% (above 100$^\circ$C) in the 4$^\circ$C temperature deviation range, surpassing the performance of the comparative models. This research demonstrates the method proposed can realize the online monitoring of the temperature of the PIR, which can effectively prevent thermal faults PIR and provide a basis for the opening and closing of the circuit breaker within a short period.

翻译：高压断路器中的预插入电阻（PIR）是关键元件，电流流经时因产生焦耳热而升温。温度升高可能导致PIR暂时性闭合故障，严重时甚至引发PIR爆裂。为精确预测PIR温度，本研究将有限元仿真技术与改进鲸鱼优化算法（IWOA）优化的支持向量回归（SVR）相结合。IWOA融合了Tent映射、基于Sigmoid函数的收敛因子以及奥尔斯坦-乌伦贝克变异策略。将IWOA-SVR模型与SSA-SVR及WOA-SVR模型进行对比。结果表明：在3°C温度偏差范围内，IWOA-SVR模型对100°C以上温度的预测准确率分别为90.2%和81.5%；在4°C温度偏差范围内，准确率分别达到96.3%和93.4%，均优于对比模型。本研究证明，所提方法可实现PIR温度的在线监测，有效预防PIR热故障，并为断路器短时内的分合闸操作提供依据。