Pre-insertion resistors temperature prediction based on improved WOA-SVR
Honghe Dai Site Mo Haoxin Wang Nan Yin Songhai Fan Bixiong Li
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.