Temperature extraction method for infrared image of high voltage power equipment

Abstract An infrared temperature prediction method for power equipment is proposed based on the radial basis function (RBF) network optimized by quantum genetic algorithm (QGA) and orthogonal least squares algorithm (OLSA). The modified compound algorithm was used to optimize parameters of the RBF network. A temperature prediction model was established through the fitting of pixels and temperatures of the infrared image of an equipment. After image matching, the infrared temperature at a position can be directly obtained from the visible image. Meanwhile, we can also directly read temperature values of different positions from the infrared image and identify the corresponding positions in the visible image. Experimental results indicate that the algorithm proposed has a better prediction performance than the RBF network optimized by OLSA alone and by adaptive genetic algorithm (AGA) and OLSA. It improves the generalization capacity of RBF network, resulting a more stable input and a higher prediction accuracy. The algorithm proposed facilitates temperature analysis and condition-based maintenance for substations.