Simulation of Overheat Detection for Combined Power Transmission and Transformation Equipment Based on Chaos Search
Power transmission and transformation equipment group is often in a state of heating,which will seri-ously affect its life cycle.To solve the problem of low accuracy of heating detection of combined power transmission and transformation equipment,this paper proposes a parameter optimization algorithm based on Tent chaotic mapping and population evolution,and constructs the MPTTE target detection model and CPTTE overheating detection model on the basis of SVM.Firstly,the environmental temperature noise and the environmental background noise are reduced through image graying,interpolation and equalization processing;secondly,the MPTTE class is preliminarily identified by adopting an R-CNN algorithm,meanwhile,the thermal characteristics of a target are extracted by con-structing an energy function,and an MPTTE data set is divided into a training set and a test set;and then that Tent chaotic map algorithm is utilized to improve the subsequent parameter optimization capability of the population evolu-tionary algorithm.The optimal solutions of gamma and C parameters of SVM are explored,and the SVM-MPTTE tar-get detection model is constructed.Finally,the MPTTE overheated target is located by Pearson correlation coefficient analysis and mean shift clustering algorithm,and the CPTTE overheated detection model is constructed.The simulation results of ablation experiments show that both the MPTTE target detection model and the CPTTE overheat detection model have positive optimization effects after using different optimization algorithms.The simulation results show that compared with the other three kinds of baseline algorithms,the comprehensive performance of the proposed model is improved by 1.03%,and it has the highest accuracy.The algorithm proposed in this paper has high accuracy and stability in equipment heating detection.
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