基于POD-RBF代理模型和特征点KNN校正的电力舱温度反演方法

Inverse temperature estimation of power cabin based on POD-RBF proxy model and feature point KNN correction

为解决数值模拟方法计算不同工况下电力舱整体温度时算力需求大、适应性差等问题,本文提出了一种基于POD-RBF代理模型和特征点KNN校正的电力舱温度反演方法。该方法基于仿真计算得到的不同工况下电力舱温度场数据,利用本征正交分解和径向基函数方法构建电力舱的温度反演代理模型,以避免重复计算,从而快速得到仿真模型的近似解。同时,使用K最近邻算法将特征温度点引入反演模型中,以此校正温度反演误差,提高反演的准确性和适应能力。以实际电力舱为例,对指定工况下的电力舱进行了温度反演。结果表明,该方法可以在电力舱内电缆通流情况以及特征温度点温度已知的情况下实现电力舱的实时温度反演,其反演温度与仿真计算温度的最大相对误差为0.96%,满足工程运用标准。

This paper proposes a power cabin temperature inversion method based on POD-RBF surrogate model and feature point KNN correction,aiming to address the challenges of high computational complexity and poor adaptability in numerical simulation methods for calculating the temperature distribution of power cabin under different working conditions.The proposed method utilizes simulated temperature field data obtained from various operating conditions to construct a temperature inversion model for the power cabin using the proper orthogonal decomposition(POD)and radial basis function(RBF)approach.This surrogate model effectively avoids redundant calculations,enabling rapid approximation of the simulated model.Meanwhile,the K-Nearest Neighbor(KNN)algorithm is employed to introduce the feature temperature points into the inversion model for correcting the temperature inversion error and improving the accuracy and adaptability of the in-version.Taking an actual power cabin as an example,the temperature inversion of the power cabin under the specified working condition is conducted.The results show that the proposed method can achieve real-time temperature inversion of the power cabin under the condition of cable current flow and known temperature of feature temperature points,with the maximum relative error between the inversion temperature and the simulation calculation temperature of 0.96%,meeting the engineering application standard.