基于ESLE采样和径向基函数响应面法的变压器静电环结构优化设计

doi:10.19595/j.cnki.1000-6753.tces.221608

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Abstract The transformer main insulation structure needs be reasonably designed to ensure its safe and reliable operation. The RSM (response surface method) is an efficient tool to the optimization problem of transformer main insulation structure. In the field of traditional transformer structure optimization, the RSM is widely used due to its superior optimization accuracy and efficiency in comparison to the empirical formula method and the global optimization algorithm. This paper proposes combining the ESLE (Enhanced Successful Local Enumeration) method with upgraded sampling uniformity and the RBF(radial basis function)-RSM with superior nonlinear fitting capability to solve the main insulation structure optimization problem, whose optimization objective is to improve the minimum insulation margin, so as to obtain a more effective design scheme for the transformer main insulation structure.
Firstly, a 500kV transformer is modeled using the APDL( ANSYS Parametric Design Language) . Secondly, it is necessary to identify optimization variables, which are selected from the electrostatic ring structure. Subsequently, the training set and test set data are obtained using the ESLE sampling method. Then, the RBF-RSM is derived using the training set data. Finally, the resulting RSM is optimized by an intelligent algorithm, which can figure out the variables’ values with the minimum insulation margin. Due to the parametric modeling, the training set and test set objective function results are obtained via Matlab and ANSYS calls, which significantly reduces the human workload and enhances the engineering applicability of the above process.
The results of the optimization based on ESLE and RBF-RSM are compared with the pre-optimized results, and the minimum insulation margin is improved by 12.56%, which indicates that the proposed method has practical utility. In order to verify the advantages of the proposed approach, firstly, from the sampling method, the ESLE method is compared to the LHS sampling method in RBF-RSM optimization, and the usage of the ESLE method with superior uniform sampling improves the optimization accuracy by 218 times. Secondly, from the response surface model, the optimization results of quadratic polynomial RSM and RBF-RSM are compared in ESLE sampling. The RBF-RSM enhances the optimization accuracy by a factor of 78 in addition to lowering the model's prediction error. Finally, the optimization results and those of the GA (genetic algorithm) are compared. The RSM yields an optimization result of 1.8525, the GA yields a result of 1.8514 and the values of the optimization variables in both schemes are consistent. Therefore, the accuracy of the RSM's optimization findings is validated. In addition, the RSM takes only 19.3 hours, whereas the GA requires 194 hours. It is evident that the RSM has significantly increased optimization efficiency while ensuing precision.
Through the above comparative analysis, the following conclusions can be drawn: 1) Compared with LHS sampling, the prediction error and optimization error of the model under ESLE sampling are less than those under LHS sampling, which verifies the feasibility of ESLE sampling method applied to the optimization of transformer main insulation structure. 2) Compared to the quadratic polynomial RSM, the predictive ability and optimization accuracy of the model proposed have been enhanced, indicating that the RBF-RSM has a proper fitting effect and calculation accuracy, and is more suitable for engineering applications. 3) Compared to GA, the precision of the optimization results of both methods is comparable, however, the efficiency of the method proposed is 10 times that of GA, which can effectively solve the optimization problem of the main insulation structure of transformers.

Key words： Response surface method (RSM) enhanced successful local enumeration (ESLE) radial basis function (RBF) insulation margin transformer structural optimization

Received: 22 August 2022

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TM403.3

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	Liu Gang
	Gao Chenglong
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	Zhu Zhangchen
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Liu Gang,Gao Chenglong,Hu Wanjun等. Optimized Design of Transformer Electrostatic Ring Based on Radial Basis Function Response Surface Method With Enhanced Successful Local Enumeration Sampling[J]. Transactions of China Electrotechnical Society, 0, (): 78-78.

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