基于改进鲸鱼算法优化的接地网腐蚀速率预测

doi:10.16180/j.cnki.issn1007-7820.2025.05.002

Abstract

Abstract:

In order to improve the accuracy of predicting the corrosion rate of substation grounding grids and solve the problems of traditional BP(Back Propagation) neural networks easily falling into local optima and traditional algorithms randomly initializing populations affecting prediction accuracy, this study proposes an improved whale optimization algorithm to optimize the BP neural network for predicting the corrosion rate of substation grounding grids. An improved whale optimization algorithm is developed using chaotic mapping to initialize whale population, improving nonlinear factor to adjust adaptive weights, and enhancing search by Levay flight. Corrosion prediction model of grounding grid based on the improved whale optimization algorithm to optimize the BP neural network is established. The corrosion data of grounding network of 72 substations are simulated and analyzed. The results show that the average relative error of the improved model is 1.84%, the global maximum relative error is 3.86%, and the root-mean-square error is 0.139 02, which is significantly lower than that of the traditional model, proving the feasibility of the proposed model.

Key words: substation, grounding grid, corrosion rate, improved whale algorithm, BP neural network, prediction model, chaotic mapping, nonlinear factors

CLC Number:

TP18

ZHANG Haibo, PAN Pengcheng, ZHENG Feng. Corrosion Rate Prediction of Grounding Grid Based on Improved Whale Algorithm Optimization[J].Electronic Science and Technology, 2025, 38(5): 8-14.

Figures/Tables 6

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Table 1.

Table 2.

References 19

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预测模型	ε_RMSE	ε_MAPE/%	ε_MAX/%
策略1	0.335 27	3.34	5.76
策略2	0.284 01	2.87	4.91
策略3	0.263 76	2.51	4.82
IWOA	0.139 02	1.84	3.86

预测模型	ε_RMSE	ε_MAPE/%	ε_MAX/%
IWOA-BP	0.139 02	1.84	3.86
WOA-BP	0.352 01	3.57	6.51
BP	0.503 26	6.81	15.8
ISHO-BP	0.313 22	3.21	3.95
IWPA-BP	0.283 57	3.87	4.65

Corrosion Rate Prediction of Grounding Grid Based on Improved Whale Algorithm Optimization

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