Spectroscopy and Spectral Analysis, Volume. 42, Issue 5, 1459(2022)
LIF Technology and ELM Algorithm Power Transformer Fault Diagnosis Research
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Fig. 5. Comparison of prediction results of ELM test set output models
Fig. 6. The relative error curve between predicted value and real value
Table 1. Performance comparison of ELM test set output models
View tableView in ArticleTable 1. Performance comparison of ELM test set output models
预处理 性能评价 KPCA-ELM PCA-ELM MSC 运行时间/s 0.5 3.296 9 平均相对误差(MRE) 0.009 6 0.011 8 均方误差(MSE) 0.000 7 0.001 1 均方根误差(RMSE) 0.027 1 0.033 9 拟合优度(R2) 0.999 41 0.999 19 SNV 运行时间/s 0.5175 3.343 8 平均相对误差(MRE) 0.017 4 0.018 5 均方误差(MSE) 0.001 3 0.001 5 均方根误差(RMSE) 0.036 0 0.038 3 拟合优度(R2) 0.999 08 0.998 83 Original 运行时间/s 0.578 1 3.484 4 平均相对误差(MRE) 0.024 1 0.035 0 均方误差(MSE) 0.004 0 0.005 3 均方根误差(RMSE) 0.063 2 0.073 0 拟合优度(R2) 0.996 95 0.996 02
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Peng-cheng YAN, Chao-yin ZHANG, Quan-sheng SUN, Song-hang SHANG, Ni-ni YIN, Xiao-fei ZHANG. LIF Technology and ELM Algorithm Power Transformer Fault Diagnosis Research[J]. Spectroscopy and Spectral Analysis, 2022, 42(5): 1459
Paper Information
Category: Research Articles
Received: Apr. 21, 2021
Accepted: --
Published Online: Nov. 10, 2022
The Author Email: YAN Peng-cheng (pcyan1988@126.com)
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