Multidimensional Time-Series Life Cycle Costs Analysis of Intelligent Substation

Comprehensive and accurate life cycle costs (LCC) of intelligent substations are conducive to improving operation efficiency and reliability of power transmission. To reduce maintenance costs and enhance assets utilization, multiple dimensional time-series LCC analysis of intelligent substation based on principal component analysis long-term short-term memory (PCA-LSTM) is proposed in this article. This article first discusses the differences between smart substations and conventional substations in LCC analysis. Because it has an advantage over the recurrent neural network (RNN) in handling gradient vanishing and explosion, long-term–short-term memory (LSTM) is used instead of RNN. Considering that LCC is a comprehensive study which contains large number of variables, principal component analysis (PCA) is utilized to deal with the data dimensionality reduction preprocessing. Results show that PCA-LSTM performs better in root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), as 0.182, 0.154 and 15.84%, respectively. In our case, the computation time is about half that of the pure LSTM. The calculation efficiency is
$2.2\times $
compared to the original.