Transmission line status portrait and assessment based on bidirectional long short-time memory networks

Abstract

To improve the transmission line status evaluation accuracy， the paper proposes a transmission line status portrait and assessment model based on clustering and later regression. Firstly， the self-organizing neural network （SONN） is designed to reduce the dimensionality of the original data of the transmission lines and to adaptively extract several types of representative feature information without manual feature extraction and selection of the number of clusters based on subjective experience. Secondly， the representative data is fed into the LSTM （long short-term memory） networks. The networks combine forward learning and reverse learning to conduct bidirectional training， evaluate the model， establish the nonlinear mapping relationship between the core data and the transmission line state， and improve the evaluation accuracy of the state of the transmission line in the power grid scenario. The experimental results show that the model proposed in this paper has achieved good evaluation results on the actual data set； specifically， it is superior to conventional support vector machine， artificial neural network， sparse automatic encoding machine， and other methods in evaluation accuracy.

Keywords

• transmission line status assessment
• bidirectional long short-term memory network
• self-organizing neural network
• dimension reduction