An Interpretable Power System Transient Stability Assessment Method with
  Expert Guiding Neural-Regression-Tree
Hanxuan Wang Na Lu Zixuan Wang Jiacheng Liu Jun Liu
Deep learning based transient stability assessment (TSA) has achieved great
success, yet the lack of interpretability hinders its industrial application.
Although a great number of studies have tried to explore the interpretability
of network solutions, many problems still remain unsolved: (1) the difference
between the widely accepted power system knowledge and the generated
interpretive rules is large, (2) the probability characteristics of the neural
network have not been fully considered during generating the interpretive
rules, (3) the cost of the trade-off between accuracy and interpretability is
too heavy to take. To address these issues, an interpretable power system
Transient Stability Assessment method with Expert guiding
Neural-Regression-Tree (TSA-ENRT) is proposed. TSA-ENRT utilizes an expert
guiding nonlinear regression tree to approximate the neural network prediction
and the neural network can be explained by the interpretive rules generated by
the tree model. The nonlinearity of the expert guiding nonlinear regression
tree is endowed with the extracted knowledge from a simple two-machine
three-bus power system, which forms an expert knowledge base and thus the
generated interpretive rules are more consistent with human cognition. Besides,
the expert guiding tree model can build a bridge between the interpretive rules
and the probability prediction of neural network in a regression way. By
regularizing the neural network with the average decision length of ENRT, the
association of the neural network and tree model is constructed in the model
training level which provides a better trade-off between accuracy and
interpretability. Extensive experiments indicate the interpretive rules
generated by the proposed TSA-ENRT are highly consistent with the neural
network prediction and more agreed with human expert cognition.