Mechanism Enhanced Data-Driven Method for Reliability Improvement of Load Margin Estimation

The data-driven method for security monitoring is promising for power system voltage stability estimation, whereas the reliability of such method is greatly affected due to the generalizability weakened by operational variability. In this article, a mechanism knowledge enhanced machine learning method for load margin estimation is established by integrating power system mechanism into intelligent neural network (NN) model. Firstly, the correlation between the load power factor and load margin explored from curve P-V were analyzed, from which the mechanism knowledge of voltage stability is extracted. Then, the mathematical logical representation of mechanism knowledge is given, which was further integrated into the loss function of the NN model. The proposed enhanced model constrains the NN model to provide mechanistically consistent results, as well as helps achieve an excellent generalizability performance. The effectiveness of the proposed method is verified by numerical simulations using the IEEE 39-bus test system and the IEEE 145-bus test system, the results of which shows that the generalization ability of the enhanced NN is significantly improved with the proposed method, thus the accuracy, reliability and robustness of the load margin estimation are greatly enhanced.