Neural Networks Based Lyapunov Functions for Transient Stability Analysis and Assessment of Power Systems

This article presents the Lyapunov functions based on the artificial intelligence (AI) method for transient stability assessment and the determination of stability region (SR). First, Lyapunov stability theory and the definition of SR are introduced. Then, the characteristics of neural networks as a general function approximator are employed as the Lyapunov function learner, and the Lyapunov function is constructed combined with stochastic gradient descent (SGD). Then, the falsifier's task is to find the state vectors that violate Lyapunov stability conditions, and the counterexampleswould be added to the training set for the function learner to accelerate convergence. After obtaining the Lyapunov function of power system, the estimation of SR boundary can be represented by the maximum level set of Lyapunov function. Finally, the IEEE 9-bus 3-machine system is used as test system to demonstrate the validity and effectiveness of the proposed construction method of Lyapunov functions for power system transient stability analysis.

Automated summary

This article introduces the application of Lyapunov functions based on artificial intelligence (AI) method in transient stability assessment and determination of stability region (SR). The characteristics of neural networks as general function approximators are employed to construct the Lyapunov function combined with stochastic gradient descent (SGD). The proposed construction method of Lyapunov functions is validated and proved effective through tests on a IEEE 9-bus 3-machine system.