Convergence Enhancement for Neural Network Integrated Power System Time Domain Simulation

Neural network (NN) dynamics for component modeling in power system is becoming an effective approach to improve model accuracy. However, poor convergence occurs in neural network integrated-time domain simulation (NNI-TDS) with existing numerical methods. To address this challenge, this work studies the impact of NN dynamics on convergence mathematically and develops an integrated fictitious admittance (FA) and successive over-relaxation (SOR) method, which can significantly enhance convergence and shorten simulation time. First, Norton's theorem-guided decoder is proposed to NN dynamics for FA calculation, and convergence of NNI-TDS can be efficiently enhanced by rebuilding network equations with FA. Then, an NN dynamics-targeted SOR method that considers characteristics of each NN dynamics is proposed for iteration acceleration. Especially, the optimal SOR factors are adaptively determined without additional calculation burdens. Numerical test results on two standard test systems (39 and 2383 buses) and a practical East China Power Grid (5075 buses) illustrate the effectiveness of proposed method. Specifically, faster-than-real-time simulation is realized in test systems even with 100 NN dynamics integrated.