Transient stability assessment of power system based on Bayesian deep learning

The gradual development of China's power grid has formed the current pattern of cross regional interconnected power grid with ultra-high/ultra-high voltage AC/DC hybrid connection, which has effectively promoted the optimized allocation and utilization of primary energy nationwide. However, with the rapid growth of model dimensions in the power system, traditional model driven power grid stability analysis and control decision-making techniques are difficult to achieve real-time perception of power grid safety and stability, and are prone to missing opportunities for power grid regulation. Therefore, there is an urgent need for a more effective power system transient stability assessment technology. This article proposes a transient stability assessment method for power systems based on Bayesian deep learning. Using Bayesian deep neural networks to learn and extract the data distribution of transient stability boundaries; Based on variational inference method, Bayesian deep neural network parameters are trained on big data to obtain an artificial intelligence based stability evaluation model for power grid faults. This model can more accurately predict the transient stability of power grid operation mode and objectively measure the reliability of artificial intelligence model prediction results, overcoming the shortcomings of traditional deterministic neural networks in easily misjudging stability in system critical states.