Deep-learning Based Modeling of Fault Detachment Stability for Power Grid
This addresses power grid reliability for grid operators by identifying a specific instability risk in backup protection scenarios.
The researchers tackled the problem of power grid stability during fault detachment by developing a deep learning model to analyze N-1 backup protection scenarios, finding that such events destabilize the system approximately 2.5% of the time.
The project intends to model the stability of power system with a deep learning algorithm to the problem, aiming to delay the removal of the fault. The so-called "fail-delay cut-off" refers to the occurrence of N-1 backup protection action on the backbone network of the system, resulting in longer time for the removal of the fault. In practice, through the analysis and calculation of a large number of online data, we have found that the N-1 failure system of the main protection action will not be unstable, which is also a guarantee of the operation mode arrangement. In the case of the N-1 backup protection action, there is an approximately 2.5% probability that the system will be destabilized. Therefore, research is needed to improve the operating arrangement.