Online assessment of transient power angle & voltage in power system: Realizing fast prediction and fast update in any scenarios

Abstract

Transient voltage and transient power angle are in the same time scale, both of which are the basis of safety operation of the power system. However, there are few studies on the integration of power angle and voltage at present. How to quickly realize the integrated assessment of voltage & power angle and how to shorten the update period when the model accuracy drops are the problems that need to be solved urgently. Therefore, this paper proposes a fast assessment scheme that considers both transient voltage and transient power angle. In this study, the stable boundary between the transient power angle and the transient voltage is first constructed from the time dimension by the variable step dichotomy. And then, a convolutional neural network with multiscale residual squeeze excitation (MRSE-CNN) is proposed, which can assess the voltage and power angle without post-fault-clearance data. It only takes three sampling points to accurately learn the mapping between the input feature and the stable boundary. At the same time, the results of whether the transient voltage and the transient power angle are stable, and the corresponding margin are output. By introducing the improved Huber loss function to dynamically adjust the cost of misjudgment and missing judgment, the reliability of the model is further enhanced. In the online application, a pool-based active transfer learning is proposed for the unlearned scenarios under load, topology, and renewable energy, which greatly reduces the adaptive update time of the model in unlearned scenarios. The model is verified in the improved IEEE 39 bus system and provincial interconnection system. It shows that the proposed method can quickly and accurately realize the integrated adaptive assessment of transient power angle and voltage in any scenarios.

Keywords

• Critical clearing time (CCT)
• Transient angle stability
• Transient voltage stability
• Squeeze excitation
• Active learning
• Transfer learning