Power System Transient Stability Assessment and Key Bus Set Selection Based on Multiple-agent Cooperation

Transient stability assessment (TSA) plays an important role in the safe operation of power systems. With the development of computer and intelligent technologies, machine learning is likely to be used for the TSA of modern power systems due to the improved arithmetic capability. This paper proposes a new multiple-agent cooperation method for transient stability assessment. With this model, the pre-AI is responsible for selecting the key bus set of the analyzed power system. Then, the phasor measurement unit data of the key bus set is used by post-AI to generates an accurate transient stability assessment result. The IEEE 39-bus system is adopted to verify the validity of this proposed method. Results show that the proposed method can significantly reduce the calculating time as well as ensure the transient stability assessment accuracy. The AI for AI working mode provides a new insight for the serial collaboration and application of multi-agent technologies in the power system.