Predictive Analysis and Correction Control of CCT for a Power System Based on a Broad Learning System

Abstract

Transient stability is an important factor for the stability of a power system. With improvements in voltage levels, and the expansion of power network scales, the problem of transient stability is particularly prominent. When a power system circuit fails, if the operation time of the relay protection device is higher than the critical clearing time (CCT), the relay protection device cannot cut the fault line in a timely manner. It is essential to forecast and adjust the CCT to improve the stability of the system; therefore, a method is proposed in this paper to predict and evaluate the critical clearing time using the broad learning system (BLS). The sensitivity of the critical clearing time can be easily calculated based on the prediction results of the critical clearing time using BLS. Moreover, the critical clearing time can be modified using the BLS correction control model. The proposed method was verified using a 4-machine 11-node system and a 10-machine 39-node system. According to the experimental results, the proposed model can predict, evaluate, and correct the CCT very well.

Correction Control Model of L-Index Based on VSC-OPF and BLS Method

“…To quickly assess the voltage stability and correct the voltage instability state of power systems, reference [34] established the prediction and linear correction model of the L-index based on BLS. Similarly, reference [35] used the BLS to conduct the CCT prediction analysis and correction control of power systems. Reference [36] proposed a state evaluation method of power systems based on BLS which improved the ability of power systems to quickly respond to emergencies.…”