| Title | Frequency Security Assessment for Receiving-end System Based on Deep Learning Method |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Ma, C., Wang, L., Gai, C., Yang, D., Zhang, P., Zhang, H., Li, C. |
| Conference Name | 2020 IEEE/IAS Industrial and Commercial Power System Asia (I CPS Asia) |
| Date Published | jul |
| Keywords | Asia, belief networks, Conferences, Cyber-physical systems, deep belief network, frequency security assessment, HVDC blocking, industrial power systems, pubcrawl, Resiliency |
| Abstract | For hours-ahead assessment of power systems with a high penetration level of renewable generation, a large number of uncertain scenarios should be checked to ensure the frequency security of the system after the severe power disturbance following HVDC blocking. In this situation, the full time-domain simulation is unsuitable as a result of the heavy calculation burden. To fulfill the quick assessment of the frequency security, the online frequency security assessment framework based on deep learning is proposed in this paper. The Deep Belief Network (DBN) method is used to establish the framework. The sample generation method is researched to generate representative samples for the purposed of higher assessment accuracy. A large-scale AC-DC interconnected power grid is adopted to verify the validity of the proposed assessment method. |
| DOI | 10.1109/ICPSAsia48933.2020.9208491 |
| Citation Key | ma_frequency_2020 |
Frequency Security Assessment for Receiving-end System Based on Deep Learning Method