Voltage collapse risk index prediction for real time system's security monitoring

• Published in: 2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings
• Main Author: Aminudin N.; Rahman T.K.A.; Razali N.M.M.; Marsadek M.; Ramli N.M.; Yassin M.I.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2015
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943138723&doi=10.1109%2fEEEIC.2015.7165198&partnerID=40&md5=8e5ba84d5d8cf9d1a08b969fd3bb6299

Risk based security assessment (RBSA) for power system security deals with the impact and probability of uncertainty to occur in the power system. In this study, the risk of voltage collapse is measured by considering the L-index as the impact of voltage collapse while Poisson probability density function is used to model the probability of transmission line outage. The prediction of voltage collapse risk index in real time requires precise, reliable and short processing time. To facilitate this analysis, Artificial Intelligent using Generalize Regression Neural Network (GRNN) technique is proposed where the spread value is determined using Cuckoo Search (CS) optimization method. To validate the effectiveness of the proposed method, the performance of GRNN with optimized spread value obtained using CS is compared with heuristic approach. © 2015 IEEE.