Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 415-420 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781479979936 |
| DOIs | |
| Publication status | Published - 22 Jul 2015 |
| Event | 15th IEEE International Conference on Environment and Electrical Engineering, EEEIC 2015 - Rome, Italy Duration: 10 Jun 2015 → 13 Jun 2015 |
Publication series
| Name | 2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings |
|---|
Other
| Other | 15th IEEE International Conference on Environment and Electrical Engineering, EEEIC 2015 |
|---|---|
| Country | Italy |
| City | Rome |
| Period | 10/06/15 → 13/06/15 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Energy Engineering and Power Technology
Cite this
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Voltage collapse risk index prediction for real time system's security monitoring. / Aminudin, N.; Rahman, T. K.A.; Muhamad Razali, Noor Miza; Marsadek, Marayati; Ramli, N. M.; Yassin, M. I.
2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 415-420 7165198 (2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Voltage collapse risk index prediction for real time system's security monitoring
AU - Aminudin, N.
AU - Rahman, T. K.A.
AU - Muhamad Razali, Noor Miza
AU - Marsadek, Marayati
AU - Ramli, N. M.
AU - Yassin, M. I.
PY - 2015/7/22
Y1 - 2015/7/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84943138723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84943138723&partnerID=8YFLogxK
U2 - 10.1109/EEEIC.2015.7165198
DO - 10.1109/EEEIC.2015.7165198
M3 - Conference contribution
AN - SCOPUS:84943138723
T3 - 2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings
SP - 415
EP - 420
BT - 2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -