TY - GEN
T1 - Locating voltage collapse points using evolutionary computation techniques
AU - Goh, S. H.
AU - Dong, Z. Y.
AU - Saha, T. K.
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2007
Y1 - 2007
N2 - In recent years, Evolutionary Computation (EC) techniques have proven to be an useful alternative approach for solving many highly nonlinear power system planning and operation problems. The objective of this paper is to investigate mathematically-complex voltage collapse problems using EC techniques, in particular the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms. It demonstrates the exceptional searching capabilities of both the PSO and DE algorithms to locate voltage collapse point solutions (also widely known as nose points or critical points), which are at least comparable to those obtained using the well-known Continuation Power Flow (CPF) technique. The feasibility and practicality of this approach has been tested on a 3-machine 9-bus, the IEEE 118-bus and the IEEE 300-bus power systems. © 2007 IEEE.
AB - In recent years, Evolutionary Computation (EC) techniques have proven to be an useful alternative approach for solving many highly nonlinear power system planning and operation problems. The objective of this paper is to investigate mathematically-complex voltage collapse problems using EC techniques, in particular the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms. It demonstrates the exceptional searching capabilities of both the PSO and DE algorithms to locate voltage collapse point solutions (also widely known as nose points or critical points), which are at least comparable to those obtained using the well-known Continuation Power Flow (CPF) technique. The feasibility and practicality of this approach has been tested on a 3-machine 9-bus, the IEEE 118-bus and the IEEE 300-bus power systems. © 2007 IEEE.
UR - https://www.scopus.com/pages/publications/79951649116
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-79951649116&origin=recordpage
U2 - 10.1109/CEC.2007.4424843
DO - 10.1109/CEC.2007.4424843
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1424413400
SN - 9781424413409
T3 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
SP - 2923
EP - 2930
BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
Y2 - 25 September 2007 through 28 September 2007
ER -