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Multiobjective optimal power flow using interior search algorithm: A case study on a real‐time electrical network
Author(s) -
Chandrasekaran Shilaja
Publication year - 2020
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12312
Subject(s) - power flow , computer science , electric power system , stability (learning theory) , voltage , mathematical optimization , power (physics) , algorithm , electrical network , reliability engineering , engineering , mathematics , electrical engineering , machine learning , physics , quantum mechanics
Optimal power flow (OPF) is a vital concern in an electrical network. In consequence of the intricacy of the power systems, the conventional formulations are not adequate for current situation. Hence, in this study, the multiobjective OPF (MOOPF) problem has been modeled to diminish the production cost, environmental emission, and losses and to enhance the voltage stability and voltage profile simultaneously. This study proposes the application of interior search algorithm (ISA) for resolving MOOPF problem. The simulations have been carried out on three various test systems such as IEEE 30‐bus system, IEEE 57‐bus system, and Tamil Nadu Generation and Distribution Corporation Limited, as a real part of 62 bus Indian utility system (IUS) to infer the efficacy of ISA in solving the OPF problems. The simulation results have been compared with other techniques. The comparison shows that ISA is used in resolving MOOPF problems.