Application of Multi-objective Gaussian Water Cycle Algorithm in Optimal Power Flow Problems

In order to surmount the shortcomings of the standard water cycle algorithm in solving the non-convex optimal power flow (OPF) problem, a multi-objective Gaussian water cycle algorithm (MOGWCA) is proposed. Three multi-objective OPF simulation experiments are used to demonstrate the property of the...

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Vydáno v:Chinese Automation Congress (Online) s. 410 - 414
Hlavní autoři: Chen, Gonggui, Han, Ying, Qian, Jie
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 06.11.2020
Témata:
Fuels
Gaussian mutation mechanism
Load flow
optimal power flow
Optimization
Rivers
Sociology
Sorting
Statistics
water cycle algorithm
ISSN:2688-0938
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Shrnutí:In order to surmount the shortcomings of the standard water cycle algorithm in solving the non-convex optimal power flow (OPF) problem, a multi-objective Gaussian water cycle algorithm (MOGWCA) is proposed. Three multi-objective OPF simulation experiments are used to demonstrate the property of the MOGWCA, which improves the global development capability and population diversity through the Gaussian mutation mechanism. Test cases considering fuel cost, emissions, active power loss and voltage deviation are applied on IEEE 30 and IEEE 57 test systems. A large number of outcomes display that the MOGWCA algorithm can get a well-distributed Pareto front (PF) and effectively solve the multi-objective OPF problem.
ISSN:2688-0938
DOI:10.1109/CAC51589.2020.9327426