Pregled bibliografske jedinice broj: 1143793
AVR and PSS Coordination Strategy by Using Multi- objective Ant Lion Optimizer
AVR and PSS Coordination Strategy by Using Multi- objective Ant Lion Optimizer // IEEE Xplore - 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO)
Opatija, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2020. str. 1151-1156 doi:10.23919/mipro48935.2020.9245128 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
AVR and PSS Coordination Strategy by Using Multi-
objective Ant Lion Optimizer
Autori
Špoljarić, T. ; Pavić, I.
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
IEEE Xplore - 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO)
/ - : Institute of Electrical and Electronics Engineers (IEEE), 2020, 1151-1156
Skup
43rd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2020)
Mjesto i datum
Opatija, Hrvatska, 28.09.2020. - 02.10.2020
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Generator Excitation Controls ; Multi-objective optimization ; Ant Lion Optimizer ; Power System Dynamics
Sažetak
In this paper a novel optimization method called Multi-Objective Ant Lion Optimizer (MOALO) is proposed for tuning synchronous generator excitation controls in multi machine power system. Devices used in excitation control are automatic voltage regulator (AVR) and power system stabilizer (PSS). Two area four machine model (TAFM) is used for observing power system dynamics through several various operating states. In a performance analysis of a proposed algorithm two objective functions are used. First objective function uses integral of time weighted absolute error of rotor speed, voltage and tie line active power data, while second objective function uses mean value of time domain transitional process quality indicators such as overshoot, undershoot and settling time. A proposed algorithm is tested and its performance is compared with performances of two other multi objective swarm intelligence algorithms: Multi- Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Salp Swarm Algorithm (MOSSA). Results are compared and presented as sets of solutions composed in Pareto fronts.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Tehničko veleučilište u Zagrebu