期刊
IET GENERATION TRANSMISSION & DISTRIBUTION
卷 14, 期 23, 页码 5480-5490出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2020.0504
关键词
control system synthesis; voltage control; learning (artificial intelligence); genetic algorithms; voltage regulators; power system control; rotors; neurocontrollers; fuzzy control; power system stability; multimachine power system; artificial neural network coordinator; brain emotional learning coordinator; two-area four-machine power system; power system signals; intelligent coordinators; automatic voltage regulator; power system stabiliser; PSS; AVR; fuzzy logic coordinator; sine-cosine algorithm; scaling factor; rotor speed; terminal voltages; acceleration power; transient error; steady-state error; performance objectives
This study presents the design of intelligent coordinators for the automatic voltage regulator (AVR) and power system stabiliser (PSS) in a multi-machine power system. The intelligent coordinators are designed to update the gains of AVR and PSS in severe disturbances to guarantee the stability of the studied power system. Three potent intelligent coordinators are proposed: (a) fuzzy logic coordinator, (b) artificial neural network coordinator, and (c) brain emotional learning coordinator. Since the intelligent coordinators are based on the knowledge of the experts, desirable scaling factors are considered in the output signals of the coordinators to achieve optimal results. The scaling factors are optimised using a new evolutionary approach known as the sine-cosine algorithm. To evaluate the efficiency of the proposed intelligent approaches, the performances of coordinators are analysed on a two-area four-machine power system. A range of power system signals, such as rotor speed, terminal voltages, acceleration power and rotor angle of generators are demonstrated to approve and compare the performance of the intelligent coordinators. The simulation results indicate that the intelligent coordinators can guarantee the stability of the power system and satisfy performance objectives, such as desired transient and steady-state errors.
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