ABSTRACT
Reactive power optimisation can effectively reduce active power loss and improve voltage quality, which is a great significance for power system planning. When the reactive power optimisation problem is solved by Harris Hawk optimisation (HHO) algorithm, there are slow convergence and falling into local optima. This is caused by the multiple random parameters in HHO’s exploration phase. To solve this problem, the Improved Logistic Chaotic mapping, Sine and Cosine Algorithm (SCA), the dynamic adaptive inertia weights and greedy strategy are introduced; the aim is to speed up convergence, reduce blind spots and improve the search capability. The improved algorithm was tested on the classical 23 benchmark functions; Wilcoxon’s signed-rank test and Friedman test were tested, the results show that the improved algorithm can obtain better performance. The improved algorithm is applied to the reactive power optimisation problem in distribution networks with distributed generators (DG). When the reactive power optimisation problem is solved by the improved algorithms HHO, WOA, CSO, CS and PSO, respectively, the improved algorithm can obtain the lowest active power loss. Compared with no optimisation, active power loss is reduced by 33.19%. Finally, the node voltage quality ensures the safe operation of the system.
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The authors report no declarations of interest.
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Shangbin Jiao
Shangbin Jiao was born in 1974. He received the B.S., M.S. and Ph.D. degree from the College of Automation and Information Engineering, Xi’an University of Technology, Xi’an, in 1997, 2002 and 2006. He is currently a professor and doctoral supervisor in Xi’an University of Technology. His general research interests include weak signal detection, evolutionary computation, device intelligence status detection and early fault diagnosis. E-mail addresses: [email protected]
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Chen Wang
Chen Wang received the bachelor degree in Electrical engineering and its automated power system direction from Xi’an University of Technology, Xi’an, China, in 2013. He received the master degree in Power System and Automation from Hubei Minzu University, Enshi, China, in 2018. He is currently pursuing the Ph.D. degree in pattern recognition and intelligent systems with the school of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China. His current research interests include evolutionary algorithms and multi-objective optimization. E-mail addresses: [email protected]
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Rui Gao
Rui Gao female, born in November 1980, master, associate professor, College of Electronic and Electrical Engineering, Baoji University of Arts and Sciences. The main research direction is weak signal detection. E-mail addresses: [email protected]
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Yuxing Li
Yuxing Li received the bachelor degree in communication engineering from Inner Mongolia University of Technology, Hohhot, China, in 2007. He received the master degree in signal and information processing from Northwestern Polytechnical University, Xi’an, Chin-a, in 2012. He received the doctor degree in acoustics from Northwestern Polytechnical University, Xi’an, China, in 2018. He is a lecturer in school of automation and information engineering, Xi’an University of Technology, Xi’an, China. His research interests include underwater acoustic signal processing and entropy. E-mail addresses: [email protected]
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Qing Zhang
Qing Zhang was born in 1974. She received the B.S. and M.S. degree from the College of Automation and Information Engineering, Xi’an University of Technology, Xi’an, in 1997 and 2007. She is currently a senior engineer at the Engineering Research Center in Xi’an University of Technology. Her main research interests include fault diagnosis, weak signal detection and engineering. E-mail addresses: [email protected]