A novel Hybrid Harris hawk sine cosine optimization algorithm for reactive power optimization problem

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 l...

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Published in:	Journal of experimental & theoretical artificial intelligence Vol. 36; no. 6; pp. 901 - 937
Main Authors:	Jiao, Shangbin, Wang, Chen, Gao, Rui, Li, Yuxing, Zhang, Qing
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 17.08.2024 Taylor & Francis Ltd
Subjects:	Algorithms Convergence Distributed generation dynamic adaptive inertia weights Electric potential Electric power loss Harris hawk optimisation improved logistic chaotic mapping Optimization Rank tests Reactive power reactive power optimisation problem sine and cosine algorithm Trigonometric functions Voltage
ISSN:	0952-813X, 1362-3079
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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.
AbstractList	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.
Author	Gao, Rui Wang, Chen Li, Yuxing Jiao, Shangbin Zhang, Qing
Author_xml	– sequence: 1 givenname: Shangbin surname: Jiao fullname: Jiao, Shangbin organization: Xi'an University of Technology – sequence: 2 givenname: Chen orcidid: 0000-0002-2803-2632 surname: Wang fullname: Wang, Chen email: 1180311029@stu.xaut.edu.cn organization: Xi'an University of Technology – sequence: 3 givenname: Rui surname: Gao fullname: Gao, Rui organization: Baoji University of Arts and Sciences – sequence: 4 givenname: Yuxing surname: Li fullname: Li, Yuxing organization: Xi'an University of Technology – sequence: 5 givenname: Qing surname: Zhang fullname: Zhang, Qing organization: Xi'an University of Technology
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Snippet	Reactive power optimisation can effectively reduce active power loss and improve voltage quality, which is a great significance for power system planning. When...
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SubjectTerms	Algorithms Convergence Distributed generation dynamic adaptive inertia weights Electric potential Electric power loss Harris hawk optimisation improved logistic chaotic mapping Optimization Rank tests Reactive power reactive power optimisation problem sine and cosine algorithm Trigonometric functions Voltage
Title	A novel Hybrid Harris hawk sine cosine optimization algorithm for reactive power optimization problem
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