A new chaos and global competitive ranking-based symbiotic organisms search algorithm for solving reactive power dispatch problem with discrete and continuous control variable

Yalcin E., ÇAM E., TAPLAMACIOĞLU M. C.

ELECTRICAL ENGINEERING, cilt.102, sa.2, ss.573-590, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 102 Sayı: 2
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s00202-019-00895-6
  • Dergi Adı: ELECTRICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Compendex, INSPEC, DIALNET
  • Sayfa Sayıları: ss.573-590
  • Anahtar Kelimeler: Optimal reactive power dispatch, Symbiotic organisms search, Adaptive chaotic symbiotic organisms search, Power loss minimization, Voltage profile improvement, PARTICLE SWARM OPTIMIZATION, ANT LION OPTIMIZER, MINIMIZATION, SYSTEMS, REAL
  • Gazi Üniversitesi Adresli: Evet

Özet

In this paper, optimal reactive power dispatch problem (ORPD) is solved by using a new chaos and global competitive ranking-based symbiotic organisms search algorithm (A-CSOS). SOS is an effective meta-heuristic algorithm, especially for optimization problems with continuous variable, with important features such as the absence of any user-defined algorithmic parameters and the easily applicable. However, some essential features of SOS such as trap into local optima and slow convergence problems need to be improved in order to find better solutions for more complex, nonlinear, multi-modal optimization problems such as ORPD. In this study, to solve ORPD and enhance the capability of the standard SOS even further, A-CSOS algorithm is developed. To test the performance of the developed algorithm in ORPD, the both SOS and the proposed A-CSOS are applied to the two different objective functions including power loss minimization and total voltage deviation minimization in IEEE 57-, 118-, 300-bus power systems. According to the results of ten different test cases, the proposed method gives better solutions up to 15.3% and 40.52% than the state-of-art algorithms and SOS, respectively. Moreover, the convergence performance of A-CSOS is considerably better than all tried algorithms. The effectiveness of A-CSOS for solving ORPD and other complex constrained optimization problems is proofed by this study.