Open AccessOptimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser
Author(s) -
Taha Ibrahim B. M.,
Elattar Ehab E.
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.0053
Subject(s) - sizing , mathematical optimization , ac power , computer science , electric power system , minimisation (clinical trials) , convergence (economics) , power (physics) , reliability engineering , voltage , mathematics , engineering , physics , quantum mechanics , art , statistics , economic growth , electrical engineering , economics , visual arts
In recent years, optimal sizing and location of reactive power resources are drawing much attention to help the operators of the utilities to enhance the power system operations. Therefore, this work presents a new version of grey wolf optimiser (GWO) to solve the problem of optimal reactive power resources sizing for power system operation enhancement. The proposed method which called improved grey wolf optimiser (IGWO) can be derived by modifying the exploration–exploitation balance in the conventional GWO to enhance its rate of convergence. Also, the weighted distance strategy is employed in the proposed IGWO to overcome the drawback of the conventional GWO. Optimal reactive power resources sizing problem is non‐linear and non‐convex optimisation problem. To solve this problem, different objective functions are used. These objective functions are minimisation of generating cost, minimisation of transmission power loss and voltage profile improvement. The validity and superiority of the proposed IGWO method are tested using three standard IEEE systems for normal and contingency conditions. Then the results are compared with those obtained from other recently published algorithms. The simulation results show that the proposed IGWO method is more accurate and efficient than other recently published algorithms.