New strategy based modified Salp swarm algorithm for optimal reactive power planning: a case study of the Algerian electrical system (114 bus). Issue 20 (10th October 2019)

• Record Type: Journal Article
• Authors: Mahdad, Belkacem; Kamel, Srairi
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 20(2019)
• Page Start: 4523
• Page End: 4540
• Publication Date: 2019-10-10
• Subjects: load dispatching -- power system planning -- particle swarm optimisation -- search problems -- genetic algorithms -- reactive power -- static VAr compensators; Salp swarm algorithm -- optimal reactive power planning -- Algerian electrical system -- load growth -- practical power systems -- intelligent strategy -- reactive power dispatch -- total power loss -- total voltage deviation -- original algorithm -- test systems -- grouped swarms -- SSA_PG -- active power -- SSA_VG -- SSA_Ti -- SSA_SVC -- intelligent planning strategy -- normal condition -- load faults -- electrical test system -- modern power systems reliability; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.5772
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Optimal reactive power planning is an important task for experts and industrials to ensure the reliability of modern power systems. Actually, the structure of practical power systems becomes dynamic and characterised by uncertainty in load and non‐linear characteristic of various elements of power systems such as constraints associated to thermal units, constraints associated with FACTS devices and renewable sources. This study introduces an intelligent strategy based new metaheuristic named Salp swarm algorithm (SSA) to improve the solution of reactive power dispatch by optimising the total power loss, the total voltage deviation individually and simultaneously considering static VAR compensators (SVCs). To improve the efficiency of the original algorithm in solving large test systems, a sub SSA is formed to optimise the various objective functions based on a grouped control variable. In this study, four grouped swarms named SSA_PG for active power, SSA_VG for voltages, SSA_Ti for Tap transformers, and SSA_SVC for SVCs are formed to operate in a flexible structure to minimise a specified objective function. The proposed intelligent planning strategy validated on the IEEE 30 bus and to the large electrical test system 114 Bus of the Algerian network at normal condition and considering critical situations such as margin loading stability and contingency. Results found using the proposed strategy compared to those cited recently in the literature proves its particularity in terms of solution quality and convergence characteristics.