scholarly journals Optimal reactive power flow solution in multiterminal AC-DC systems based on artificial bee colony algorithm

2014 ◽  
Vol 22 ◽  
pp. 1159-1176 ◽  
Author(s):  
Faruk YALÇIN ◽  
Uğur ARİFOĞLU
Keyword(s):  
Power Flow ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Flow Solution ◽  
Bee Colony ◽  
Reactive Power Flow ◽  
Optimal Reactive Power Flow

scholarly journals Minimizing Power Loss Using Modified Artificial Bee Colony Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 111-118
Author(s):  
Nur Azlin Ashiqin Mohd Amin ◽  
Siti Hafawati Jamaluddin ◽  
Nur Syuhada Muhammat Pazil ◽  
Norwaziah Mahmud ◽  
Norhanisa Kimpol
Keyword(s):  
Power System ◽  
Power Loss ◽  
Power Flow ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Control Variable ◽  
Test System ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Reactive Power Flow

Electrical energy losses are found in any part of the power system. In the power system, it is essential to minimize the real power loss in transmission lines. The voltage deviation at the load buses through controlling the reactive power flow is very important. This ensures the secured operation of power systems regarding voltage stability and the economics of the process due to loss minimization. In this paper, the Modified Artificial Bee Colony (MABC) algorithm is implemented to solve the power system's optimal reactive power flow problem. Generator bus voltages, transformer tap positions, and settings of switched shunt of compensators are used as decision variables to control the reactive power flow. These control variable values are adjusted for loss reduction. MABC algorithm is tested on the standard IEEE-30 bus test system. The results are compared with Firefly algorithm (FA) and Artificial Bee Colony (ABC) algorithm method to prove the effectiveness of the newest algorithm. The power loss results are quite productive, and the algorithm is the most efficient than the other methods such as ABC algorithm and FA algorithm. These results are produced by Matlab 2017b.

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