scholarly journals Power loss minimization load flow studies using Artificial Bee Colony swarm intelligence technique

2021 ◽  
Vol 40 (4) ◽  
pp. 728-731
Author(s):  
Z.O. Jagun ◽  
M.B. Olajide ◽  
B.A. Wokoma ◽  
E.N. Osegi
Keyword(s):  
Power Systems ◽  
Swarm Intelligence ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Flow Analysis ◽  
Load Flow ◽  
Loss Minimization ◽  
Power Loss Minimization ◽  
Bee Colony ◽  
Intelligence Technique

This paper presents the capability of an emerging swarm intelligence technique for power loss minimization known as the Artificial Bee Colony (ABC) used in the context of an Alternative Load Flow Analysis (LFA) technique (ABC-LFA) for the solution of a power systems network. Studies are performed considering the effect of an important parameter of the ABC, the “maxcycle” on the LFA process; experiments are conducted by applying the ABC-LFA to the Western System Coordinated Council (WSCC) 3-machine 9- bus power system and a section of the Nigerian 132-kV power transmission network Port-Harcourt Region (NPHC-132), and the results reported. The results indicate that increasing the value of the ABC “maxcycle” parameter has a pronounced effect on the results obtained by the ABC-LFA. The results also indicate the sensitivity of the ABC to low values of maxcycle parameter.

scholarly journals Power Loss Minimization in Radial Distribution Systems with Simultaneous Placement and Sizing of Different Types of Distribution Generation Units Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 12 (3) ◽  
pp. 686-707
Author(s):  
Pavan Khetrapal ◽  
◽  
Jafarullakhan Pathan ◽  
Shivam Shrivastava ◽  
◽  
...  
Keyword(s):  
Power Loss ◽  
Artificial Bee Colony Algorithm ◽  
Distribution Systems ◽  
Artificial Bee Colony ◽  
Economic Incentives ◽  
The Other ◽  
Loss Minimization ◽  
Bee Colony ◽  
Different Types ◽  
Distribution Generation

Poweraloss is considered as one of theaimportant indicators used toaquantify theaperformance of distributionsnetworks. Minimisation of power lossesawith integration of distributedsgeneration (DG) unitssin distributionisystems has gained significantimomentum due to the associateditechno – economic incentives. Inithis paper, a noveliImproved Artificial Bee ColonyiAlgorithm (IABC) is developed toirobustly detect the optimalisite and sizedof DG units for minimisation of total poweralosses without violatingathe equality anddinequality constraints. Theiproposed algorithm is simulated in MATLABienvironment, and theieffectiveness of theialgorithm is validatedron IEEE – 34 bus andtIEEE – 69 bus radialrdistribution systems. Therperformance of thetproposed techniquerhas been validated by comparingsthe results obtained fromsother competesalgorithms. Comparisons showithat the proposed technique is moreiefficient in terms of simulationiresults of power loss andiconvergence propertyithan the other reportedialgorithms, suggesting that theisolution obtained is a globalioptimum.

Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network

2022 ◽  
pp. 728-748
Author(s):  
Gummadi Srinivasa Rao ◽  
Y. P. Obulesh ◽  
B. Venkateswara Rao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Load Flow ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.

Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network

2019 ◽  
pp. 35-55
Author(s):  
Gummadi Srinivasa Rao ◽  
Y. P. Obulesh ◽  
B. Venkateswara Rao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Load Flow ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.

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