Optimal Placement and Sizing of Multiple DG Units using Improved Metaheuristic Algorithm on Transmission Systems

Vellingiri, Mahendiran; Sivaraju, S. S.

doi:10.1109/icirca51532.2021.9544723

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The objective function of this study is to minimize the active power losses by optimal placement and sizing of DG. The total active power loss is calculated as the summation of the active power loss of each problem formulation which expresses as in Eq (1) [9]:…”

Section: Objective Functionmentioning

confidence: 99%

See 1 more Smart Citation

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

Kuamthab,

Rasyidah Mohamad Idris

2024

Elektrika

View full text Add to dashboard Cite

In recent years, most growing nations worldwide have experienced an unavoidably rising demand for electricity, which may result in a dropping of the voltage profile and a reduction in system stability, both of which have the potential to overload the power generation system significantly. One of the ways to reduce loss system network in this study is by distributed generation (DG) allocation, which the DG optimal location and sizing are the two most significant considerations for integration of DG. Improper placement and DG sizing in the power system not only result in increased total power losses but also affect the electrical system’s capability to be function properly. This paper provides a comparative analysis of two meta- heuristic optimization techniques, that is, Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), to examine the optimal placement and sizing of distributed-generation (DG) for photovoltaic systems with the aim of minimizing the losses in interconnected distributed generation, IEEE-30 bus system. The objective function of this study is minimization the power loss with considered some constraints which are voltage limits and DG sizing limits are being covers in this study. The optimization issue has been solved by working with MATLAB/m-files software. The outcomes derived from the simulation results in this study demonstrated that PSO performance of two units of DG is better compared to EP algorithm. The voltage profile was improved drastically, and the power loss was reduced as lowest as 17.4486MW in the first case, 17.4368MW in the second case, and 17.4179MW in the third case.

show abstract

Section: Objective Functionmentioning

confidence: 99%

“…In PSO, the state of each particle is defined by two fundamental variables, namely velocity and position. The equation utilized for the updating of particle velocity and position is as in Eq (9) and Eq (10) [11] [12]:…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%