Hybrid PIPSO-SQP Algorithm for Real Power Loss Minimization in Radial Distribution Systems with Optimal Placement of Distributed Generation

S. Angalaeswari, P. Sanjeevikumar, K. Jamuna and Zbigniew Leonowicz
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S. Angalaeswari: School of Electrical Engineering, Vellore Institute of Technology, Chennai Campus, Tamil Nadu 600127, India
P. Sanjeevikumar: Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark
K. Jamuna: School of Electrical Engineering, Vellore Institute of Technology, Chennai Campus, Tamil Nadu 600127, India
Zbigniew Leonowicz: Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50370 Wroclaw, Poland

Sustainability, 2020, vol. 12, issue 14, 1-21

Abstract: This paper proposes the hybrid sequential quadratic programming (SQP) technique based on active set method for identifying the optimal placement and rating of distribution generation (DG) incorporated in radial distribution systems (RDS) for minimizing the real power loss satisfying power balance equations and voltage limits. SQP runs quadratic programming sequentially as a sub-program to obtain the best solution by using an active set method. In this paper, the best optimal solution is selected with less computation time by combining the benefits of both classical and meta-heuristic methods. SQP is a classical method that is more sensitive to initial value selection and the evolutionary methods give approximate solution. Hence, the initial values for the SQP technique were obtained from the meta–heuristic method of Parameter Improved Particle Swarm Optimization (PIPSO) algorithm. The proposed hybrid PIPSO–SQP method was implemented in IEEE 33-bus RDS, IEEE 69-bus RDS, and IEEE 118-bus RDS under different loading conditions. The results show that the proposed method has efficient reduction in real power loss minimization through the enhancement of the bus voltage profile.

Keywords: radial distribution systems; sequential quadratic programming; Distributed Generation; power loss minimization; Parameter Improved Particle Swarm Optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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