TY  - JOUR
VL  - 3
EP  - 32
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857056396&doi=10.1016%2fj.swevo.2011.11.002&partnerID=40&md5=dff178194ebbfc0150dcddbb546ce9a9
JF  - Swarm and Evolutionary Computation
A1  - Sahoo, N.C.
A1  - Ganguly, S.
A1  - Das, D.
SN  - 22106502
Y1  - 2012///
KW  - Distribution network; Distribution systems; Electrical distribution system; Multi objective; Multi objective particle swarm optimization; Multi-objective planning; Multiple objectives; Network reliability; Nondominated solutions; Operational costs; Optimal values; Optimum number; Pareto dominance; Particle swarm; Planning algorithms; Power distribution system planning; Sectionalizing switch; Solution strategy
KW  -  Electric load distribution; Electric power distribution; Multiobjective optimization; Particle swarm optimization (PSO)
KW  -  Local area networks
TI  - Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization
SP  - 15
ID  - scholars3041
N2  - A multi-objective planning approach for electrical distribution systems using particle swarm optimization is presented in this paper. In this planning, the number of feeders and their routes, number and locations of sectionalizing switches, and number and locations of tie-lines of a distribution system are optimized. The multiple objectives to determine optimal values for these planning variables are: (i) minimization of total installation and operational cost and (ii) maximization of network reliability. The planning optimization is performed in two steps. In the first step, the distribution network structure, i.e., number of feeders, their routes, and number and locations of sectionalizing switches are determined. In the second step, the optimum number and locations of tie-lines are determined. Both the objectives are minimized simultaneously to obtain a set of non-dominated solutions in the first step of optimization. The solution strategy used for the first step optimization is the Strength Pareto Evolutionary Algorithm-2 (SPEA2) based multi-objective particle swarm optimization (SPEA2MOPSO). In the second step, the solutions/networks obtained from the previous step are further optimized by placement of tie-lines. SPEA2-based binary MOPSO (SPEA2BMOPSO) is used in the second step of optimization. The proposed planning algorithm is tested and evaluated on different practical distribution systems. © 2011 Elsevier B.V. All rights reserved.
N1  - cited By 88
AV  - none
ER  -