Authors:
M. W. Siti
;
B. P. Numbi
and
D. Nicolae
Affiliation:
Tshwane Univeristy of Technology, South Africa
Keyword(s):
Network reconfiguration, Mixed-integer programming, Binary particle swarm optimization, Neural network.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Higher Level Artificial Neural Network Based Intelligent Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper uses artificial intelligent algorithms for reconfiguration of the distribution network. The problem is formulated as an optimization problem where the objective function to be minimized is the power losses, and the constraints are nodal voltage magnitude limits, branch current limits, Kirchhoff’s current law (KCL), Kirchhoff’s voltage law (KVL) and the network radiality condition. While the state (on-off) of the tie switch is considered as control or independent variable, the nodal voltage magnitude, branch current are considered as state or dependent variables. These state variables are continuous whilst the switch state is an integer (binary) variable. The problem being a mixed-integer programming one because of the state of switch (on=closed=1 or off=open=0), a Binary Particle Swarm Optimization (BPSO) and Neural Network are used separately to solve this problem. The effectiveness of proposed method is demonstrated through an example.