Abstract
This study proposes a new integrated technique for efficient distributed generator (DG) installation and scaling in distribution systems. The Biogeography-Based Optimisation (BBO) algorithm and an Artificial Neural Network(ANN) are combined in the suggested integrated approach. The BBO algorithm determines the location of the DG. BBO is a subset of evolutionary computation that is used to address a range of combinatorial optimization problems, mostly in the transportation, location, and scheduling domains. The system is optimised using the load bus's voltage stability indices, power loss and active power. The artificial neural network (ANN) is a form of artificial intelligence (AI) methodology for estimating DG capacity. The network is trained according to the system's target using the back-propagation training technique. In the MATLAB programming environment, the proposed integrated technique is implemented to identify the optimal location of DG and ideal Size of the DG to obtain stable voltage with reduced real power losses. The proposed methodology was evaluated using the IEEE 30 bus system's major distributed bus (load bus).