Effect of Multi-DG Installation to Loss Reduction in Distribution System

• Main Authors: Nur Zahirah Mohd Ali, Ismail Musirin, Saiful Izwan Suliman, Ngah Ramzi Hamzah, Zuhaina Zakaria
• Format: Article
• Language: English
• Published: ESRGroups 2016-03-01
• Series: Journal of Electrical Systems
• Subjects: Artificial intelligence; Adaptive Embedded Clonal Evolutionary Programming; loss minimization
• Online Access: http://journal.esrgroups.org/jes/papers/12_1_13.pdf

Since last decade, Artificial Intelligence (AI) methods have been used to solve complex DG problems because in most cases, they can provide global or near global solution. The major advantage of the AI methods is that they are relatively versatile for handling various qualitative constraints. AI methods mainly include Artificial Neural Network (ANN), Expert System (ES), Genetic Algorithm (GA), Evolutionary Programming (EP), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The purpose of this paper is to present a new technique, namely Adaptive Embedded Clonal Evolutionary Programming (AECEP). The objective of the study is to employ AECEP optimization techniques for loss minimization. This technique was developed to optimally determine the location and sizing of DG. The IEEE 41- Bus RTS was implemented for testing several cases in terms of loading conditions.