Abstract

Optimal operational & control aspects of distribution networks have been a thrust research area in academics as well as in industries since last two-three decades. In day to day practice, every one of us uses services offered by public utility distribution networks namely, water distribution network, electrical power distribution network etc. Operational topology of power distribution networks are radial in nature and hence are termed as radial distribution networks (RDN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ). Network reconfiguration has been exercised as one of the prime and widely adopted approach for operational, maintenance and control activities of an RDN. Since last two decades, researchers have been using evolutionary computation based techniques (Genetic Algorithm, Simulating Annealing etc) for optimal network reconfiguration. The choices of network topology for the specific purpose/application requires a careful analysis of its merits and demerits. In RDN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> , the ultimate performance of a specific network topology is usually assessed by an iterative algorithm known as Load flow analysis (LFA) and its execution results in estimation of voltages, currents and losses profiles which in turn decides, whether the obtained network topology is good or bad. Thus, an obvious need is in favor of developing a conceptual frame work for faster load flow algorithm especially to meet near real-time operation requirements. In this paper, a simple and computationally efficient method for terminal (leaf) nodes identification is presented and thus, by integrating this subroutine in LFA definitely leads to an efficient and faster LFA algorithm.

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