Faculty Publications
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Item An improved mechanism of leaf node identification for radial distribution networks(2011) Sharma, D.P.; Chaturvedi, A.; Purohit, G.; Prasad, G.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 (RDNs). 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 [1], 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 RDNs, 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. © 2011 IEEE.Item Distributed load flow analysis using graph theory(2011) Sharma, D.P.; Chaturvedi, A.; Purohit, G.; Shivarudraswamy, R.In today scenario, to meet enhanced demand imposed by domestic, commercial and industrial consumers, various operational & control activities of Radial Distribution Network (RDN) requires a focused attention. Irrespective of sub-domains research aspects of RDN like network reconfiguration, reactive power compensation and economic load scheduling etc, network performance parameters are usually estimated by an iterative process and is commonly known as load (power) flow algorithm. In this paper, a simple mechanism is presented to implement the load flow analysis (LFA) algorithm. The reported algorithm utilizes graph theory principles and is tested on a 69- bus RDN.Item Distributed load flow analysis using graph theory(2011) Sharma, D.P.; Chaturvedi, A.; Purohit, G.; Shivarudraswamy, R.In today scenario, to meet enhanced demand imposed by domestic, commercial and industrial consumers, various operational & control activities of Radial Distribution Network (RDN) requires a focused attention. Irrespective of sub-domains research aspects of RDN like network reconfiguration, reactive power compensation and economic load scheduling etc, network performance parameters are usually estimated by an iterative process and is commonly known as load (power) flow algorithm. In this paper, a simple mechanism is presented to implement the load flow analysis (LFA) algorithm. The reported algorithm utilizes graph theory principles and is tested on a 69- bus RDN.Item Faster load flow algorithm for radial distribution network using graph theory(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019) Sharma, D.P.; Chaturvedi, A.; Saxena, R.; Raguru, J.Since last 3 decades, load flow solutions have enjoyed success on different fronts. Primarily, the importance and utility of these algorithms is assessed using performance measures, which usually include issue like implementation complicacy, optimized execution time, and memory storage. In this work, a graph-theoretical approach is used to facilitate load flow solutions for a static network topology. Algorithm is tested for 2 different radial distribution topologies, and its deployment for both of these network finally results in phenomenal saving on 2 important algorithm performance measures, ie, time and space complexity. Obtained phenomenal saving for both of these 2 parameters is compared with earlier reported work on statistical basis. © 2018 John Wiley & Sons, Ltd.