Author
Kola Sampangi Sambaiah
Bio: Kola Sampangi Sambaiah is an academic researcher from VIT University. The author has contributed to research in topics: Distributed generation & Renewable energy. The author has an hindex of 3, co-authored 6 publications receiving 52 citations.
Papers
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47 citations
TL;DR: The proposed Salp Swarm Algorithm (SSA) is based on the salps swarming behavior in oceans when navigating and foraging and aims at minimisation of power loss and voltage deviation in the distribution network.
Abstract: Recent studies show that the majority of the researchers have focused on either Distributed Generation (DG) allocation or network reconfiguration for enhancing distribution network performance. How...
44 citations
TL;DR: Electric power generated in a centralized station is transmitted through the transmission system and supplied to the consumer end through an electric distribution substation.
Abstract: Electric power generated in a centralized station is transmitted through the transmission system and supplied to the consumer end through an electric distribution substation. Electric power losses ...
11 citations
01 Jan 2020
TL;DR: A new hybrid gray wolf optimizer (HGWO) is proposed to solve the DG allocation problem and it is found that the proposed HGWO has more potency in terms of loss reduction and voltage stability enhancement compared to the existing techniques.
Abstract: Distributed generation (DG) allocation is the most promising source for reducing network loss and enhancing bus voltage stability in a distribution system. Because of the vast availability and nonpolluting character of renewable energy resource, it is gaining more attention nowadays. The most widely used renewable-based DG (RDG) is wind turbine (WT) and solar photovoltaic (SPV). Power generation patterns of the WT and SPV modules are random and nonlinear because the power output of WT and SPV modules are dependent on wind speed and solar irradiation. These require a probabilistic model to represent the actual power generation. The present paper reflects the potency of WT and SPV modules for reducing system losses and enhancing voltage stability. A new hybrid gray wolf optimizer (HGWO) is proposed to solve the DG allocation problem. The proposed optimization method is tested on IEEE 12- and 15-bus radial distribution system (RDS) and it is found that the proposed HGWO has more potency in terms of loss reduction and voltage stability enhancement compared to the existing techniques.
5 citations
27 Sep 2020
TL;DR: The proposed grasshopper optimization algorithm (GOA) a novel meta-heuristic optimization algorithm is used to solve the network reconfiguration problem in presence of distribution static compensator (D-STATCOM) and photovoltaic (PV) arrays in a distribution system and found that the optimal network reconfigured is more beneficial than individual objective optimization.
Abstract: In this paper, grasshopper optimization algorithm (GOA) a novel meta-heuristic optimization algorithm is used to solve the network reconfiguration problem in presence of distribution static compensator (D-STATCOM) and photovoltaic (PV) arrays in a distribution system. Here, D-STATCOM acts as distribution flexible ac transmission (D-FACT) device and PV arrays as decentralized or distributed generation (DG). The main purpose of the present research includes power loss minimization and voltage profile (VP) enhancement in radial distribution systems under different loading conditions. The proposed GOA is based on swarming behavior of grasshoppers in nature. The proposed GOA is validated using the standard 33, 69 and 118 – bus test systems. The simulation results proved that the optimal network reconfiguration in presence of D-STATCOM units and PV arrays leads to significant reduction in power loss and enhancement in VP. The results obtained by the proposed GOA are compared with base value and found that the optimal network reconfiguration in presence of D-STATCOM and PV arrays is more beneficial than individual objective optimization. Also, the proposed GOA is more accurate, efficient and reliable in finding optimal solution when compared to existing modified flower pollination algorithm (MFPA), firework algorithm (FWA), fuzzy-based ant colony optimization (ACO) and genetic algorithm (GA).
4 citations
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TL;DR: A comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others is presented in this article.
Abstract: Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.
98 citations
TL;DR: The result shows that the algorithms give better DG allocation and minimizes the power losses but at the nascent stage of advancement, which can be used as a reliable method in DG settings and sizing in distribution network system which produce better outputs than hybrid grey wolf optimization (GWO) and hybrid big bang big crunch.
67 citations
47 citations
TL;DR: Investigating the optimal sizing and placement of DGs in distribution networks with a novel concept to simultaneously minimize total energy cost along with total power loss and average voltage drop proves that proposed ABC algorithm mostly outperforms other algorithms.
47 citations
TL;DR: The results show that MPSO outperforms these techniques in terms of quality of solution, power loss reduction and voltage profile enhancement.
35 citations