Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

This paper describes a newly developed Moth-Flame optimization algorithm to deal with optimal reactive power dispatch problem. The prime intention of reactive power dispatch problem is to curtail the real power loss and control the bus voltages in power system network. The Moth-Flame algorithm is one of the most powerful and robust new global optimization algorithms in engineering. The primary aim of this advanced algorithm is quick convergence rate owing to the roulette wheel election method. This algorithm deals with numerous nonconvex, non-linear, continuous &amp; discrete variable reactive power optimization problems. The Moth-Flame optimization is established by flow of moths in the direction from source to flame. This optimization algorithm employs a standard IEEE-30 bus &amp; 57 bus system to attain the optimal settings of regulating variables from reactive power compensating components. As a result of the regulating variables, the prime intentions for system network can be achieved. The outcome solutions and results are notable in comparison with other well-known algorithms reported in journals. © Research India Publications.

Department of Electrical Engineering

School of Electrical Engineering

Vellore Campus

Operational Research

Optimal reactive power dispatch using water wave optimization algorithm

This research accesses a novel approach of utilising an advanced Meta–heuristic Optimization technique with a single objective to pledge with optimal reactive power dispatch problem in electrical power system network. The prime focus of reactive power dispatch is to curtail the total active power loss in transmission lines. In this detailed study, the dragonfly algorithm was realized on standard IEEE-14 bus and 30 bus systems. The outcome of dragonfly algorithm lucidly indicate the capablity of increasing the antecedent random population size for a liable global optimization problem, focalized close to the global optimum and contributing precise outcome results related to another popular algorithm. © 2018, Gazi University Eti Mahallesi. All rights reserved.

World University root node

Gazi University Journal of Science

A new meta heuristic dragonfly optimizaion algorithm for optimal reactive power dispatch problem

This paper discusses an improved colliding bodies optimization algorithm to pledge competently to realize optimal reactive power dispatch problem. In large scale power system, optimal reactive power dispatch problem is a huge constraint as it deals with large range of non-linear and non-convex global optimization problems involving the composite of continuous &amp; discontinuous control variables. The reactive power dispatch problem is formulated by reactive power supply components like generator output voltages, regulating transformers and reactive power compensating devices. In order to get the optimal values of control variables, a robust optimization algorithm has been tested for reactive power dispatch problem. The prime objectives of reactive power dispatch problems are: to lessen the real power loss in transmission lines, reduce the congestion in transmission lines, to maintain the specified voltage magnitude in all the buses in power system network for both normal &amp; abnormal operating conditions. This is achieved by satisfying the set of specified operational constraints. This algorithm was well-established on single-dimensional collisions intervening bodies, with every operator result being evaluated by substance with mass. Later, the collision of impelling bodies with titled masses &amp; velocities showed that these objects were distinct with new impetus. This collision response prompts the operators to act in almost fine point in the search area. The optimization of the colliding bodies forward simple formations to catch minimum or maximum of functions. The algorithm was applied on standard IEEE - 57 bus system &amp; the outcome solutions are correlated with lately matured algorithms like cuckoo search, particle swarm optimization and gravitational search algorithms. © 2017 IEEE.

2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)

Optimal reactive power dispatch problem solved by an improved colliding bodies optimization algorithm

This paper introduces a newly developed symbiotic organism search algorithm for deal with ORPD problem. The reactive power optimization problem is essential for suitable operation and regulation in power system network. It helps operators to control the voltage limits, to curtail the real power loss in transmission lines, enhances the strength of the electrical power system to withstand and counteract voltage collapse during load variations in an electrical power system. The symbiotic organism search algorithm is one of the assuring, latest developments in the tract of Meta heuristic algorithms. The texture - animated philosophy of symbiotic organism search algorithm resembles the interactive nature among organisms in feature. Organisms in the real cosmos infrequently live in isolation due to their dependence on other organisms for livelihood and longevity. This ORPD problem is formulated by generator output voltages (continuous variable), regulating transformers and switchable VAR devices (discrete variables). The proposed symbiotic organism search algorithm is realized for IEEE-14 bus and 30-bus systems. The improved result values are compared with Evolutionary programming, Differential evolutionary algorithm, dynamic particle swarm optimization, self-adaptive real coded genetic algorithm and modified Gaussian bare bones teaching learning based optimization. © 2017 IEEE.