Evolutionary Based Optimal Power Flow Solution For Load Congestion Using PRNG

Evolutionary Based Optimal Power Flow Solution For Load Congestion Using PRNG

  IJETT-book-cover           
  
© 2021 by IJETT Journal
Volume-69 Issue-8
Year of Publication : 2021
Authors : Vijaya Bhaskar K, Ramesh S, Chandrasekar P
DOI :  10.14445/22315381/IJETT-V69I8P228

How to Cite?

Vijaya Bhaskar K, Ramesh S, Chandrasekar P, "Evolutionary Based Optimal Power Flow Solution For Load Congestion Using PRNG," International Journal of Engineering Trends and Technology, vol. 69, no. 8, pp. 225-236, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I8P228

Abstract
This paper presents a solution for optimal power flow by considering the random nature of load variations in a regulated electricity network. The algorithms are based on an evolutionary approach. Namely, Improved Learner Performance-based Behaviour algorithm (ILPB), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), and Harris Hawks Algorithm (HHO) are attempted to identify the best solution under random load variations. The concept of a pseudo-random number generator is used to represent the variations in load. The IEEE-30 and IEEE- 118 bus standard systems are considered in addition to the practical 62-bus Indian utility system to evaluate the performance of the algorithms. The systems are assessed with different objectives such as total fuel cost, total active power losses, total voltage deviation, and voltage stability index to achieve the optimal solution for the power flow problem. The purpose of all algorithms is to obtain the optimal solution by minimizing the fitness functions. Based on the optimal value of the solution and convergence characteristics of the test systems, the effectiveness and robustness of the algorithms are compared under random load conditions and definite raise and fall-off load conditions.

Keywords
Grey Wolf Optimization; Harris Hawks Optimization; Improved Learner Performance-based Behaviour algorithm; Optimal power flow; Whale Optimization Algorithm

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