A New Hybrid Cuckoo Search-Artificial Bee Colony Approach for Optimal Placing of UPFC Considering Contingencies

Volume 6 Issue 1 February - April 2018

Research Paper

A New Hybrid Cuckoo Search-Artificial Bee Colony Approach for Optimal Placing of UPFC Considering Contingencies

Siva Sankar Akumalla*, Bharath Kumar Polineni **, Sujatha Peddakotla ***

* Senior Lecturer, Department of Electrical and Electronics Engineering, Government Polytechnic, Proddatur, Kadapa, Andhra Pradesh , India.

** Lecturer, Department of Electrical and Electronics Engineering, JNTUA College of Engineering, Ananthapuramu, Andhra Pradesh, India.

*** Professor, Department of Electrical and Electronics Engineering, JNTUA College of Engineering, Ananthapuramu, Andhra Pradesh, India.

Akumalla, S, S., Polineni, B. K., and Peddakotla, S. (2018). A New Hybrid Cuckoo Search-Artificial Bee Colony Approach for Optimal Placing of UPFC Considering Contingencies. i-manager’s Journal on Power Systems Engineering, 6(1), 26-34.https://doi.org/10.26634/jps.6.1.14308

Abstract

The Optimal Power Flow (OPF) is a specialised area of power system, that generally requires solving nonlinear optimization problems. The OPF formulation, when includes generators' operating constraints and Flexible AC Transmission Systems (FACTS) devices becomes profoundly complicate multi-objective optimization problem. To target the multi-objective solution, Metaheuristic algorithms have been favored as they give promising results in many such optimization cases. Herein, developing and evaluating a new hybrid method, such as a blend of two simulation-based metaheuristic methods, Cuckoo-Search (CS) and Artificial Bee Colony (ABC) algorithms was focussed. This approach helps the engineer in best locating Unified Power Flow Controller (UPFC), the versatile FACTS' controller, in a multimachine power system to preserve voltage stability and reduce the line power losses under contingencies. The CS algorithm, which was first presented in the mid of 2009 as a novel optimization technique, is motivated by the compel behavior, clearly, brood parasitism of the cuckoos. Here, the ABC feature alters the levy flight behavior and, in consequence, the searching ability of the CS algorithm is enhanced. The test results, when examined on IEEE 30-bus benchmark system, reveal the notable going of the combined approach over the original CS method.