Moth-Flame Optimization Algorithm Based Load Flow Analysis of Ill-Conditioned Power Systems

Suvabrata Mukherjee and Provas Kumar Roy
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Suvabrata Mukherjee: NSHM Faculty of Engineering and Technology, Durgapur, West Bengal, India
Provas Kumar Roy: Kalyani Government College, Kalyani, West Bengal, India

International Journal of Applied Evolutionary Computation (IJAEC), 2020, vol. 11, issue 1, 1-27

Abstract: Using a novel bio-inspired optimization algorithm based on the navigation strategy of moths in a universe called transverse orientation, called the Moth-Flame Optimization Algorithm (MFOA), has been applied to solve the load flow problem for power systems under critical conditions. This mechanism is highly effective for traversing covering expanded radius in straight direction. As a matter of fact, moths follow a deadly spiral path as they get confused by artificial lights. For the tuning of parameters, both exploration and exploitation processes play an important role. MFOA is exercised for load flow analysis of small, medium, and large ill-conditioned power systems. The three different standard ill-conditioned cases considered in order to verify the robustness of the algorithm are IEEE 14-bus, IEEE 30-bus and IEEE 57-bus test systems. The results obtained by the application of MFOA shows that the algorithm is able to provide better results than the results obtained by the application of a biogeography inspired optimization algorithm, namely biogeography-based optimization (BBO) and a nature-inspired optimization algorithm, namely the whale optimization algorithm (WOA). This approves the superiority of the proposed algorithm. Simulation and numerical results demonstrate that the MFO is a potent alternative approach for load flow analysis under both normal and critical conditions in practical power systems especially in case of failure of conventional methods, thereby proving the robustness of the method. To the best of the authors' awareness, it is the first report on application of MFOA load flow analysis.

Date: 2020
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