Mitigation of power quality issues in smart grid using levy flight based moth flame optimization algorithm

摘要

Now a days, energy demand have been increasing due to industrialization factors in the world. The energy demand and environmental problems also increased such as global warming and air pollution. The problems must be solved by introducing renewable energy resources in grid connection. The increasing demand solved by combining renewable energy resources with grid connection which is the smart grid (SG) system. The SG system is affected by the voltage collapses and power quality (PQ) issues such as voltage sag, voltage swell, voltage interruption etc. Because of connection of renewable resources with grid connection. In the SG is consists of renewable energy systems (RES) and power storage devices. The RES are photovoltaic (PV), wind turbine (WT) that are connected with grid through the voltage source inverter (VSI). In the SG system, the power flow, power management and PQ are the main problems which must be solved to maintain stable operation. Thus, in this paper, Levy flight-moth flame optimization (LFMFO) is developed for improving the performance and mitigates the PQ issues in the SG system. To avoid the local optima and improve the global search of MFO, Levy flight is utlized in the SG system. Using the proposed algorithm, improve the performance of the SG with the voltage, droop control based low voltage ride through (LVRT) and damping controls for reduce the PQ issues. The proposed strategy is validated in the MATLAB/Simulink platform and investigated the PV power, wind power, voltage sag, current, voltage swell and voltage interruption. The proposed method shows the ability to mitigate PQ issues in SG system. The proposed methods provide the best optimal results toward to achieve objective of MG system. Under voltage sag, voltage swell and voltage interruption conditions of SG, proposed methods shows best performance to improve stability of the system. The execution of proposed system is shown and compared with the existing techniques of Cuckoo search (CS) algorithm and particle swarm optimization (PSO) algorithm.