Improvement of Power and Energy Disturbances in AC Micro Grid Using Discrete Wavelet Based Extended Neural Network

Popularity of micro grids increases day by day by facilitating distribution energy generations (DGs) and it forms a remote consumer based integrated energy system. The AC micro grid is one of the popular energy supply network in the remote areas. The renewable energy sources plays important role in AC Micro Grids and popularly known as distributed generations (DGs). The multiple DG integration, coordination and energy management in the operational transition in MG system is highly challenging task. During this DG integration and inclusion of conventional Grid creates the disturbance in system dynamics. The conventional controllers are not sufficiently strengthened to support the dynamic disturbance in Micro Grid (MG). In present research, various improved neural networks approaches have been implemented which gives promising solutions to improve micro grid operation performance along with power/energy quality problems. Thus In this paper a new method based on Extended Neural network (ENN) based on discrete wavelet transform, has been applied to improve the energy disturbances in terms of power quality problems for AC Micro Grid with DG integration. This research aims to investigate the power quality enhancement such as sag/swell, oscillatory transient. Also the dynamics is investigated through a single line to ground fault (LG) fault and load change. A micro grid consisting of three PV cells operated in parallel with an battery energy storage system simulated in MATLAB/SIMULINK environment. The results depicted that the proposed controller performs better than the conventional PI controller.