A conquest for Science and also Technology and also the ever-growing planet of innovation trigger numerous innovations. Currently India demandingly picking to beat non-renewable fuel sources sparsity issues along with Renewable Energy Sources (RES). Renewable Energy Sources needs intricate innovations for the usage. Renewable resource resources and also modern technologies possess prospective to supply options to the enduring electricity troubles being actually experienced due to the establishing nations. The renewable resource resources like wind power, solar power, geothermal power, electricity, biomass electricity and also energy tissue innovation may be made use of to get over electricity lack in India. To overcome the power demand for such a fast-growing economic climate, India will certainly need an ensured source of 3- 4 times extra power than the overall power consumed today. This work reviews different control techniques and proposes a new control technique Optimal Recurrent Neural Networks (ORNN) based Controller to mitigate the power quality (PQ) disturbances of power system. In the proposed approach, optimal weight selection is employed for enhancing the learning procedure of RNN (ORNN). Here, ORNN technique is utilized for selecting the ideal control signal of grid inverter through optimal adjustments of the control variables in the power system. The proposed strategy creates the ideal control of the grid inverter which tries to enhance the power quality of a power system and manage the line voltage by providing reactive power compensation. This paper reviews several papers with different control strategies applied to the grid connected inverter.

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