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Energy demand taking a bigger leap day by day, Renewable energy gets the  most leading importance in catering the purpose. Solar being the abundantly available renewable energy resource solar panels are key components in harnessing solar energy. Solar energy is the most dependable and cheap energy in renewable sector. But harnessing solar energy with partial shading makes it difficult for simple tracking algorithm because of multiple power peak points. Settling time of DC link voltage during the dynamics in the load and the irradiation also plays a major role in power delivered to the grid . Highly dynamic situation aware processors have been in the verge for many applications where large amount of online processing is a need like the smart grid, which needs a faster online reacting time. This paper deals with such an online reacting Maximum Power Point Optimization (MPPO) on a PV system with Partial shaded condition (PSC). The MPPO uses the recent non-parametric optimization techniques like Particle Swarm Optimization (PSO) for maximizing the power delivered from the solar panel. This optimization is achieved by populating the duty cycle and K p and K i parameters of PI controller given to the DC-DC converter connected to the PV arrays for the stable supply to the grid. While applying the maximization algorithm for the solar power output from the PV arrays the PSC conditions are considered in order to make the control technique more robust. This paper deals with minimization of DC link voltage settling time and maximization of power in multi-objective,. MATLAB based simulation is carried and the comparative inference is produced in this paper. The simulation is developed for the 2KW PV array with the proposed method. The simulation carried out had performed better with the proposed method than the single objective method.Satisfactory results were observed both in the simulation of the proposed algorithm

Maximum power point optimization for a grid synchronized PV system considering partial shaded condition using multi-objective function