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In recent years, solar energy is effectively utilized as an alternate energy source for generating electricity. Maximum Power Point Tracking is applied to the photovoltaic (PV) system to extract maximum power (MP). Numerous algorithms have been developed and implemented. One such algorithm is Particle Swarm Optimization algorithm(PSO).This article introduces a novel PSO algorithm with Cauchy distribution to track MP from the PV system. It is designed to overcome the drawback of slow convergence rate of conventional PSO. Parameters required for conventional PSO are inertial weight, acceleration coefficients, and a number of particles. In case of CPSO, tuning parameter is the number of particles. In order to increase the convergence speed, Cauchy distribution is used instead of normal distribution function to generate the random numbers. The advantage of this algorithm is to provide the global best-optimized result with faster convergence speed. It has the ability to track the MP in extreme climatic conditions with varying loads. The proposed method outperforms than standard PSO and some of the existing methods in terms of quick convergence. The effectiveness of this algorithm is evaluated and verified with the existing methods using MATLAB/SIMULINK simulation.

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