Abstract:
In recent years, solar energy is effectively
utilized as an alternate energy source for generating
electricity. Maximum Power Point Tracking(MPPT)
is applied to the photovoltaic (PV) system to extract
maximum power (MP). Numerous algorithms are
developed and implemented to extract the MP under
varying environmental conditions. One such
algorithm is the 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 Cauchy PSO(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 that it provides the
global best-optimized result at a faster
convergence speed. It has the ability to track the MP
in extreme climatic conditions with varying loads.
The proposed method outperforms the standard PSO
and some of the existing methods in terms of quick
convergence and also has a simplified structure.
