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122 CHAPTER 4 Performance of MPPT Techniques of Photovoltaic Systems

two sensors to measure the PV array voltage and current from which power is
computed or only one voltage sensor and the PV array current from the PV array
voltage is estimated.
The tuning factor of the change in the duty ratio of modified HC technique can be
obtained from the following equation [23]:

jDDj
M ¼ (4.3)
jDPj
where DP represents the change of output power and DD represents the change in
duty ratio.

1.9 FUZZY LOGIC CONTROLLER AS MPPT

FLC has been introduced in many researches as in [40e47] to force the PV to work
around MPP. FLC has the advantages of working with imprecise inputs, not
needing an accurate mathematical model, and handling nonlinearity. FLC gener-
ally consists of three stages: fuzzification, aggregation,and defuzzification.During
fuzzification, numerical input variables are converted into a membership function.
The output of the systems has linguistic relations with the inputs of the system.
These relations are called rules and the output of each rule is a fuzzy set. More
than one rule is used to increase conversion efficiency. Aggregation is the process
whereby the output fuzzy sets of each rule are combined to make one output fuzzy
set. Afterward, the fuzzy set is defuzzifiedtoacrispoutputinthe defuzzification
process.

1.10 PARTICLE SWARM OPTIMIZATION MPPT TECHNIQUE
Particle Swarm Optimization (PSO) is one of the swarm intelligence techniques
that uses stochastic variables based on population for solving optimization prob-
lems.Thistechnique wasfirstintroducedbyEberhartand Kennedyin(1995)
[48]. The study to use PSO in MPPT of PV systems was by Miyatake et al. in
2004 [49]. A wide range of studies have been done in the same area. PSOs are
inspired by the social swarming behavior of fish schooling or bird’s flocking.
PSO evolutionary process, potential solutions, called particles, move about the
multidimensional search space by following and tracking the current best particle
position in the swarm. The operation of PSO technique can be outlined in the
following [27]:
Each particle in the swarm has mainly two variables associated with it. These
variables are: the position vector x i (t) and the velocity vector v i (t) as shown in
Eq. (4.4). Thus, each particle x i (t) is represented by a vector [x i1 (t),
x i2 (t),.,x iD (t)], where i is the index number of each particle in the swarm, D
represents the dimension of the search space, and t is the iteration number.

x i ðt þ 1Þ¼ x i ðtÞþ v i ðt þ 1Þ (4.4)