Unlike other works, an extra DC-DC converter to perform
MPPT was not needed, since the front-end rectifier executed active input current wave shaping for power factor correction and
MPPT as well.
Artificial intelligence methods have been used to design
MPPT controllers; such methods include artificial neural networks [7]-[9], particle swarm optimization (PSO) method [10], [11], fireworks-enriched algorithm (FE) [12], [13], and fuzzy logic controller (FLC)-based Mamdani or T-S model [14]-[16].
Two sensors are installed: one is installed before
MPPT to measure the PV current and the other is installed after
MPPT to measure the battery current.
To achieve maximum power point, the
MPPT buck converter can be designed using different
MPPT algorithms.
In the first stage of a grid-connected inverter, an
MPPT control algorithm mainly includes the constant voltage method, the perturbation and observation (P&O) method, and the conductance increment method.
[20] proposed a short-circuit pulse-based
MPPT with fast scan on the P- V curve to identify the proportional parameter which is commonly used in a current-based
MPPT [21].
The usage of
MPPT algorithm enables PV port to operate at maximum power point at different insolation levels.
Over the past decades, various
MPPT methods have been developed.
MPPT capability on a charge controller is similar to
MPPT with inverters (and DC optimizers).
Different
MPPT techniques have been considered in PV power applications.
The solar system includes solar roof,
MPPT controller, D/C control unit.
For several years, many
MPPT control methods have been developed and implemented, like Fuzzy Logic Method [4-7], perturbation and observation (P&O) method [5, 6, 8], and Incremental Conductance (Inc.Con.) method [7, 9-11].