Page 122 - Renewable Energy Devices and System with Simulations in MATLAB and ANSYS

Overview of PV Maximum Power Point Tracking Techniques 109

performance. Although their operation can be affected by external disturbances (e.g., system noise,
short-term or rapidly changing meteorological conditions, etc.), they are able to recover and move
in the correct direction, where the MPP is located as soon as the disturbance has subsided. The
constant-voltage, constant-current, model-based MPPT and artificial intelligence–based methods
are more robust compared to the P&O and InC methods, since they are less affected by external
disturbances. However, their efficiency is lower due to the periodic interruption of the PV source for
measuring the open-circuit voltage/short-circuit current of the PV source. The resulting efficiency is
further reduced in case accurate knowledge of the PV source operational parameters, which is required
for their implementation, is not available. The single-sensor MPPT approach comprises a P&O MPPT
method, thus exhibiting equivalent robustness to external disturbances with the P&O approach.
However, its efficiency is lower because of the deviation of the power converter operation, which is
predicted using a theoretical model, from the actual performance obtained under practical operating
conditions. This is due to the tolerance of the electric/electronic components, values, circuit parasitics,
etc. In the numerical optimization MPPT algorithms (except the multistage and parabolic prediction
MPPT methods), a scan process is periodically repeated in order to detect possible changes of the MPP
position, which results in efficiency reduction due to the associated power loss until convergence to
the MPP has been achieved. The numerical optimization MPPT algorithms do not require a signifi-
cant system knowledge for their application, but their implementation complexity is higher than that
of the P&O and InC methods. Among the numerical optimization MPPT algorithms, the multistage
and parabolic prediction MPPT methods exhibit similar performance to the P&O and InC methods.
The robustness of the remaining numerical optimization MPPT algorithms is affected by external
disturbances, since they are not able to recover from possible error estimations, which are performed
due to the decisions taken during each iteration, until the next scan process is reinitiated. Due to the
exploitation of the inherent, low-amplitude switching ripples of the power converter for performing
the MPPT process, the robustness of the RCC MPPT technique may easily be affected by the impact
of external disturbances on the accuracy of calculating the PV power–voltage correlation function.
Additionally, appropriate codesign of the power converter and MPPT control system is required for
the implementation of the RCC MPPT method, thus requiring system knowledge availablility. The
control-circuit complexity of the RCC and ESC MPPT techniques is relatively high. A better robust-
ness to external disturbances is obtained using the ESC method compared to the RCC-based MPPT
approach since its operation is based on the injection of perturbation signals, rather than using the
inherent, low-amplitude switching ripples of the power converter. However, detailed knowledge of the
PV system operational characteristics is still required by the ESC method for tuning the operational
parameters of the MPPT control loop. Both the RCC and ESC MPPT methods operate by employing
a continuously operating feedback loop; thus, their efficiency is not affected by periodic disruptions
of the PV source operation. The MPPT method based on sliding-mode control requires knowledge
of the PV source operational characteristics. Since the PWM generator is replaced by a sliding-mode
controller and a P&O MPPT process is also performed during its execution, the complexity of the
corresponding control circuit is similar to that of the P&O MPPT process. However, a better efficiency
and robustness to external disturbances may be obtained under dynamic conditions, compared to the
PWM-based P&O MPPT method, due to the faster response of the sliding-mode MPPT controller.
The constant-voltage, constant-current, model-based MPPT and ANN-based and sliding-mode
control methods operate based on the knowledge of the PV source operational characteristics. Thus,
their accuracy is highly affected by the PV module aging, unless the drift of the PV source electrical
characteristics with time is compensated through a suitable model, which may be difficult to derive
and would increase the complexity of the control unit.
In contrast to the rest of the MPPT methods, which perform the MPPT process through a
continuously operating feedback loop, the periodic reinitialization of the MPPT process required in
the constant-voltage, constant-current, and numerical optimization techniques (except the multistage
and parabolic prediction MPPT methods) imposes the need to apply a high sampling rate in order to
be able to quickly detect the MPP position changes.