Abstract:
The solar energy technology seems a long term reliable and sustainable energy
model. Besides, the solar cell and solar power harvesting technology have advanced
to the impressive levels. However, solar energy technology is lacking in terms of efficiency and usability. Solar power generating systems can be organized
into mini-grids to serve areas not covered by the mains supply. The issue of the
depletion of oil reserves in the world, the demand for electricity, frequent power
outage and the problem of air pollution produced by motor vehicles emissions
which have led to global warming motivate many researchers to seek alternative
energy sources. In this study, a mathematical model to represent the MGA dynamics
was designed. Detailed mathematical derivations from first principles have
been presented and then represented the derived equations within Simulink. The
parameters identification was investigated by applying different methods in MGA
models. The Nonlinear Least-Square and Pattern-Search strategies were the best
methods among the methods studied because of the performance and the accuracy
and could be automated in MATLAB. Moreover, the effect of each of the PID
parameters on the closed-loop dynamics were discussed and demonstrated how to
use a PID controller to improve the system performance. Finally, the stability of
the MGA Model was studied using Routh-Hurwitzs, Nyquist and Bode plots. It
was found that the Bode plot is the best tool for determining the range of the
gear ratio while the Nyquist plot and Routh Hurwitz methods are the best to obtain
the relative stability of the closed and open system respectively. Simulation
showed that the system is stable for 1 Gr 7 and the excess voltage is achieved
when the Gr > 3 .This research has contributed to the eld of system modeling
and system identification. This research has contributed to the eld of system
modeling, system identification, and system stability analysis. This is a relatively
new area that has a growing importance in control problems. The precise mathematical
models are essential during the controller designing process because they allow the designer to estimate the closed-loop behavior of the plant. The errors in parameter values could result in poor instability and control. Therefore, adequacy and accuracy of parameters identification are primary modeling problems that always have to be addressed
