Abstract:
As energy demands around the world increase, the need for renewable energy
sources that will not harm the environment increases. Renewable energy, such
as wind and solar energy, is desirable for power generation due to its unlimited
existence and environmental friendly nature. However wind and solar sources
are not reliable in terms of sustainability and power quality due to their inter-
mittent nature. A management system is thus required for supplying the load
power demand. This thesis presents a control strategy for power management
in a standalone solar photovoltaic and wind hybrid power system based on arti-
cial intelligence techniques. To ensure e cient optimization of sources, Adap-
tive Neural Fuzzy Inference System (ANFIS) strategy is employed to achieve the
Maximum Power Point (MPP) for photovoltaic (PV) panels and the Fuzzy Logic
Control (FLC) strategy is used to achieve the MPP of wind turbine. Moreover,
the FLC power management strategy is developed to manage the power
ow to
the system. The FLC chooses the optimal operating mode of power sources en-
suring continuous supply of the load and maintaining the battery state of charge
(SOC) at acceptable levels. The proposed system and its control strategy was as-
sessed using a hybrid system comprising of PV panels, wind turbine and battery
storage. Perturb and observe (P&O) MPP algorithm is used for a comparison
with the proposed ANFIS MPPT system. From the simulation results based on
the mathematical model of the system, the comparison of proposed MPPT with
the classical P&O reveals the robustness of the proposed PV control system for
solar irradiance and temperature changes. Moreover results also show that the
proposed FLC Power management strategy for the hybrid system gives a greater
reliability in terms of power generation and distribution compared to a stand
alone system with single source. It provides e ective utilization of power sources
and minimizes usage of the battery, hence improving its life. The whole system
is analyzed through simulation in MATLAB / Simulink environment.
