NEURO-FUZZY BASED MAXIMUM POWER POINT TRACKER FOR PHOTOVOLTAIC SYSTEM ON A MOVING VEHICLE

Decreasing amount of fossil fuel pushed the use of renewable energy resources in the electricity generation, including for ship electrical system. One of the renewable energy resources is photovoltaic (PV) that has many advantages such as clean and available in abundance in nature. In this research, PV system with KY converter and artificial neural network (ANN) based maximum power point (MPPT) algorithm is proposed. MPPT unit is very important to extract most energy from the PV. MPPT unit on a moving vehicle must have high tracking accuracy and fast response because of rapidly changing environment conditions. To evaluate the proposed system, the power generated by the system is compared with maximum power that possible generated by PV. The systems are tested in 25°C, 1000 W/m2 at beginning, and changed to 25°C, 600 W/m2 after 2 seconds. The results show that the output voltage ripple of KY converter is 2.97% at 25°C, 1000 W/m2 and 2.86% at 25°C, 600 W/m2 from the 220 V. The steady state time of KY converter output voltage is 0.72 second from zero until reach steady state at 25°C, 1000 W/m2, and 0.58 second from the input change to 25°C, 1000 W/m2 until reach steady state. These results prove that KY converter can generates smaller voltage ripple and fast transient response. The test of system is also show that the proposed system has high MPPT accuracy and matching efficiency with the value above 99% in both conditions.