Abstract:
Energy has great importance for our life and economy. Designing a stand-alone
Photovoltaic power and water pumping system is highly essential for supplying energy to
rural communities. This study presents the design of an artificial neural network (ANN)
based maximum PowerPoint tracking (MPPT) controller for a solar-powered water pumping
system. Yalow Lala Keble is one of the villages most affected by getting basic needs such as
water supply and reliable electricity. The government has made sufficient efforts towards
rural electrification through the use of renewable energy, particularly PV, in stand-alone
systems. This effort aims to solve the water supply problem for the isolated community’s
people who transport water from the river using plastic cans, bottles, pots, and vessels. ANN
based MPPT drives a DC-DC boost converter, which maximizes the power from the PV
module while also giving the Brushless DC (BLDC) motor constant input. The speed of the
BLDC motor was managed through a Pulse Width Modulation (PWM) control of the voltage
source inverter using a DC Link voltage controller. The built-in encoder has been used to
generate a PWM signal, which is then used to carry out the electronic commutation by hall
signal sensing. The system's efficacy was evaluated in comparison to Perturb and Observe
(P&O) and Incremental (INC) MPPT control methods. Based on the results of the study, a
population of 5,200 with a daily water demand of 520 m3 at the minimum design flow rate
has been set as the target size. Water needs to be pumped at a minimum head of 82.5 m at a
rate of 12 L/s with a minimum power rating of 17 kW from the pump being considered for
this site. The estimated total power demand from the solar PV array was 29 kW. The analysis
leads to the selection of the 305-watt module, resulting in a total of 96 modules to be
connected in 6 series and 16 parallel. The systems were sized appropriately before being
modeled in MATLAB / Simulink
