Solar water pumps are bringing environmental and socio-economic benefits for remote areas where agriculture plays a vital role in the livelihoods of people. Maximum power point tracking (MPPT) solar charge controller known as smart DC-DC converter is necessary for all solar photovoltaic (PV) power system to extract maximum available power from PV module MPPT controller forces PV module to operate at voltage close to a maximum power point which improves the efficiency of the solar PV system. The prior knowledge of MPP is necessary to start any conventional MPP algorithm. As the MPPT algorithms have no prior knowledge of the MPP at the start of the perturbation, these algorithms take a long time to reach the MPP. In this work, a deep learning-based long short term memory (LSTM) network is used to provide the prior knowledge on MPP which minimizes the tracking time and maximizes the efficiency of the PV system. The cascaded buck-boost converter is utilized to minimize the input and output side ripples. The dataset needed to train the LSTM is collected from a 33 kWp PV plant installed in PSR Engineering College, India. The PV system along with an Arduino UNO based MPP controller is simulated using Proteus software. The proposed algorithm is found to be far better by its statistical analysis and also this initial value makes the MPP algorithm superior in both accuracy and response, i.e., 0.05 seconds prior. To validate the proposed algorithm hardware prototype with proportional parameters is developed.
