Solar forecasting for a PV-battery powered DC system

Abstract

The photovoltaic (PV) power generation sector has been growing rapidly as a result of the rising need for solar power and the advancement of PV technology. PV Power generation is affected by weather factors such as cloud cover, solar irradiation, temperature, breeze direction and speed, and the amount of rain or snow. As a result, a highly precise PV power predictor is essential to improve security and reliability in the face of financial penalties and ambiguity. Hence, this paper suggests a novel approach to improve the efficiency of PV-battery-powered DC systems by combining solar irradiance prediction using the Long Short-Term Memory (LSTM) algorithm with a power electronic converter design that incorporates a bidirectional port. The LSTM algorithm was employed to predict one week of solar data with a remarkable R2 score of 0.96. A steady-state analysis of the proposed Three-Port Converter (TPC) is performed for five different operating modes to guarantee optimal performance. The suggested system's prediction performance was tested using several error metrics such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Squared Error (RMSE), which were computed as 0.0318, 0.0027, and 0.0526, respectively. Results from the above error measures show that the suggested approach performs more effectively in estimating solar irradiance. The Adaptive Neural-Fuzzy Interface System (ANFIS) and Incremental Conductance (IC) algorithms are employed for Maximum Power Point Tracking (MPPT) and assessed against various atmospheric conditions. From the MATLAB simulation results, the tracking efficiency of the ANFIS-based MPPT technique is 99.97 %, which is superior to the IC-based MPPT technique. Furthermore, it proved that the suggested approach improves the efficiency of PV-battery-powered DC systems, which is more appropriate for real-world applications such as DC microgrids and Electric Vehicles.