advanced photovoltaic emulator with ann-based modeling using a dc-dc push-pull converter and lqr control with current observer

As solar photovoltaic power generation becomes increasingly widespread, the need for photovoltaic emulators (pves) for testing and comparing control strategies, such as maximum power point tracking (mppt), is growing. pves allow for consistent testing by accurately simulating the behavior of pv panels, free from external influences like irradiance and temperature variations. this study focuses on developing a pve model using deep learning techniques, specifically a multi-layer perceptron (mlp) artificial neural network (ann) with backpropagation as the learning algorithm. the ann is integrated with a dc-dc push-pull converter controlled via a linear quadratic regulator (lqr) strategy. the ann emulates the nonlinear characteristics of pv panels, generating precise reference currents. additionally, the use of a single voltage sensor paired with a current observer enhances control signal accuracy and reduces the pve system’s hardware requirements. comparative analysis demonstrates that the proposed lqr-based controller significantly outperforms conventional pid controllers in both steady-state error and response time.