Adaptive discrete-time grid-voltage sensorless interfacing scheme for grid-connected DG-inverters based on neural-network identification and deadbeat current regulation

This paper presents an adaptive discrete-time grid-voltage sensorless interfacing scheme for grid-connected distributed generation inverters, based on neural network identification and deadbeat current regulation. First, a novel neural network-based estimation unit is designed with low computational demand to estimate, in real-time, the interfacing parameters and the grid voltage vector simultaneously. A reliable solution to the present nonlinear estimation problem is presented by combining a neural network interfacing-parameters identifier with a neural network grid-voltage estimator. Second, an adaptive deadbeat current controller is designed with high bandwidth characteristics by adopting a delay compensation method. The delay compensation method utilizes the predictive nature of the estimated quantities to compensate for total system delays and to enable real-time design of the deadbeat controller. Third, the estimated grid voltage is utilized to realize a grid-voltage sensorless average-power control loop, which guarantees high power quality injection. Theoretical analysis and comparative evaluation results are presented to demonstrate the effectiveness of the proposed control scheme. Index Terms--Deadbeat current control, digital control, distributed generation (DG), grid-voltage sensorless control, neural network identification, pulsewidth modulated (PWM) inverters.