A Lightweight ANN Controller for Grid-tied Inverters with Strong Adaptability

Inverter's controller directly determines the grid-integration quality of power conversion systems. To address complex operating conditions, e.g. grid voltage distortion, parameter variations and weak grid scenarios, this paper proposes a lightweight artificial neural network (ANN) controller with strong adaptability. In implementation, the ANN controller generates control signals while simultaneously optimizing its weights in real time using the gradient descent algorithm. Distinctively, the weight gradients are directly calculated using the loss function and weights, which compared to the error backpropagation method, significantly reduces computational complexity, and therefore achieves the computational lightweight feature. In addition, the ANN phase-locked loop (ANN-PLL) is constructed to provide phase alignment for current reference while enabling fully ANNbased inverter control architecture. In principle, the proposed ANN controller relies neither on an offline training dataset nor on the system model, and achieves adaptive weights adjustment in real time with minimal computational effort. Through physical experiments, the proposed lightweight ANN controller was compared with the sliding mode controller and the model-based ANN controller, verifying its superior performance under complex operating conditions, such as grid voltage distortion, input voltage variation, current reference variation, filter parameter variation, and extremely weak grid (short circuit ratio=1.09).