IEEE Access (Jan 2022)
From <italic>MPC</italic>-Based to End-to-End <italic>(E2E)</italic> Learning-Based Control Policy for Grid-Tied <italic>3L-NPC</italic> Transformerless Inverter
Abstract
This paper proposes an end-to-end ( $E2E$ ) learning-based control policy to directly control a transformerless grid-tied three-level neutral-point-clamped (3L-NPC) inverter powered by a photovoltaic (PV) array. This $E2E$ control policy is represented by an artificial neural network (ANN) and a time-delay neural network (TDNN), namely, ANN - and TDNN -based control policies, to properly estimate the optimal switching vector of the 3L-NPC. With such learning-based control policy, there exists no need for deriving or understanding deeply the complex mathematical model of the 3L-NPC, as the dynamics of both the system and the control scheme, as well as the cost function to be minimized, are learned via an end-to-end learning fashion that maps directly from the raw observations to the optimal switching states. This definitely eliminates the major barriers of the model-based control strategies (i.e., model predictive control (MPC)) such as (i) the need for an accurate system model, and (ii) the exponential increase in computational complexity. In order to train the two control policies, the conventional MPC is employed, as an expert, for acquiring a set of training data (i.e., input-output pairs) and, thereafter, for assessing our proposed control schemes. The proposed E2E control strategies are validated using MATLAB/Simulink software, where the impact of having different input features and training data are studied. With the proposed control policies, especially the TDNN -based control policy that has only one time-delay window, a high-quality sinusoidal grid current is achieved with low total harmonic distortion (THD), resulting in enhancing the power quality of the utility grid. In addition, the leakage current is minimized compared to the conventional MPC by more than 25%. However, the same dynamic behavior is almost obtained during the irradiation changes compared to the MPC strategy. Moreover, the experimental verification of the proposed E2E control strategy is implemented on the basis of the Hardware-in-the-Loop (HIL) simulator using the C2000TM-microcontroller-LaunchPadXL TMS320F28379D kit, demonstrating the applicability and good performance of our proposed control strategy under realistic conditions.
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