Abstract
In the last decade, model predictive control (MPC) has been widely studied in power converters, such as voltage source inverters (VSIs). Unfortunately, MPC often presents a high computational burden that limits their applicability, especially when driving multilevel inverters (MLIs) because of their higher number of switching combinations than two-level inverters. As a result, some strategies have been developed to reduce the computational complexity of MPC. One of the most relevant is the use of artificial neural networks (ANNs) to approximate the behavior of an MPC. However, ANNs require to be evaluated at bounded inputs. Otherwise, their response cannot be guaranteed to be a good approximation of the controller they learned from. Furthermore, when driving an LC-filtered VSI, the inductor current can present high peaks due to the cross-coupling effect between the inductor and the capacitor. These current peaks can cause physical damages and loss of performance of an ANN-emulated MPC. This paper presents a new constrained modulated MPC (M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PC), better suited for ANN emulation, to overcome these issues. The proposed strategy evaluates the cost function once per switching region, allowing easy and intuitive constraint inclusion. Additionally, an overmodulation stage is used to handle negative duty cycles and enhance disturbance rejection. Finally, the proposal is validated through simulations, using MATLAB-Simulink, taking into account different load conditions. Simulations show that the constrained M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PC keeps the inductor current below its desired limit while having a good performance (low harmonic distortions and fast dynamics) even when the inverter operates near its boundaries.
Highlights
In recent years multi-level inverters (MLIs) have become a vital study focus due to their advantages compared with two-level inverters
This study compares three control strategies previously described, namely the unconstrained M2PC (Section III-A), used as a reference [20], the M2PC with constraints added to its cost function using the hlim term described in [41] (Section III-B), and the proposed constrained M2PC (Section III-C)
WORK This paper has developed a novel constrained M2PC strategy for an LC-filtered 3φ-3L-voltage source inverters (VSIs) converter
Summary
In recent years multi-level inverters (MLIs) have become a vital study focus due to their advantages compared with two-level inverters. This strategy generates empty areas in the switching space, which increases the resulting THD [38] compared with other M2PC strategies, such as [20], [21], [39] These studies only consider the sum of the duty cycles as a constrain, and an optimal overmodulation stage is added to handle negative duty cycles. In [15], a post-correction stage is added to a CCS-MPC controller to limit the inductor current and the inverter voltage It guarantees fixed switching frequency, it requires weighting factors. ANN-emulated MPC controllers applied to VSIs require learning from MPC controllers with good performance, such as fast dynamic response and low THD at steady-state, regardless of their computational cost.
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