Performance Investigation of Deadbeat Predictive Controllers for Three-Level Neutral Point Clamped Inverter
Nassima Bekhoucha, Mostefa Kermadi, Nadhir Mesbahi, Saad Mekhilef
Abstract
This article aims to improve the performance of the deadbeat (DB) predictive controller for a three-level neutral point clamped (NPC) inverter. The effect of the number of effective vectors considered for the cost function evaluation on steady-state and dynamic performance is investigated. To do that, three DB predictive controllers with different numbers of effective vectors, namely, 19 vectors-based, six vectors-based, and three vectors-based DB, are compared beside the conventional current-based model predictive control (MPC). The neutral-point (NP) voltage is balanced using the redundant vectors. Simulations and experimental tests are performed to evaluate the performance of the competing MPC algorithms in terms of four main criteria, namely: NP voltage balancing error, total harmonic distortion (THD), the computational effort required, average switching frequency, power loss, and sensitivity to parameters mismatch. Compared to conventional MPC, the experimental results show that the three vectors-based DB predictive controller has the best steady-state and dynamic performance with a reduction of computational burden up to 60% and a reduction of the current THD up to 72%.