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Multilevel cascaded H-bridge (CHB) converters are usually operated applying a phase-shifted pulsewidth modulation (PS-PWM) method. In this modulation strategy, it is important to define the sampling instants in order to obtain a superior harmonic performance in the CHB output waveforms. Previous approaches to define the PS-PWM method consider a shorter sampling time, becoming even smaller when the number of cells of the CHB is large. In addition, unbalanced operational conditions in the CHB aggravate this issue also degrading the CHB harmonic performance. In this article, both challenges (small sampling time and unbalanced operational conditions) are faced. A feed-forward compensation technique is proposed to face with the unbalanced operating conditions and enlarging the available computational time for control schemes keeping the good CHB harmonic performance. Experimental results demonstrate the superior performance of the proposed strategy.

Cascaded H-bridge (CHB) converters are good candidates for many industrial applications. Among the modulation techniques that can be applied to CHB converters, the phase-shifted pulse width modulation (PWM) method (PS-PWM) provides very good harmonic quality being the most popular modulation technique at the expense of presenting high switching losses. On the contrary block switching technique as well as selective harmonic elimination/mitigation techniques provides lower switching frequency and losses with a lower harmonic quality. Additionally, with these techniques the converter operation becomes stiff. In this work a modification of PS-PWM based on an extremely low frequency carrier is proposed with the objective of achieving both low switching losses and an acceptable harmonic quality, simultaneously. The main advantage of the proposed solution is the capability of adjusting the phase-shift angle among the carriers to compensate for harmonic grid voltage disturbance. Several experimental results have been reported to demonstrate the good performance of the proposed modulation technique.

Successful application of finite control set model predictive control strategies with long prediction horizon depends on the careful design of the optimization algorithm. The conventional method involves transforming the problem to an equivalent box-constrained integer least-squares formulation that can be solved with branch-and-bound techniques, such as the sphere decoding algorithm (SDA). In this work, it is proposed to define an artificial neural network (ANN) to replace the SDA, avoiding its inherent computational variability. Similarly to practical applications of the SDA, the ANN finds an approximate solution of the underlying optimization problem. In contrast, the main benefit of the proposed approach is that it can be implemented in a low-cost microprocessing platform, greatly improving the performance in terms of resources in comparison with other advanced techniques proposed in the literature.