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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.

Semiconductors in power converters are operated in switched-mode. For a given sampling frequency, a digital controller computes the next actuation according to several objectives. Typically one desires to regulate current or voltage values with minimal error and harmonic distortion. Higher switching frequency of the semiconductor is helpful as the granularity of the problem is improved and the harmonic spectrum is shifted toward high-order harmonics that are easier to filter with passive elements. However, the switching losses increase. Finite control set model predictive control (FCS-MPC) is proven to be an excellent control method with a great ability to maximize harmonic quality in terms of the switching frequency, particularly when the controller is configured to consider long prediction horizons. Nonetheless, in order to avoid computational overruns, the prediction horizon and the sampling frequency cannot be indefinitely increased. In this article, the tradeoff between these two parameters is studied, providing guidelines on FCS-MPC tuning for different configurations.

The power conversion system based on the modular connection has widespread applications in various power electronic systems. To accurately estimate the state of health without recognizing the systematic mathematical model and to extend the lifetime, this article proposes a lifetime extension approach based on the Levenberg–Marquardt back propagation neural network (LM-BPNN) and power routing of interleaved dc–dc boost conversion systems. The LM-BPNN model is constructed based on the voltage, current, and temperature data generated by the system. On the basis of the trained LM-BPNN, the real-time cumulated damage estimation of each power cell in the conversion system can be accomplished. Applying the power routing concept, the dc–dc boost conversion system allocates different power to the cells according to the cumulated damage of each cell, thereby delaying the failure of cells with higher cumulated damage. Numerical simulation results show that the proposed lifetime extension approach can extend the overall system lifetime. Furthermore, an experimental setup of the interleaved dc–dc boost conversion is constructed to verify the proposed approach, which is of great significance for predictive maintenance in the industrial system.