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Reducing the common mode voltage (CMV) fluctuations is crucial in transformer-less (T-less) converters. The modulation modification-based methods inherently increase the steady-state error of the compared currents due to the reduced number of voltage vectors. This error can significantly raise the total harmonic distortion (THD) output current of the inverter. This research presents a strategy of odd virtual vectors based on model-free predictive control using the extended state observer (ESO) to fix the CMV fluctuations and a significant decrease in the THD of the output current. This means the number of CMV stabilizing vectors increases with the linear combination of odd voltage vectors. The proposed method has two advantages over CMV fluctuation reduction schemes that are modulation modification-based: simultaneous control of CMV stabilization and THD reduction in T-less converters, and independence of the controller from system variables and parameters, making it a robust predictive control method. The practical results show that the proposed method, in addition to the complete CMV stabilization and the reduction of the current THD, is completely robust to the changes in the parameters of the ultralocal model and ESO compared to the model-based solutions.

In this paper, the quadratic buck converter (QBC) is proposed as competitive alternative to implement a battery charger. Since QBC is a high order system, the required control is designed to follow the conventional constant-current constant-voltage protocol by means of three loops. Namely, 1) an inner-loop operating in sliding mode to control the current of the closest inductor to the input port providing the proper stability of the system, 2) a first outer loop designed to regulate the battery voltage providing the reference of the inner loop, and finally 3) a second outer loop to regulate the battery current modifying the reference of the voltage loop. Proportional Integral (PI) controllers are used in both outer loops, one of them synthesized by means of the robust loop shaping M-constrained integral gain optimization (RLS-MIGO) method, and the other designed using classical considerations for cascaded controllers. Both simulation and experimental results are presented validating the theoretical study and confirming the feasibility of the proposed control by means of analogue electronics.

Abstract A sampling error‐based finite‐set predictive current control (FS‐PCC) is proposed in this article for the open‐end winding induction motor (OEWIM) drive. The proposed scheme controls the zero‐sequence current (ZSC) alongside the stator currents. In a model‐free approach, this method predicts the future of ZSC and stator current components by the stator current and voltage sampling errors. In this way, the parameters of the OEWIM are not utilised in the prediction algorithm of the FS‐PCC. So, the proposed method is robust against the variation of the parameter. Moreover, this article presents a simple vector selection technique for the FS‐PCC of the OEWIM. The proposed technique has two cost functions and a simple algebraic equation to put the voltage vectors (VVs) in the prediction algorithm. The first cost function uses VVs that do not have the zero‐sequence voltage component. Then, the algebraic equation determines VVs that must be utilised in the second cost function. Finally, the optimum VV is selected by the second cost function. In the proposed scheme, the prediction algorithm is iterated 14 times instead of 27 iterations of the conventional predictive algorithm. So, besides establishing a novel model‐free prediction algorithm, the proposed method has almost 50% fewer calculations. The validity of the proposed sampling error‐based FS‐PCC and the simplified vector selection technique has been verified through experimental tests.

Tuning the weighting factor is crucial to model predictive torque and flux control. A finite set of discrete weighting factors is utilized in this research to determine the optimum solution. The Pareto line optimization technique is implemented to prevent the occurrence of local optimum solutions. By conducting an accuracy analysis, the number of discrete weighting factors is optimized, and the number of iterations is reduced. The stator current distortion minimization criterion is used to obtain the ultimate global optimal solution from the Pareto line. This study compares the results of the proposed optimization method and the particle swarm optimization method based on experimental data from a 4 kW induction motor drive test bench. The proposed technique can achieve the global optimum weighting factor in a shorter computational duration while maintaining a slightly lower total harmonics distortion and torque ripple.

The increasing demand for energy and the high penetration of distributed energy resources require the evolution of current electrical systems toward smarter and more reliable electric grids. In this regard, microgrids (MG) play a vital role in integrating distributed energy resources (DER), loads, and storage systems. However, microgrid architectures lack versatility and flexibility in terms of control, limiting their expansion. This paper presents a multi-mode master-slave control approach to increase the flexibility of DC-coupled hybrid microgrids. The proposed control scheme allows optimal coordination of the power units connected to each bus. Coordination among buses is also achieved through interlinking and interfacing converters; thus, ensuring the reliable operation of the microgrid. Moreover, this approach considers the possible expansion of the capacity of the MG, providing more degrees of freedom for optimization and control. An MG with two DC distribution buses connected to the main grid is selected as a case study to develop dynamic modeling and establish a control architecture. The advantages of the proposed control are discussed via MATLAB simulation results considering the operation of the MG in several scenarios.

This paper analyzes for the first time a two-loop sliding-mode control (SMC) of a high-order converter supplying a constant power load (CPL). The converter is a single-switch quadratic buck structure (QBC) interfacing a domestic 380 V DC bus to a CPL requiring a regulated voltage of 48 V DC. The converter is unstable in the absence of control and even after the insertion of an inner loop based on SMC of the input inductor current. The addition of an appropriate linear outer loop establishing the reference to the inner loop stabilizes the system and provides output voltage regulation. The regulated QBC shows a fast recovery of the output voltage with negligible overshoot in response to step-type changes of the output power or the input voltage. It is also shown that the implemented regulator for CPL supply can be used directly in the case of a constant current load (CCL) or a constant resistance load (CRL) resulting in similar performance to the CPL case. PSIM simulations and experimental results in a 400 W prototype are in good agreement with theoretical predictions.

Current distortion is a critical issue of power quality because the low frequency harmonics injected by adjustable speed drives increase heating losses in transmission lines and induce torque flickering in induction motors, which are widely used in mining facilities. Although classical active filtering techniques mitigate the oscillatory components of imaginary power, they may not be sufficient to clean the sensitive nodes of undesirable power components, some of which are related to real power. However, the usage of power electronic converters for distributed generation and energy storage, allows the integration of complementary power quality control objectives in electrical systems, by using the same facilities required for active power transferring. This paper proposes a predictive control-based scheme for mitigating the current distortion in the coupling node between utility grid and the mining facility power system. Instead of the classical approach of active filtering, this task is included as a secondary level objective control referred into the microgrid control hierarchy. Hardware-in-the-Loop simulation results showed that the proposed scheme is capable of bounding the current distortion, according to IEEE standard 1547, for both individual harmonics and the total rated current distortion, through inequality constraints of the optimization problem.

The transition from fossil fuels to renewable energies as power sources in the heavy industries is one of the main climate change mitigation strategies. The carbon footprint in mining is related to its inherent extraction process, its high demand of electric power and water, and the use of diesel. However, considering its particular power requirements, the integration of microgrids throughout the whole control hierarchy of mining industry is an emergent topic. This paper provides an overview of the opportunities and challenges derived from the synergy between microgrids and the mining industry. Bidirectional and optimal power flow, as well as the integration of power quality have been identified as microgrid features that could potentially enhance mining processes. Recommendations pertaining to the technological transition and the improvement of energy issues in mining environments are also highlighted in this work.

Conventional model predictive control (MPC) of power converter has been widely applied to power inverters achieving high performance, fast dynamic response, and accurate transient control of power converter. However, the MPC strategy is highly reliant on the accuracy of the inverter model used for the controlled system. Consequently, a parameter or model mismatch between the plant and the controller leads to a sub-optimal performance of MPC. In this paper, a new strategy called model-free predictive control (MF-PC) is proposed to improve such problems. The presented approach is based on a recursive least squares algorithm to identify the parameters of an auto-regressive with exogenous input (ARX) model. The proposed method provides an accurate prediction of the controlled variables without requiring detailed knowledge of the physical system. This new approach and is realized by employing a novel state space identification algorithm into the predictive control structure. The performance of the proposed model-free predictive control method is compared with conventional MPC. The simulation and experimental results show that the proposed method is totally robust against parameters and model changes compared with the conventional model based solutions.

The composite converter allows integrating the high-efficiency converter modules to achieve superior efficiency performance, becoming a prominent solution for electric transport power conversion. In this work, the versatile buck–boost dc–dc converter is proposed to be integrated into an electric vehicle composite architecture that requires a wide voltage range in the dc link to improve the electric motor efficiency. The inductor core of this versatile buck–boost converter has been redesigned for high voltage applications. The versatile buck–boost converter module of the composite architecture is in charge of the control stage. It provides a dc bus voltage regulation at a wide voltage operation range, which requires step-up (boost) and step-down (buck) operating modes. The PLECS thermal simulation of the composite architecture shows a superior power conversion efficiency of the proposed topology over the well-known classical noninverting buck–boost converter under the same operating conditions. The obtained results have been validated via experimental efficiency measures and experimental transient responses of the versatile buck–boost converter. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 4.4 kW powertrain is presented using a PLECS RT Box 1 device. The HIL simulation results verified the accuracy of the theoretical analysis and the effectiveness of the proposed architecture.

Photovoltaic (PV) microinverters have grown rapidly in the small-scale PV market, where typical two-stage converters are used to connect one PV module to the single-phase AC grid. This configuration achieves better performance in terms of energy yield compared with other PV configurations. However, the conversion efficiency of a two-stage system is the main drawback, especially when a high-voltage gain effort is required. In this context, single-stage microinverter topologies have been recently proposed since only one power conversion stage is required to extract the maximum power of the PV module and inject the AC power to the grid. This single-stage configuration allows considerable improvement of the overall efficiency of microinverters by reducing the number of elements in the system. However, the main challenge of these topologies is their control, since all variables of the converter are composed by the AC waveform with DC-bias. In this paper, four control strategies are analyzed for the mainstream single-stage topology, which is the dual-boost inverter (DBI). Classical linear control and three non-linear strategies, namely finite control set–model predictive control, flatness-based control, and sliding mode control, are detailed. The main contribution of this work is a complete comparison of the control strategies, to give insights into the most suitable control strategy for the DBI in PV microinverter application.

Robust control techniques for power converters are becoming more attractive because they can meet with most demanding control goals like uncertainties. In this sense, the Takagi-Sugeno (T-S) fuzzy controller based on linear matrix inequalities (LMI) is a linear control by intervals that has been relatively unexplored for the output-voltage regulation problem in switching converters. Through this technique it is possible to minimize the disturbance rejection level, satisfying constraints over the decay rate of state variables as well as the control effort. Therefore, it is possible to guarantee, a priori, the stability of the large-signal converters in a broad operation domain. This work presents the design of a fuzzy control synthesis based on a T-S fuzzy model for non-minimum phase dc-dc converters, such as boost and buck-boost. First, starting from the canonical bilinear converters expression, a Takagi-Sugeno (T-S) fuzzy model is obtained, allowing to define the fuzzy controller structure through the parallel distributed compensation technique (PDC). Finally, the fuzzy controller design based on LMIs is solved for the defined specification in close loop through MATLAB toolbox LMI. Simulations and experimental results of a 60 W prototype are presented to verify theoretical predictions.

Single-stage voltage step-up inverters, such as the Dual Boost Inverter (DBI), have a large operating range imposed by the high step-up voltage ratio, which together with the converter of non-linearities, makes them a challenge to control. This is particularly the case for grid-connected applications, where several cascaded and independent control loops are necessary for each converter of the DBI. This paper presents a global current control method based on a combination of a linear proportional resonant controller and a non-linear sliding mode controller that simplifies the controller design and implementation. The proposed control method is validated using a grid-connected laboratory prototype. Experimental results show the correct performance of the controller and compliance with power quality standards.