This study presents a self-error compensated sequential predictive control (SE-SMPC) technique for three-phase multimodular matrix converters (MMMC). Traditional model-based predictive control (MPC) methods typically require manual tuning of weighting factors and involve significant computational effort. In contrast, the SE-SMPC method addresses these limitations by prioritizing control objectives hierarchically, thereby reducing the number of switching states without sacrificing system performance. A key feature of this approach is its builtin error self-compensation mechanism, which distributes and corrects prediction errors across modules, thereby improving dynamic accuracy. Simulation results show that the proposed controller provides accurate tracking of the load current, significantly reduces the input reactive power and offers high fault tolerance in the event of module failure. In addition, it has been shown to reduce total harmonic distortion (THD) without the need to design complex cost functions. These advantages make it particularly suitable for high-efficiency AC-AC power conversion in various renewable energy applications.

This paper presents the design and simulation of a multimodular power conversion system based on voltage source inverters (VSI), composed of two parallel-connected three-phase modules driving an induction motor. A self-error-compensated predictive current control strategy is proposed to coordinate the modules, enabling dynamic current sharing and fault-tolerant operation. In the event of a failure, the control scheme incorporates the tracking error of the faulty module into the cost function of the operational one, improving performance and continuity. Simulation results demonstrate the effectiveness of the proposed method, with improved dynamic response, reduced root mean square error (RMSE), and lower total harmonic distortion (THD) under both normal and fault conditions.

This paper presents a delay-compensated modulated model predictive direct power control (DC-M2PDPC) strategy for grid-connected three-level neutral-point-clamped (3L-NPC) converters. The proposed method integrates power-oriented control, fixed-frequency modulation, and control delay compensation within a predictive control scheme. The DC-M2PDPC strategy achieves fast dynamic response and demonstrates superior steady-state performance in power tracking and DC-link voltage balancing. It also ensures lower harmonic content than conventional methods, even under dynamically varying power conditions, in accordance with the IEEE 519-2022 Standard for Harmonic Control in Electrical Power Systems. These features make it well-suited for grid-tied applications such as wind energy conversion systems (WECS) and photovoltaic (PV) systems.

This paper presents the implementation of LoRaWAN-based non-invasive temperature sensor nodes for detecting loss points in distribution networks. The primary challenges involve integrating and calibrating the electronic components, including temperature sensors, LoRaWAN modules, and digital processors, to develop a robust system for analyzing loss points using thermal measurement techniques. The system employs activation by personalization for deploying sensor nodes, which simplifies the activation process and enhances network efficiency. The data collected is securely transmitted to a network server, then analyzed on an application server to identify loss points. This methodology aims to automate the analysis and detection of losses in medium and high voltage distribution networks, offering a technologically advanced, rapidly deployable, and cost-effective solution. Initial results demonstrate the system’s effectiveness in providing accurate and timely detection of loss points, contributing to improved network efficiency and sustainability.

This paper studied the design of a predictive current control strategy with fault tolerance and reactive power minimization applied to a multi-modular topology based on indirect 2-level matrix converters fed by a six-phase generator. The control algorithm of the proposed strategy involves coupled current signals to perform error compensation between the converter modules, aiming to address potential system faults while maintaining reactive power close to zero. The results, obtained through simulation, were evaluated considering each module’s input and output currents and the final load current, as well as reactive power minimization, incorporating the obtained values of total harmonic distortion and mean squared error. The behaviour was analyzed in steady-state and transient conditions, with the system operating nominally and under fault conditions. The results demonstrate the effectiveness and good performance of the proposed strategy with the utilized topology, achieving a satisfactory response to faults through compensation and constant reactive power minimization.

A predictive current control design for a multi-modular indirect matrix converter using a three-level neutral-point clamped for the inverter side is studied. The multi-modular topology is based on two 3-Level Indirect Matrix Converter modules, and the control strategy proposal involves a single model-based predictive current control and the use of a coupled current signal. It is the interaction between the two three-phase output current of each module for error compensation between these, along with the ability to operate in failures. This article shows the results of the simulation in Matlab/Simulink of the 3-level multi-modular indirect matrix converter with coupled control, compared with an independent control for each module of the converter, in order to verify the performance obtained in terms of total harmonic distortion and mean square error. In addition, the analysis was carried out with respect to the behavior in transient, stationary state and with simulated failures for each module. The results obtained support the effectiveness of the control strategy with the proposed topology showing, with the control coupled, a decrease in the mean square error in the current signal of the final load, having a good value of total harmonic distortion and the capacity for optimal operation in the event of failures of the generation or conversion system.

Multi-phase wind generation systems are emerging as a promising technology for distributed generation systems. These systems can present unbalanced voltages or phase faults for several reasons. In this paper a modular three-phase direct matrix converter topology is used as conversion stage in a six-phase generation system to supply the desired current to a load. To achieve a reliable performance in the conversion stage, an improved predictive current control is proposed that take advantage of the modularity of the converter to enhance the behavior and to provide the capability to work continuously even under unbalance in the source or during fault operation of the generation system whilst achieving the desired tracking and power quality. The technique is compared against a classical approach to show the benefits of the proposed.

This work investigates the problem of finite-time position and attitude trajectory of quadrotor unmanned aerial vehicle systems based on a modified second order sliding mode algorithm. The selected algorithm is a modified super-twisting with both nonlinear and linear correction terms.

In this work, a time delay estimation based optimal conventional and second order sliding mode stator currents controller are proposed and compared for a six-phase induction machine fed by voltage source power converters. In a first step, the speed is regulated in an outer loop using a proportional-integral controller. Then, in a second step, the stator currents are controlled in an inner loop using the proposed methods. On one hand, the first method is a combination of time delay estimation with optimal first order sliding mode with exponential reaching law. On the other hand, the second method is a combination of time delay estimation with optimal super-twisting control. Based on Lyapunov theory, the stability of the stator currents closed-loop error dynamics is demonstrated and sufficient conditions of stability are determinate. As a case example, numerical simulations have been conducted on an asymmetrical six-phase induction machine to demonstrate the good features of the developed nonlinear controllers.

This paper investigates the problem of stator current control in presence of uncertainties and unmeasurable rotor current for a six-phase induction motor drive. An inner control loop based on a robust discrete-time sliding mode with time delay estimation method is proposed to ensure the finite-time convergence of the stator currents to their desired references while the proportional-integral controller is used for the outer speed control. Sufficient conditions are established to ensure the stability of the closed-loop system. Simulation results were carried out to verify the performance of the proposed robust control strategy for a six-phase induction motor drive.

The path planning of an ASV in a lake for environmental monitoring has been modeled as a particular case of the travelling salesman problem, in which the ASV should visit a ring of beacons deployed at the shore of the lake for delivering the collected data. For achieving a complete representation of the lake, the distance travelled should be maximized instead of minimized as in the classic TSP. The evolutionary technique known as Genetic Algorithm is applied for finding the optimal solution. The simulations show promising results even in the case that some restrictions are included in the problem.

There are several control techniques for active power filter applications. This paper presents a new alternative of predictive current control for a cascade H-bridge multilevel converter, where the optimal vector selected by the predictive controller is used for a modulation stage to obtain a fixed switching frequency. Simulation results validate the proposal where almost similar results can be obtained in comparison with the traditional methods.

The interest in electricity generation from photovoltaic (PV) systems has experienced significant growth in recent years, justified by the reduced environmental impact generated. The energy extracted from PV systems is related to the amount solar irradiation and thus the angle of incidence of the sun's rays on the surface of the panels. This paper presents, on one hand, a novel design of a biaxial solar tracking system (azimuth and elevation angle) for PV power application and on the other, the set-up of the control system focused on increasing the efficiency of the PV generation system across the implementing of a digital Proportional-Integral-Derivative (PID) position control scheme to achieve the maximum power point tracking (MPPT), ensuring the maximum energy available from the photovoltaic panels.

This article considers the speed control of asymmetrical dual three-phase generator by using an inner loop of Model Based Predictive Control (MBPC) to predict the effects of future control actions on the state variables. In order to achieve this goal, the algorithm uses both a Luenberger Observer and a Kalman Filter with the purpose of estimating the rotor currents. Thereafter, the speed control is used to reach the Maximum Power Point Tracking (MPPT), which ensures that it is delivered to the load. Finally, a comparison of the efficiency of the MBPC when considering the rotor current estimators and when they are used an estimator based on the state space representation.

Most sensorless algorithms applied to electrical drives are based on the mathematical representation of a physical system which includes electrical and mechanical variables of the motor. However, in electrical drive applications, the rotor current cannot be measured, so it must be estimated. This paper deals with the speed sensorless control of asymmetrical dual three-phase induction machines by using an inner loop of Model-Based Predictive Control (MBPC). The MBPC is obtained from the mathematical model of the machine, using a state-space representation where the two state variables are the stator and rotor currents, respectively. The rotor current is estimated using a reduced order estimator based on a Luenberger observer. Finally, simulation results are provided to show the efficiency of the proposed sensorless speed control algorithm.