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One of the main features of microgrids is the capability of operating in both grid-connected (GC) and standalone (SA) modes. This paper presents a novel dual mode robust model predictive control (RMPC) strategy for a three-phase inverter with an LCL filter in both GC and SA operating modes under the filter’s parameter uncertainty. At first, a disturbance observer gain is obtained by solving a linear matrix inequality (LMI), which is determined to preserve the stability of the algorithm. The designed disturbance observer takes into account the polytopic uncertainty of system parameters. A performance index with two weighting matrices is then defined and solved in an infinite horizon by turning it into an optimization problem under LMI constraints. The performance of the control strategy highly depends on the weighting matrices. Hence, an optimization algorithm is formulated to ascertain the best matrices values for both GC and SA operation modes. Given the nonlinearity issue, particle swarm optimization (PSO) is employed to derive the optimal weighting matrices offline. To evaluate the proposed control strategy’s effectiveness, simulations and experiments are performed under several scenarios in both GC and SA operating modes. The results reveal the proposed control strategy’s powerfulness compared to other techniques in the presence of grid voltage and load current disturbances for both inverter operating modes.

Ensuring the reliability of power electronic converters is a matter of great importance, and data-driven condition monitoring techniques are cementing themselves as an important tool for this purpose. However, translating methods that work well in controlled lab environments to field applications presents significant challenges, notably because of the limited diversity and accuracy of the lab training data. By enabling the use of field data, online machine learning can be a powerful tool to overcome this problem, but it introduces additional challenges in ensuring the stability and predictability of the training processes. This work presents an edge computing method that mitigates these shortcomings with minimal additional memory usage, by employing an autonomous algorithm that prioritizes the storage of training samples with larger prediction errors. The method is demonstrated on the use case of a self-commissioning condition monitoring system, in the form of a thermal anomaly detection scheme for a variable frequency motor drive, where the algorithm self-learned to distinguish normal and anomalous operation with minimal prior knowledge. The obtained results, based on experimental data, show a significant improvement in prediction accuracy and training speed, when compared with equivalent models trained online without the proposed data selection process.

The present paper identifies the root causes of current ringing in a Dual Active Bridge (DAB) power converter and proposes a novel methodology to design the magnetic tank (transformer, primary and secondary inductances) configuration to minimizeit. In particular, two metrics proportional to the current harmonic content on each transformer side are correspondingly proposed based on a steady-state frequency analysis of primary and secondary ac voltages and impedance of the magnetic tank. To get the metrics, different combinations of dv/dt, switching frequency, primary and secondary inductance configuration, and stray capacitance of the magnetic tank (model inputs) are simulated to generate primary and secondary harmonic current indicators (model outputs). Then, a machine-learning model is trained to predict the harmonic current metrics for each given operating setpoint. The model is able to make predictions beyond its training data, therefore generalizing to unseen inputs and enabling rapid evaluation of various designs. Experimental results are presented to evaluate the performance of the metric model for 4 different magnetic tank configurations. Designers can use the methodology to rapidly analyze the generation of current ringing under multiple operating parameters, identifying operating zones with low harmonic content and reduced sensitivity to parameter shifts for the converter's operation

Voltage boost converters are one of the most important components of DC microgrids, since they are used to enhance the voltage of naturally intermittent energy sources such as solar panels in order to feed unknown demands. In this work, a novel tuning algorithm for Composite Nonlinear Feedback (CNF) is studied in depth to improve transient performance and address output voltage regulation for a DC-DC boost converter in the presence of DC input uncertainty. The proposed CNF controller comprises both linear and nonlinear feedback terms. The linear part contributes to the stability and output tracking with a small damping ratio and a quick response. The nonlinear part, i.e., damping term, reduces the overshoot stemming from the linear feedback law and increases the damping ratio of the overall closed-loop system. The nonlinear part is automatically tuned whereby the transient performance of the DC-DC boost converter improves significantly. To assess the performance of the proposed technique, a boost converter is simulated in MATLAB Simulink considering different scenarios such as changing load, DC input, and voltage reference. The numerical results demonstrate that the tuned CNF controller outperforms the linear controller in the DC boost converter. Additionally, several experiments are conducted to validate the efficacy of the suggested technique.

This paper proposes a real-time algorithm for identifying the grid parameters, which is concurrently used for online tuning of the predictive controller in each iteration, in a gridtied active front end (AFE) voltage source converter (VSC) applications. The algorithm is designed by inspiring from the concepts of the extended Kalman filter (EKF) and the model predictive controller (MPC). The performance of the algorithm highly depends on the weighting factors of the algorithm. The artificial neural networks (ANN)-based algorithm is used to find the optimal set of weighting factors among the ones in a parameter search block. An offline particle swarm optimization (PSO) is run to provide the data source for the parameter search block. The algorithm identifies not only the inductance but also the resistance of the grid. Additionally, the hard constraints on the amplitude of the input and output variables are guaranteed. The validation of the proposed approach is performed experimentally and compared with the state-of-the-art conventional methods. The experimental results show the proposed method could effectively stabilize the system in weak grid conditions and under wide impedance variations. Additionally, the accuracy of the proposed impedance identification method is 96%.

This paper proposes a novel attack identification mechanism for internet-of-things-based (IoT-based) converter-composed DC grids, where each agent collects its own and neighbours’ measurement data for output regulation to meet a preceding power-sharing consensus. Independent from model-free or average-model-based attack detection theories, this mechanism is mainly inspired by converter stitching behavior analysis. Correspondingly, when facing latent signal substitution or agent instigation attacks, through comparing estimated signals with received ones for signal source authentication, both self-sensors and neighbours will be inspected. Eventually, not only can such attacks be detected, but also will respective attack sources be identified. A simulation case of 4-agent 800V IoT-based DC grid on Simulink and a hardware case of 3-agent 90V IoT-based DC grid on dSpace testing platform were investigated. Experimental results revealed that the estimation ratio error kept lower than 3.9% and all attacks were successfully identified, verifying the effectiveness of the proposed mechanism.

Grid-connected voltage source converters (VSCs), which are the key block of the future power electronics-based power systems, are at a high risk of losing stability and robust performance. These challenges in industrial applications require analyzing and designing a simple, robust, and advanced controller to guarantee not only reliable operation but also the performance of the VSCs. This paper proposes a novel constraint and unconstraint continuous control set (CCS) model predictive controller (MPC) for VSCs. In the unconstraint MPC, the optimal control law is analytically calculated. However, for the constraint MPC, the analytical solution is not available. So, the convex optimization methods are used to find the suboptimal solution. Generally, the key novelties of this paper can be enumerated as using CCS-MPC to obtain fixed switching frequency, considering hard constraints on the amplitude of the input and output signals, and analytically obtain the optimal solution for unconstraint MPC. Finally, to evaluate the validity of the proposed approaches, some experimental tests on the laboratory-scale stand-alone VSC are extracted.

This paper deals with the problem of controller design for DC microgrids that feed constant power loads. To design the proposed controller, first by the use of the exact feedback linearization approach, the linear model of Brunovsky's canonical representation of the system has been obtained to address the nonlinearity problem of the system. Then, the desired control technique is developed by a combination of sliding mode and backstepping control approaches in which a nonlinear disturbance observer is utilized to estimate the disturbance. The overall stability of the system is analyzed based on the Lyapunov approach. A suitable and practical sliding surface is one of the controller strengths that allow the bus voltage to track the reference voltage with high accuracy and fast transient response. Finally, to prove the mentioned claims, an experimental setup has been constructed and the proposed controller is implemented. The experimental results have been analyzed and error analysis is performed. The results confirm the superiority of the proposed controller compared to state-of-the-art controllers.

This paper proposes a robust cooperative distributed secondary control strategy for DC microgrids, with the main focus on reducing communication burdens. To this end, we adopt a sparsity-promoting consensus-based distributed secondary control framework for converter-interfaced DC microgrids consisting of multiple distributed generation (DG) units. The proposed control strategy relies on less (sparse) information exchange over a communication network and does not require exchanging voltage signals or their estimated values amongst neighbouring DG units. Rigorous Lyapunov-based stability certificates for DC microgrids are derived. The stability conditions can be verified without the knowledge of distribution lines connecting different DG units. The effectiveness of the proposed secondary control approach is verified by experimental results and a comparative simulation case study on a multiple-DG DC microgrid.

This paper proposes a distributed intelligent secondary control (SC) approach based on brain emotional learning-based intelligent controller (BELBIC) for power electronic-based ac microgrid (MG). The BELBIC controller is able to learn quick-auto and handle model complexity, non-linearity, and uncertainty of the MG. The proposed controller is fully model-free, indicating that the voltage amplitude and frequency deviations are regulated without previous knowledge of the system model and parameters. This approach ensures low steady-state variations with higher bandwidth and maintains accurate power-sharing of the droop mechanism. Furthermore, primary control is realized with a robust finite control set-model predictive control (FCS-MPC) in the inner level to increase the system frequency bandwidth and a droop control in the outer level to regulate the power-sharing among the distributed generations. Finally, experimental tests obtained from a hardware-in-the-loop testbed validate the proposed control strategy for different cases.

Nonlinear behavior of constant power loads (CPLs) and their negative incremental impedance, which impose adverse effects on system damping and stability margins, necessitate developing proper strategies for efficient implementation framework of DC microgrid. In this paper, a fixed-time sliding mode disturbance observer (SMDO) is first addressed to provide an estimation of the instantaneous power flow moving along the uncertain CPLs with time-varying nature within a fixed time. It not only expedites the estimation rate but also improves the robustness against physical parameter variation. The fast fixed-time backstepping technique is then developed based on the estimated load power to control the duty cycle of the boost converter such that the entire power grid becomes stable and the desired voltage of the DC bus is tracked within a fixed time irrespective of the initial conditions. A rigorous Lyapunov-based approach is represented to guarantee the fixed-time stability of the proposed scheme. Finally, to validate the merits and implementation feasibility of the proposed methodology, the experimental realization is represented under various operating case studies.

The Dual Active Bridge (DAB) is a bidirectional DC/DC power converter with a high-frequency transformer to provide a wide input-output voltage range and galvanic isolation, making the converter attractive to use in multiple power conversion applications. Highly efficient modulation schemes like Triple Phase Shift (TPS) and control strategies, like classic PI controllers and Model Predictive Control, have been proposed to control voltage or current reference in the literature. Particularly for battery charging stations, independent of the charging process the implementation should limit both current and voltage inside a secure operational area. The present paper proposes a voltage control with output current saturation capability for the output side of a DAB with a TPS modulation strategy based on [1] and a Moving Discretized Control Set Model Predictive Control (MDCS-MPC) [2]. Modulation is transparent from the proposed control algorithm, which only generates the dc output current for the desired actuation. Simulation results show the dynamic and steady-state performance of a pulse charging process on a battery model. PI controller results are contrasted with the MDCS-MPC strategy implemented, highlighting advantages and trade-offs.

To cope with the weak grid stability issue of grid-tied Voltage Source Converters (VSCs), this letter proposes an Artificial Neural Network (ANN)-based approach for online stabilization control of the grid-tied VSC with the pole-tracking feature. First, an ANN is adopted to establish the mapping between the control parameters and the closed-loop poles of the grid-VSC system, serving as a computationally light model surrogate that is favorable for real-time control applications. Then, an online parameter search algorithm enabling simultaneous tuning of multiple controllers and parameters is developed, by which the systems poles under a new grid condition can be pulled to the reference ones, i.e., achieving the pole-tracking-based stabilization control of this work. Finally, the efficacy of the proposed method along with its stabilization effect is verified by MATLAB simulations and experimental results.

Grid-connected power electronic converters are the fundamental building blocks of future power electronic-based power systems. Their fast dynamic performance and robustness are the key requirements for ensuring the reliable operation of such systems. This letter proposes a simple optimal model predictive controller (MPC) to control the active and the reactive current of the three-phase voltage source grid-connected converters. Better performance over other state-of-the-art controllers is evaluated based on quantitative criteria comprising the settling time, and the percentage of the overshoot. Finally, the experimental tests are provided to prove the applicability of the proposed approach.

The parallel connection of DC-DC converters requires the development of an appropriate control strategy that regulates load voltage and shares current amongst participating converters. This paper proposes a resilient and robust cooperative distributed control approach that simultaneously ensures voltage regulation and balanced current sharing in parallel DC-DC converters in the presence of false data injection attacks on control input channels. Based on analytical tools from network control and Lyapunov stability theory, concise stability certificates are derived. The proposed cooperative distributed control strategy guarantees resilience against unknown bounded attacks on the actuators of DC-DC converters and the robustness to uncertainties in load parameters and the physical parameters of converters. Furthermore, the control design for each converter does not require any knowledge about the number of participating converters. The detailed simulation and experimental results verify the satisfactory performance of the proposed method in voltage regulation and balanced current sharing in parallel converters, as well as resilience to bounded false data injection attacks.

Finite set model predictive control (FS-MPC) has been identified as one of the most favorable controllers for power electronic applications due to its capability over real-time solutions to multiple objectives and constraints. However, the main challenge in the FS-MPC is the choice of appropriate weighting factors in the cost function to reach the best switching state of the inverter. This study proposes an approach based on brain emotional learning (BEL) to provide online tuning of weighting factors in FS-MPC of a power converter, which prevents the dependency of the converter control system on the various uncertainties coming from operating conditions and loading conditions. The proposed BEL approach is fully model-free, indicating that the weighting factors are adjusted without previous knowledge of the system model and parameters. Simulation and experimental results validate the proposed control scheme’s effectiveness under different load conditions.

Sharp LC resonance peak may enlarge inherent harmonics, dominant harmonics of the supply voltage generated by a three-phase diode rectifier, around the resonance frequency, which will intensify fluctuations of the DC-link voltage and grid currents in motor drives with small DC-link capacitor. In this paper, a novel resonance suppression strategy based on inherent harmonics impedance shaping is proposed specially for three-phase input PMSM drives with small DC-link capacitor. Instead of increasing the damping of the whole system for resonance suppression, the motor side power disturbance can be reduced by designing the impedance for inherent harmonics around LC resonance frequency, based on the new model with the consideration of motor side characteristic. For damping current generation, the angle of the virtual admittance is modified to eliminate the influence of the DC-link voltage sampling delay. Experimental results show the effectiveness of the method on a prototype of electrolytic capacitorless PMSM drive.

This paper proposes a novel approach to uncover deficiencies of the existing cyber-attack detection schemes and thereby to serve as a foundation for establishing more reliable cybersecure solutions, with particular application in DC microgrids. For this purpose, a multi-agent deep Reinforcement Learning (RL) based algorithm is proposed to automatically discover the vulnerable spots on the conventional index-based cyberattack detection schemes, and automatically generate coordinated stealthy destabilizing False Data Injection (FDI) attacks on cyberprotected islanded DC microgrids. To enable a continuous action space for the trained RL agents and enhance the algorithm’s precision and convergence rate, Deep Deterministic Policy Gradient DDPG) is incorporated. Using this approach, susceptibility of a state-of-the-art detection scheme to several different coordinated FDI attacks on the distributed communication links is identified. The proposed algorithm is also enhanced with a sniffing feature to enable maintaining the stealthy attacks even under the sudden disconnection of any of the compromised links. To address the discovered deficiencies within the index-based detection scheme, a complementary multi-agent RL detection algorithm using Deep Q-Network (DQN) is integrated, which provides a more reliable overall identification performance. Taking into account the communication delays and load changes, the effectiveness of the proposed algorithm is verified by the experimental tests.

This paper presents the application of Model Predictive Control (MPC) in high-performance drives. A wide variety of machines have been considered: induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the paper is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise.

Virtual synchronous generator (VSG), recognized as a grid-friendly control technique, contributes to providing inertia and damping support from the grid-tied power electronic systems. However, similar to conventional synchronous generator (SG), the VSG is vulnerable to wideband oscillations, and can even become unstable when the system is subjected to a variety of disturbances. To tackle this issue, this paper proposes a magnitude-phase motion equation (MPME) for grid-forming VSG attached to weak grid. Besides, a feedback analytical method is introduced to identify the frequency dynamic stability, judged by the sign of (d/dt)/. Besides, the rate of change of frequency (RoCoF) and frequency nadir (FN) are evaluated by the two indices, i.e., d/dt and . In addition, the relationship between low-inertia-RoCoF-FN-related stability and the weak-damping-oscillation-related stability is illustrated by the feedback analysis. Moreover, in the framework of MPME, a reactive-power feedforward (PPFF) decoupling control strategy is proposed for enhancement of stability and robustness performance of the VSG-system. Finally, the proposed modeling and the reactive-power feedforward decoupling control strategy are demonstrated with the experiments.