Synchronous machines (SMs) are characterized as nonlinear dynamic systems, with stator flux linkages being critical for controller design. Among the developed methods for flux linkage estimation, the disturbance observer-based flux linkage estimator (DOB-FLE) is recognized as the state-of-theart. However, DOB-FLE faces challenges in ensuring exponential convergence during transient states. To address this, this paper introduces an extended state observer-based flux linkage estimator (ESO-FLE), which represents an advancement over DOB-FLE. This novel approach utilizes extended states to model the nonlinear disturbance term as time-varying ramp signals, offering a contrast to the constant assumption employed in DOB-FLE. It concurrently estimates both the extended states and the flux linkages through an ESO. Simulation results from a 35-kW SM drive demonstrate that ESO-FLE provides superior transient performance under dynamic conditions compared to DOB-FLE.

Modular-Multilevel-Cascade Converters are the preferable choice for high-voltage and high-power applications. The price of simple scalability, modularity, and flexibility is paid by a large number of components, especially semiconductors and capacitors. Such components are prone to failure, so fault detection and diagnosis, fault-tolerant control concepts, and reliability analysis are research fields within this topology. This paper presents several fault-tolerant approaches for the class of Modular-Multilevel Converters under the severe failure of a whole cluster and compares them in detail. Furthermore, the different fault modes are emphasized, and operation recommendations are given. All approaches are extensively derived and verified by simulation and experimental results in a steady-state. Additionally, experimental results of transients, i.e., the transition from normal-to fault-operation and power steps, are provided for the most suitable proposed approach. The results show the proper functionality of the control strategies. The simple implementation enables easy integration into (existing) systems and thus provides better fault-tolerance.

Due to the complex rotor design of reluctance synchronous machines, a finite element analysis is essential for the accurate calculation of machine relevant performance objectives. Reluctance synchronous machines tend to have a large torque ripple, if this objective is not considered during the machine design. This necessitates a large number of simulation steps, resulting in a high computational burden and a long simulation time per design evaluation. Therefore, an efficient optimization algorithm is required. This paper proposes a complete framework for single-objective machine design optimization using Gaussian process regression and Bayesian optimization. Focusing on reluctance synchronous machine design, different kernel functions (squared exponential, Matern, rational quadratic) and hyperparameter configurations ´ are assessed for regression accuracy of the optimization objectives mean torque, torque ripple, and power factor. The impact of noise in the input data on the regression results is also investigated. Bayesian optimization with the infill criterion Expected Improvement is finally used to perform machine design optimization for 18 design variables. Bayesian optimization outperforms the classical algorithms such as genetic or particle swarm algorithms. It results in a faster design optimization, even for such a high number of design variables.

During the design of electrical machines, multiple performance objectives need to considered. Although stochastic optimization algorithms are extensively employed for this purpose, a primary drawback is the time-consuming and substantial number of design evaluations. Bayesian optimization (BO) presents an alternative that can address multi-objective optimization in particular for objective functions which are expensive to evaluate. Probabilistic surrogate models based on Gaussian process regression (GPR) form its basis. The high accuracy of Gaussian processes and their uncertainty estimation render Bayesian optimization extremely efficient and effective. Nevertheless, Bayesian optimization is hardly used in the field of electric machine design. Consequently, this study explores, analyses and evaluates the application of global multi-objective Bayesian optimization for machine design. Employing four distinct Bayesian acquisition functions, the study conducts a three-objective design optimization of a reluctance synchronous machine characterized by 14 design variables. A comprehensive comparison with the most common NSGA-II reveals that significantly superior outcomes are achievable within a considerably shorter time.

A generic current control system for modular multilevel cascade converters (MMCCs) is proposed. It is based on (i) input/output-linearization (I/O-linearization), (ii) feedforward disturbance compensation and (iii) parallelized proportional-integral-resonant controllers to compensate for arbitrarily many harmonics (including positive and negative sequence). The controller design is performed in state space. It is generic and flexible and combines pole placement and LQR tuning. Moreover, it allows to take all system parameters such as cluster or output (grid) inductances or resistances individually into account during controller design and tuning. Moreover, the typically imposed simplifying assumptions on system dynamics (e.g., identical parameters) or operating conditions (e.g., balanced loading) can be overcome. The proposed control system is validated for a dual-star bridge-cell MMCC. The simulation results illustrate its excellent control performance under ideal but also non-ideal conditions, where conventional approaches usually fail.

An optimal reference voltage saturation (ORVS) for nonlinear current control of synchronous machine drives is presented. It is integrated into a nonlinear current control system based on input/output (I/O) linearization and proportional-integral (PI) controllers with anti-windup. The ORVS is derived from an optimization problem that can be solved analytically. The deviation between the desired current dynamics obtained by the proportional-integral (PI) current control system if the voltage constraints were not present and the feasible current dynamics due to the saturated voltage is minimized. Finally, the proposed ORVS is compared to (i) classical (vector-valued) reference voltage saturation and (ii) the absence of any saturation and/or anti-windup. The simulative comparison is done for a highly nonlinear, anisotropic reluctance synchronous machine (RSM) whose parameters and look-up tables were obtained from a real machine. The simulation results show that the proposed ORVS outperforms the other two approaches in terms of current tracking accuracy and faster current dynamics.

Multi-objective hyperparameter optimization (MO-HPO) is applied to find optimal artificial neural network (ANN) architectures used for optimal feedforward torque control (OFTC) of synchronous machines. The proposed framework allows to systematically identify Pareto optimal ANNs with respect to multiple (partly) contradictory objectives such as approximation accuracy and computational burden of the considered ANNs. The obtained Pareto optimal ANNs are trained and implemented on a realtime system and tested experimentally for a nonlinear reluctance synchronous machine (RSM) against non-Pareto optimal ANN designs and a state-of-the-art OFTC approach. Finally, based on the most recent results from ANN approximation theory, guidelines for Pareto optimal ANN-based OFTC design and implementation are provided.Â

Facing the climate crisis, more and more renewable inverter-based resources (IBRs), such as wind energy conversion systems (WSs), replace grid-connected synchronous machines (SMs). SMs inherently provide balanced and sinusoidal grid voltages but also power system inertia, whereas standard grid-following (GFL) control of IBRs leads to decreasing power system inertia. This paper proposes grid-forming (GFM) control for doubly-fed induction machine (DFIM) based wind energy conversion systems (WSs) based on the virtual synchronous machine (VSM) concept with the following extensions: (i) Maximum power power tracking (MPPT) compensation for accurate inertia emulation, (ii) feedforward torque control (FTC) for fast power reference tracking, (iii) de-rated operation or reference power point tracking (RPPT) to provide additional power reserves, (iv) dynamic droop power saturation control to avoid excessive power overshoots (as available power reserves are taken into account), and (v) grid voltage control utilizing reactive power from both, DFIM stator and rotor-side back-to-back inverter. The modeling and control of the DFIM-based WSs is shown in detail and the grid model is based on the IEEE 9-bus test system. Comprehensive simulation results show that the GFM control enables unlimited power penetration of IBRs and that the grid frequency dynamics of VSM-based power systems can be interpreted and handled analogously to SM-based power systems. During normal operation, compared to existing VSM control without FTC, the proposed VSM control with FTC increases the wind energy yield, i. e. typical MPPT or RPPT performance is achieved, similar to the performance of GFL control. For high power penetration of IBRs, the proposed VSM control enables stable operation due to its inertia emulation or GFM capability, whereas GFL control tends to instability. During faults and system splits, the VSM provides higher power system damping than a real SM due to (i) virtual/internal damping that can be adapted by software (hence almost independently from the hardware, whereas the response of SM damper windings depends on the physical machine design and cannot be altered during operation), and due to (ii) faster droop control which adapts the virtual turbine power without the mechanical delays that dominate real turbine dynamics.

A unified method is presented which allows to estimate all harmonic components, DC-offset and fundamen- tal frequency in arbitrarily distorted single-phase grids us- ing a frequency-adaptive observer (FAO) consisting of modi- fied Second-Order Generalized Integrators (mSOGIs), a DC- Integrator (DCI) and a modified Frequency Locked Loop (mFLL). DCI and mSOGIs are tuned by pole placement which allows for an arbitrarily fast detection of DC-offset and harmonic components if the fundamental frequency is known. If the fundamental frequency must be estimated as well, a mFLL with Gain Normalization (GN), Rate Limitation (RL), Anti-Windup (AW) strategy and low-pass filters (LPF) must be employed. The effectiveness of the proposed FAO is validated by experimental results and its enhanced performance is shown by comparisons to existing estimation methods.

Modular Multilevel Cascade Converters (MMCCs) are considered a promising power electronics topology in industry. Their scalability allows to reach (ultra/very) high voltage levels with low harmonic content and high efficiency and makes MMCCs an ideal solution for high-power applications; such as electrical drives, solid-state transformers and high-voltage direct-current (HVDC) transmission systems. However, the high levels of thermal, electrical and mechanical stress on the power electronics devices and the large number of components (e.g. capacitors or semiconductors) make MMCCs prone to several kinds of faults. Fault detection and diagnosis (FDD) in combination with fault isolation and system reconfiguration techniques, based on cell redundancy, can increase the reliability, availability and safety of MMCCs, which is crucial for their utilization in critical energy applications. This paper comprehensive surveys: (i) reliability including failure rates, fault modes, failure mechanisms and fault impact analysis, (ii) concepts of fault tolerance and FDD (e.g. expert system, model- or hardware and data-based FDD methods) and (iii) system reconfiguration strategies (e.g. cold- or hot-redundant) for MMCCs. All discussed aspects are related to generic MMCC models and operation principles, unifying notation of over 250 papers and, therefore, simplifying the understanding and comparability. The state-of-the-art, challenges and future research trends and opportunities towards reliable MMCC-based systems are thoroughly collected.

Synchronous machines (SMs) like reluctance synchronous machines (RSMs) or interior permanent magnet synchronous machines (IPMSMs) are characterized by nonlinear stator flux linkages which are crucial for optimal control and operation management. This paper presents a novel disturbance observer that allows to estimate the nonlinear flux linkages online for any SM (with constant or no excitation). Moreover, a simple identification sequence with ramp-like voltage signals is proposed to extract key information of the nonlinear stator flux linkage maps within a short period of time and without the need of any controller, torque sensor or prime mover. Finally, the proposed observer with identification sequence is implemented and validated for a real and highly nonlinear IPMSM by realistic and detailed simulation results.

An Artificial Neural Network (ANN) based Optimal Feedforward Torque Control (OFTC) strategy for electrically excited synchronous machines (EESMs) is proposed. After design, data set creation, training and validation of the ANN, the analytical computation of the optimal stator and exciter currents is achieved which allows to minimize copper and iron losses and to produce the desired (or maximally feasible) machine torque. Voltage and currents constraints of stator and exciter are considered as well. In contrast to conventional OFTC, the proposed ANN-based OFTC strategy does not require iterations nor a decision tree to find the optimal current triple while machine nonlinearities, magnetic cross-coupling, saturation and speed-dependent iron losses are taken into account. In addition, the proposed ANN design procedure allows to consider measurable OFTC goals and computational resources that ensures a real-time capable implementation. Comprehensive simulation results for a real and nonlinear EESM clearly show these benefits by comparing the proposed ANN-based OFTC with results of a nonlinear optimization problem (NLP) solver (which cannot be used in real-time).