Consensus algorithm-based secondary control, such as virtual impedance, is typically used to achieve power-sharing and ensure stable operation in microgrids. The introduction of communication, however, increases the system's vulnerability. Communication failure, e.g., under cyber attack or disruption, can lead to a huge loss. To solve this, this article proposes a signal reconstruction approach for the miscommunicated signals. The power-sharing convergence, both in normal conditions and under communication failure, is analyzed via the Lyapunov method. The feasibility and effectiveness of the proposed approach are validated on a lab-scale microgrid.

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The development of lithium-ion batteries has experienced massive progress in recent years. Battery aging models are employed in advanced battery management systems (BMSs) to optimize the use of the battery and prolong its lifetime. However, Li-ion battery cells often experience fluctuations in battery capacity and performance during cycling, which makes capacity prediction more difficult. Moreover, the reason for the capacity regeneration phenomenon occurring after resting periods is not clear yet, as well as the influence of cycling conditions on capacity regeneration. A relationship between this phenomenon to cycling state of charge (SoC) ranges and current rates was investigated in this article on a battery cell with lithium nickel manganese cobalt (NMC) oxide positive electrode. Experimental results show that the capacity increase is a consequence of decreased internal impedance after the resting period. The experiments also showed that a significant power drop and subsequent power regeneration after a resting period occurs only for specific SoC ranges, and applying a resting period after battery cycling can mitigate this power fading process. The semi-empirical model of battery degradation including capacity regeneration is proposed in this article based on physical processes inside of the cell retaining low computational requirements. The acquired results can be utilized in BMSs for more accurate state of health (SoH) estimation and to prolong battery lifetime.

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Communication-based distributed secondary control is deemed necessary to restore the state of islanding AC microgrids to set points. As its limited global information, the microgrids become vulnerable to cyber-attacks, which by falsifying the communicating singles, like the angular frequency, can disturb the power dispatch in the microgrids or even induce blackout by pushing the microgrids beyond the safe operation area and triggering the protection. To make the microgrids more cyber secure, adaptive resilient control for the secondary frequency regulation is proposed. It assumes that each converter is communicating with its adjacent converters. With the proposed control, the weight of the communication channel being attacked is automatically reduced, and the more the communicating signals are falsified, the further the weight of that communication channel is weakened. The proposed approach does not rely on attack detection and thereby is easy to implement; Besides, it still works when challenged by a combination of multi-attack signals; Moreover, it applies to multiple communication lines getting attacked cases. Finally, the effectiveness and feasibility of the proposed resilient control scheme are validated by both simulations and experimental results.

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Balancing converters play a pivotal role in bipolar dc grids, and while numerous topologies have been explored, many suffer from drawbacks, such as reliance on bulky passive components, limited soft-switching capabilities, or complex control mechanisms. This article introduces an LC series-resonant balancing converter topology that addresses these issues, featuring smaller passive components, the ability to achieve soft switching across its entire operational range, and simplified control with proper design. By operating in the capacitive region, the converter ensures soft switching for the complete load range, enabling all switches to achieve zero voltage switching (ZVS) turn on. The study reveals that the converter's power flow depends on both the switching frequency (fsw) and the phase shift angle (φ), but notably, when the resonant inductor value is kept sufficiently low, fsw and φ become decoupled, simplifying power flow control. In addition, this article provides detailed design guidelines for the converter's resonant tank and validates the operation and control of the converter through an experimental setup.

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This article discusses the various operating modes of a series resonant balancing converter for bipolar dc grids. It is shown that the converter can be operated in both the capacitive and inductive regions with respect to the resonant frequency of the LC tank. Furthermore, concerning the pulse width modulation signals to the switches, the converter can either be operated by controlling the phase shift between the converter half bridge legs or the duty cycle of the half bridges. A qualitative comparison of the different modes proves that a) the phase shift modes have better soft switching capabilities, b) the capacitive phase shift mode can show zero voltage switching at switch turn-on in the whole operating range, c) the losses in case of capacitive phase shift mode shows best performance at low load power, d) the inductive region power modes show lower rms current for the same power flow compared with capacitive region modes which lead to lower losses at higher output power. The simulation and experimental results depict the operation of all the modes. Finally, a prototype is designed to validate all operating modes, demonstrating $>$99% system efficiency at 1.75 kW.@en

The communication network used in distributed sec-ondary control (DSC) for microgrid power and voltage regulation is vulnerable to cyber-attacks. Unlike the predominantly resilient research on secondary control, which tends to employ passive defense strategies, this paper presents a proactive defense mecha-nism to design a resilient network for microgrid secure operation. This proposed method involves preparing the resilient scheme before attacks occur and facilitates timely resilience during an attack. First, novel metrics are introduced to effectively quantify the impact of various cyber attacks. Then, a multiobjective optimization method is applied to design the communication graph considering the quantified attacks, convergence, time-delay robustness, and communication cost. Simulations are performed on a microgrid consisting of 10 inverter units under different scenarios to validate the effectiveness of the proposed methodology.

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Offshore wind is expected to be a major player in the global efforts toward decarbonization, leading to exceptional changes in modern power systems. Understanding the impacts and capabilities of the relatively new and uniquely positioned assets in grids with high integration levels of inverter-based resources, however, is lacking, raising concerns about grid reliability, stability, power quality, and resilience, with the absence of updated grid codes to guide the massive deployment of offshore wind. To help fill the gap, this paper presents an overview of the state-of-the-art technologies of offshore wind power grid integration. First, the paper investigates the most current grid requirements for wind power plant integration, based on a harmonized European Network of Transmission System Operators (ENTSO-E) framework and notable international standards, and it illuminates future directions. The paper discusses the wind turbine and wind power plant control strategies, and new control approaches, such as grid-forming control, are presented in detail. The paper reviews recent research on the ancillary services that offshore wind power plants can potentially provide, which, when harmonized, will not only comply with regulations but also improve the value of the asset. The paper explores topics of wind power plant harmonics, reviewing the latest standards in detail and outlining mitigation methods. The paper also presents stability analysis methods for wind power plants, with discussions centered on validity and computational efficiency. Finally, the paper discusses wind power plant transmission solutions, with a focus on high-voltage direct-current topologies and controls.@en

This paper presents a control strategy for active power decoupling in a Solid State Transformer (SST) using a cascaded H-bridge (CHB) converter, dual active bridge (DAB) converters, and a high-frequency link (HFL). The strategy balances capacitor voltage and decouples power flow, significantly reducing second harmonic voltage ripple in DC link capacitors and potentially reducing their size.

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In this chapter, various electric vehicle (EV) charging technologies are reviewed, including onboard charging, offboard charging, and contactless charging. The focus is on charging power control as well as its converter topology. Because EV charging significantly influences the grid, the power quality of EV charging is thoroughly reviewed in terms of modeling, analysis, and mitigation measures. EV charging, especially overnight, gives a lot of flexibility to instant charging power, which can be used to improve the load flow in the electric grid. Smart charging describes those approaches, which are also reviewed.

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This article provides the design procedure of a ±350 V series resonant balancing converter for bipolar DC grids. The process creates a η-ρ Pareto front to design a 3 kW converter with natural convection cooling.

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In the production of green hydrogen, electrolyzers draw power from renewable energy sources. In this paper, the design of Solid State Transformer (SST) for large-scale H ₂ electrolyzers is benchmarked. The three most promising topologies are chosen for design and comparison, including Modular Multi-level Converter (MMC) based SST, Modular Multi-level Resonant (MMR) based SST, and Input-Series-Output-Parallel (ISOP) based SST. The distance between converter towers for insulation and maintenance, the insulation system of the transformer, and the cooling system are designed with practical considerations in order to have an accurate estimation of the volume and weight of the SST. Losses in the switches are calculated based on equations, and losses in passive components are calculated based on FEM simulation. The operating frequency for each topology is optimized to minimize loss, weight, and volume. The best of each topology is then compared with each other to identify the most suitable one for large-scale H ₂ electrolyzers.

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This paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the state-of-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.

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This paper concerns the control problem of the active and harmonic power sharing caused by the mismatched impedance in resistive feeders-dominated microgrids. A distributed model predictive control (DMPC) scheme is suggested to regulate the virtual impedance of each involved unit for power sharing based on the neighbor's state. With the distributed philosophy, the central controller is not required. Moreover, the proposed method benefits resilience to communication failure by designing the communication matrix. Furthermore, it involves propagating information among units in a short period, significantly reducing the communication and computation burden. Finally, the performance of the proposed control scheme is evaluated in terms of its convergence, robustness to communication delay and load variations, resilience to communication failure, and plug-and-play functionality without communication in an inverter-connected system.

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Accurately evaluating battery degradation is not only crucial for ensuring the safe and reliable operation of electric vehicles (EVs) but also fundamental for their intelligent management and maximum utilization. However, the non-linearity, non-measurability, and multi-stress coupled operating conditions have posed significant challenges for battery health prediction. This paper proposes a battery capacity estimation framework based on real-world operating data. Firstly, a comprehensive feature pool is constructed from the direct external features extracted during multi-step fast charging processes and the quantitative representation of operating conditions. Subsequently, a two-step feature engineering is introduced to select the most relevant features and eliminate the interference components. The battery capacity estimation framework is then implemented using machine learning methods. Validation results demonstrate that the proposed framework achieves superior estimation accuracy with lower computational expense compared to the modelling process without feature engineering. The MAPE and RMSE reach 1.18% and 1.98 Ah, respectively, representing reductions in errors of up to 8.53% and 11.21%. Collectively, the proposed framework paves the foundation for online health prognostics of batteries under practical operating conditions.@en

Accurately predicting the battery's aging trajectory is required to ensure the safe and reliable operation of electric vehicles (EVs) and is also the fundamental technique toward residual value assessment. As a critical enabler for mainstreaming EVs, fast charging has presented formidable challenges to health prognosis technology. This study systematically compares the performance of features extracted from the multistep charging process in the state of health (SOH) assessment. First, 12 direct features are extracted from the voltage curve, and the degradation mechanisms strongly correlated to these features are analyzed in detail. Integrating the degradation mechanism and correlation analysis, a data feature construction strategy is designed to categorize extracted features into groups. Then, the performance of different features extracted from the fast charging process in the SOH assessment is compared regarding estimation accuracy. Finally, the generalization and feasibility of the optimal data feature are verified with different fast charging protocols and training data sizes. The verification results indicate that the data feature representing fused degradation modes has excellent generalization and feasibility in SOH estimation, and the mean absolute error (MAE) and root-mean-squared error (RMSE) for various cells under different decline patterns are within 0.90% and 1.10% , respectively.

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Resonant converters are popular in power electronics due to their soft-switching capabilities, which enhance efficiency and prolong component lifetime. Three- phase resonant converters are particularly noteworthy for their higher power density and reduced ripple, making them ideal for demanding applications. A critical aspect of optimizing three-phase LLC resonant converters is the design of a transformer with adequate leakage inductance required for the resonance circuit. This paper compares two distinct transformer designs for such converters: a five-limb shell-type transformer and a symmetrical triangular transformer. Both designs are evaluated in terms of their performance, efficiency, and suitability for integration into the converter architecture. A detailed design procedure using Finite Element Method (FEM) analysis is presented to guide the development of these transformers. The practicality of this approach and its effectiveness are demonstrated through the implementation of a 3.4 kV to 60 V, 50 kVA prototype. This work provides a comparative analysis of transformer designs and introduces a validated methodology for improving the performance of three-phase LLC resonant converters through optimized transformer design.

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This paper concerns the control problem of the active caused by the mismatched impedance in resistive feeders-dominated microgrids. A distributed model predictive control (DMPC) scheme is suggested to regulate the virtual impedance of each involved unit. Moreover, the proposed method benefits resilience to communication failure by designing the communication matrix. Furthermore, it involves propagating information among units in a short period, significantly reducing the communication and computation burden. Finally, the performance of the proposed control scheme is evaluated in terms of its convergence, robustness to communication delay and load variations, resilience to communication failure, and plug-and-play functionality without communication in an inverter-connected system.

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Traditional methods such as Steinmetz's equation (SE) and its improved variant (iGSE) have demonstrated limited precision in estimating power loss for magnetic materials. The introduction of Neural Network technology for assessing magnetic component power loss has significantly enhanced accuracy. Yet, an efficient method to incorporate detailed flux density information—which critically impacts accuracy—remains elusive. Our study introduces an innovative approach that merges Fast Fourier Transform (FFT) with a Feedforward Neural Network (FNN), aiming to overcome this challenge. To optimize the model further and strike a refined balance between complexity and accuracy, Multi-Objective Optimization (MOO) is employed to identify the ideal combination of hyperparameters, such as layer count, neuron number, activation functions, optimizers, and batch size. This optimized Neural Network outperforms traditionally intuitive models in both accuracy and size. Leveraging the optimized base model for known materials, transfer learning is applied to new materials with limited data, effectively addressing data scarcity. The proposed approach substantially enhances model training efficiency, achieves remarkable accuracy, and sets an example for Artificial Intelligence applications in loss and electrical characteristic predictions with challenges of model size, accuracy goals, and limited data.@en