Distributed secondary control achieves voltage restoration and power sharing through communication among adjacent units but exposes the microgrid to potential cyber-attacks. Traditional mitigation strategies modify the secondary controller after the attack, addressing the issue only postoccurrence. Furthermore, in microgrid planning, the structure of the communication network significantly influences the resilience to attacks, but it remains to be explored. This article presents a proactive defense mechanism by designing a resilient communication network. The proposed method quantifies the impact of attacks and develops a multiobjective optimization algorithm to design the network, considering quantified attacks, convergence, time-delay robustness, and communication costs. The method is validated through OPAL-RT simulations of an islanded microgrid with ten converters.

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.

Electric vehicle (EV) charging plays a crucial role in paving the way for zero-emission vehicle markets, which require massive installations of EV chargers. However, that causes degraded power quality of the power grid, which may lead to non-compliance issues. To prepare for massive installations, a comprehensive understanding of EV chargers’ potential power quality impacts is essential. Furthermore, mitigation measures for potential power quality issues should be prepared beforehand to prevent catastrophic power quality issues from happening. Therefore, the thesis aims to clarify the potential power quality issues caused by EV chargers, prepare modelling methods to understand the root cause of the power quality issues and develop mitigation measures to alleviate chargers’ power quality impacts.

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.

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.

This article proposes an analytic approach to design the typical power factor correction (PFC) control of an electric vehicle (EV) charger to ensure small signal stability in weak grid conditions. Compared to the previous works, the proposed method considers the dynamics of all the control loops, i.e., phase-locked loop (PLL), voltage loop (VL), and current loop (CL). The impacts of key influential parameters on stability are analyzed. Furthermore, the upper limits of the PLL and VL bandwidth to ensure small signal stability are derived. Accordingly, the influences of the CL bandwidth, short circuit ratio (SCR), and the filter inductance on the upper limit of the PLL bandwidth and the VL bandwidth are quantified. Consequently, a design procedure that eliminates the need to model the input impedance for tuning the controller to prevent small signal instability is proposed. Simulations and experiments validate the analysis.

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.

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.

This article 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.

Due to the mismatched feeder impedances in a resistive feeder AC microgrid, it's challenging to accurately share harmonic and active power while promising a low bus voltage distortion rate. To address this issue, this paper proposes a distributed philosophy-based virtual impedance modulation strategy. The proposed method regulates the fundamental and harmonic impedance at the desired value by exchanging information with its adjacent inverters. Notably, the proposed method benefits from resilience against communication delay, failure, and cyber-attacks. Moreover, it significantly reduces the communication burden. The proposed method's effectiveness is validated through experiments conducted in various cases, including different communication scenarios and plug-and-play operations.

The critical short circuit ratio (CSCR), as an important metric for grid stability evaluation, is not clearly defined in the literature. Aimed at clarifying the misunderstandings, the paper compares the different CSCR definitions. Moreover, CSCR reduction-oriented design is studied for electric vehicle chargers. Simulations verify the analysis.

Extracting an electric vehicle (EV) charger's input impedance with the analytical model (white-box approach) or the frequency sweep (black-box approach) is limited by the parameter confidentiality or the measurement noise, respectively. To overcome these challenges, a gradient-descent (GD) optimization-based gray-box modeling approach is proposed. To start with, a sensitivity study on the analytical impedance model of an EV charger with a typical controller is carried out to identify the influential frequency range per controller and circuit parameter. On top of that, given an EV charger with unknown control and circuit information, a GD optimization-based algorithm for multiple parameter estimation is designed to identify the unknown controller and circuit parameters based on the measured impedance, by assuming the EV charger is using the typical controller. Then, an analytical input impedance of the black-box EV charger can be obtained. Moreover, the low-accuracy issue commonly encountered when estimating multiple parameters with GD optimization is mitigated with the proposed algorithm. Compared to pure frequency sweep, the proposed approach achieves a higher accuracy for the coupling impedance and a comparable accuracy for the diagonal impedance. The effectiveness of the proposed approach is validated by experimental results.

This paper proposes a virtual impedance reshaping strategy to share active and harmonic power while promoting the PCC voltage quality. Moreover, the suggested method is resilient to cyber-attacks and immune to communication interruption and delay. Furthermore, it significantly reduces the communication burden. Experiments verify the effectiveness.

Electric vehicles (EVs) are playing a crucial role in achieving the carbon neutral goal. To make the charging experience comparable with the refueling of the gasoline cars, more and more chargers are installed and connected to the grid. Meanwhile, the charging power is going up. As a result, more and more power quality issues associated with EV charging events have already been reported. In this paper, the power quality issues that are relevant to EV charging, including flicker, harmonics, and supraharmonics, are summarized. Their generation mechanisms, harm to the grid, and the promising mitigation measures are discussed. Case studies are also done to mimic the power quality issues in EV charging, and verify the analysis.

Smart grid steady control relies heavily on the communication infrastructure among sensors, actuators, and control systems, which makes it vulnerable to cyber-attacks. Accurate acquisition of dynamic state information is deemed vital for efficient detection of these cyber-attacks on a smart grid. However, several popular state estimation methods at the present stage are restricted in practical use and require some assumptions. In this paper, we investigate the security of smart grid systems. We (1) identify and define the security problem in the smart grid, (2) compare the performance of several state estimate methods including Least Square, Kalman filter, Extend Kalman filter, in identifying smart grid dynamic information using measurements, and (3) investigate the Chi-square detector, Euclidean Distance, and Cosine similarity matching approaches for attack detection.

Distributed secondary control is deemed necessary to restore the state of AC micro-grids to set points. However, for its limited global information, the power electronic system is vulnerable to cyber-attacks that aim to desynchronize converters or even cause a shutdown of micro-grids by unnecessarily triggering the protection schemes. To this end, an adaptive communication weight update for the secondary control layer is proposed. It guarantees frequency synchronization and active power sharing despite the presence of these attacks. Moreover, it automatically dispatches optimal communication lines when all its neighboring data are corrupted to different levels. Finally, the efficacy of the proposed resilient control method is demonstrated using simulations.

In emerging fast-charging stations, DC fast chargers (DCFCs) are employed which rely on power electronics and control to achieve the required performance. Harmonic emission induced by the complex system behavior is of great concern in the DCFC system. This paper proposes a harmonic emission model for the typical electric vehicle charger design, i.e., two-level active front end. The technique is based on the Fourier series method and the impedance model which is able to reveal the harmonic current emission of DCFCs under different grid conditions. Time-domain simulations are presented subsequently to validate the proposed model.

With growing concern on climate change, widespread adoption of electric vehicles (EVs) is important. One of the main barriers to EV acceptance is range anxiety, which can be alleviated by fast charging (FC). The main technology constraints for enabling FC consist of high-charging-rate batteries, high-power-charging infrastructure, and grid impacts. Although these technical aspects have been studied in literature individually, there is no comprehensive review on FC involving all the perspectives. Moreover, the power quality (PQ) problems of fast charging stations (FCSs) and the mitigation of these problems are not clearly summarized in the literature. In this paper, the state-of-the-art technology, standards for FC (CHAdeMO, GB/T, CCS, and Tesla), power quality issues, IEEE and IEC PQ standards, and mitigation measures of FCSs are systematically reviewed.

This article presents a small-signal model for power-electronics converters that use a typical control structure in wind energy applications: the double synchronous reference frame current control. The article considers the presence of unbalanced currents and voltages, and analyzes their impact on the frequency couplings of the converter. In addition, it is revealed that, in the presence of negative-sequence voltage synchronization, the converter presents an additional coupling at -2f_1-f_p.

To accurately simulate the harmonic emission of EV DC fast chargers (DCFCs) and the harmonic voltage of the power grid to which the chargers are connected, a small time-step, i.e., typically smaller than 10μs, is required. However, for harmonic assessment, a long timescale, typically a day, is required. A conflict between accuracy and time efficiency exists. To address this issue, a multitimescale modeling framework of fast charging stations (FCSs) is proposed in this paper. In the presented framework, the DCFCs’ input impedance and harmonic current emission in the ideal grid condition, i.e., the grid impedance is zero and there are no background harmonic voltages, is obtained firstly through a converter switch model with a small timescale. Since the DCFC’s input impedance and harmonic current source change in the charging course, the input impedance and harmonic emission at different input power should be obtained. Then, the DCFCs’ input impedance and harmonic emission will be used in the fast-charging station modeling, where the DCFCs are simplified as their Norton equivalent circuits. In the station level modeling, a bigger time step, i.e., 1 minute, is used, since the DCFCs’ operating power can be assumed as a constant in one minute. With this framework, the FCSs’ long-term power quality performance can be assessed efficiently without neglecting the DCFCs’ small timescale dynamics.