This paper proposes an optimal power-sharing algorithm (OPSA) devised to regulate the power distribution among grid-forming inverters (GFMIs) in modern power systems powered by distributed energy resources (DERs). Unlike traditional power-sharing techniques that assume an infinite or constant power supply, OPSA continuously modifies virtual impedance based on resource availability, such as the battery capacity and state of charge (SOC). The supervisory controller integrating OPSA calculates the demanded power to adjust the virtual impedance, which is optimally distributed among the GFMIs, minimizing battery stress and promoting balanced discharge. Furthermore, the proposed approach improves grid resiliency by dynamically adapting to changing load variations and mitigating potential instabilities. Finally, the effectiveness of the OPSA is validated by the simulation results of multiple test scenarios across varying operating conditions.

The integration of solid-state transformers (SSTs) with grid-forming inverters (GFMIs) improves flexibility and control in modern microgrids while simultaneously introducing new cyber-physical vulnerabilities. The strong coupling between SSTs and GFMIs creates dynamics that result in the diffusion of localized cyberattacks throughout the microgrid. This paper provides a comprehensive impact analysis of four representative cyberattack scenarios: side-channel noise intrusion (SNI) compromising the integrity of SST measurement feedback, tampering with the voltage-regulation loop of the SST, a virtual governor frequency-restoration attack on GFMI, and cascaded tripping of breakers simulating insider access to protection coordination. The attacks are targeted towards specific control or protection layers and are evaluated through simulations on a modified IEEE 14-bus system. Results demonstrate that even brief attacks can result in prolonged instability, including frequency deviations, power oscillations, and voltage regulation failures, due to the low inertia and damping of power electronic-based systems. Furthermore, the coupling between the SST and the GFMIs results in aftereffects even well beyond the removal of the attack, degrading overall system. The findings underscore the importance of recognizing control-layer cyber threats in power electronic-dominated microgrids and motivate the need for more resilient control architectures.

Mass penetration of renewable energy sources (RES) has drastically triggered the integration of clusters of VSG-based grid-forming inverters (GFMIs) to enhance grid stability and facilitates the transition towards power electronics dominated grid (PEDG). Although high number of GFMIs provides a smoother frequency response due to higher emulated virtual inertia, considering the complex dynamic of the PEDG, estimating the emulated virtual inertia by the primary control layer of the GFMIs introduces extensive mathematical modeling and computational burden. To address the shortcomings of conventional model-based approaches, this paper proposes a data-driven methodology based on convolution neural network (CNN) to estimate effective virtual inertia by analyzing the explanatory frequency response of a cluster of GFMIs. The neural network (NN) is trained offline and evaluated on a sample cluster of 100% GFMIs through multiple case studies to validate the effectiveness of the proposed methodology. The application of the proposed data-driven tool can further facilitate network analysis by considering the corresponding aggregated model to enhance the stability analysis of the PEDG.

Single-phase inverters are essential for converting DC to AC in various applications. This paper introduces a novel transistor-less active ripple mitigation (ARM) method using an augmented discontinuous modulation scheme (ADMS) on a differential mode buck inverter (DMBI). By optimizing the switching process and using small AC capacitors, this approach significantly reduces the size of the DC link capacitor while also reducing switching losses and enhancing overall efficiency using a simple control system. The proposed method's effectiveness is demonstrated through a comparative analysis before and after applying the harmonics injection.

With the evolving characteristics and dynamics of the grid, Solid-State Transformer (SST)-based Power Substations (SSPS) are being proposed as a new solution to effectively incorporate distributed generation and energy storage systems. However, these power converter-based solutions are vulnerable to cyber and physical threats due to their mixed signal characteristics (analog and digital data processing) and communication requirements, leading to severe consequences if the attacks remain undetected and unmitigated. To identify potential threats, this paper proposes a real-time supervised machine learning-based anomaly detection system (ML-ADS) for detecting Electromagnetic Side-channel Noise Injection (EM-SNI) attacks on SSTs. The proposed system can detect anomalies with fine granularity, meeting real-time latency requirements for effective mitigations. The model was deployed and optimized for evaluation into a realistic hardware-in-the-loop (HIL) SST testbed environment. Our experimental evaluation demonstrates a promising performance with high accuracy (more than 99%) and low false-negative rates (less than 1%), with feasible latency (≈200ms).

This paper proposes a resilient control scheme for a Solid-State Power Transformer (SST) based power substation (SSPS). The proposed control scheme consists of a centrally located secondary aggregator and primary controller that govern the operation of corresponding SST cells. A cross-correlation-based Intrusion Detection (CC-IDS) mechanism estimates the cross-correlation coefficient between the global voltage feedback and a reference signal. As the estimated cross-correlation drops below the preset threshold, an intrusion on the SST’s global voltage feedback is detected, and a Convolutional Neural Network (CNN) enabled attack mitigation mechanism is triggered. The CNN estimator utilizes the SSPS’s attacked global input voltage feedback to predict the desired global input voltage. Estimated global input reference is then substituted with noisy global input voltage feedback in the SST cell’s primary controllers to mitigate the impacts of the intrusion. To ensure high prediction accuracy, the CNN estimator is trained using datasets encompassing various side-channel noise intrusion (SNI) attack scenarios. Several scenarios are emulated in MATLAB Simulink environment to validate the CNN intrusion mitigation approach under the influence of different attack vectors.

The solid-state transformer (SST) is a developing technology which is being integrated in low and high-voltage power systems, supporting the integration of renewable energy in smart grid applications and fast charging stations for electric vehicles (EV). SST is becoming more critical in certain applications of advanced power distribution systems as it provides several advantages over the conventional transformer including its controllability, small weight and size, potential low cost, and simplicity of connection for offshore wind farms. However, some challenges arise in modular SST design and implementation including control architecture for equal power balancing, protection, and communication compatibility that need to be addressed. This paper provides an overview of different SST communication protocols and offers prospects, applications, and future directions for research and development. A control architecture for input and output voltage balancing is presented for a direct power conversion (DPC) type SST system. A detailed SPI communication protocol is also provided which allows the implementation of decentralized operation of the modular SST system.

Microgrids in power electronics dominated grid (PEDG) should be able to operate under islanded and grid-connected modes and switch between them seamlessly. During the islanding events severe fluctuations in voltage and frequency of the microgrid may arise that can cause unstable operation and ultimately blackout. Moreover, under this transition the inverter may lose synchronism or point of synchronization because of disconnection from grid. This paper proposes: (i) a predictive control scheme that enables dual-mode (grid-forming and grid-following) operation of inverters and enables seamless transition between these two modes; and (ii) a swift single-point synchronization scheme for the network of grid-following and grid-forming inverters. The proposed control scheme mitigates voltage, current, and frequency fluctuations and enables seamless transition while needing limited time for synchronization between the grid-connected and the islanded modes of operation. Moreover, the predictive controller ensures smooth toggling between grid-following and grid-forming modes of operation and extracting maximum power from photovoltaic energy sources. The effectiveness of the proposed control scheme with its self-synchronization capability is validated by various case studies under grid-tied, islanded and transition from grid-connected to islanded mode of operations.

In this paper, a hierarchical distributed energy management of multimicrogrids (MMGs) with energy routing is proposed. Existing control strategies for power sharing, transient performance, and economic-emission dispatch in microgrids with distributed generators (DGs) fall short in providing good dynamic performance. To address this issue, a hierarchical distributed optimization is proposed by using top–down approach, which decomposes original economic-emission dispatch of MMG scenario into individual microgrid (MG) and energy routing subproblems. Distributed electric vehicle charging, intermittent photovoltaic source, and battery energy storage system are incorporated in the optimization model. Using multiagent system model for DG, a dynamic performance controller (DPC) is proposed for each MG to achieve improved performance during transients. Convergence of optimization algorithm is proved using Lyapunov theory. Performance evaluation results show that the proposed DPC for economic-emission dispatch improves system performance significantly during either load or generator switching.

Power efficiency of multi-source integrated energy harvesting (EH), storage, and delivery is a fundamental requirement of emerging energy autonomous internet-of-thing (IoT) and internet-of-everything (IoE) devices. To make these energy autonomous devices practical, an enhanced, universal power management integrated circuit (PMIC) needs to be developed for efficiently controlling power on-chip. Currently, there exists no universal power manager (UPM) that seamlessly interfaces to DC and/or AC EH sources. The state of the art viable PMICs delivered by industry rely primarily on the following approach: for an unregulated DC source a DC/DC power manager is adopted whereas for an AC source, a front-end rectifier is inserted. This reduces efficiency and enhances distortion, requiring additional filtering.

An approach for sequential transmission of high-frequency (HF) power and data signals over a common HF channel (i.e., co-transmission) is outlined. This is in contrast to the conventional power-line communication, where the power and data co-transmission is simultaneous. Sequential co-transmission avoids data corruption by temporally distributing power and data signals over an HF channel and limiting their overlap. A data-transfer mechanism to realize the sequential co-transmission approach is outlined. Simple transmitter and receiver circuits, synthesized without the use of any analog-filtering circuitry are designed and a modified asynchronous serial-communication-interface protocol is implemented for ensuring the integrity of the transmitted data. The sequential HF power and data co-transmissions are experimentally validated using a closed-loop distributed dc/dc converter operating in switching frequency range of 100 kHz.

Efficient utilization of distributed generation (DG) resources in a microgrid requires coordinated control, which can be realized using multiagent-based system model. The coordinated control requires information exchange among the distributed agents, which can be implemented using either periodic or need-based aperiodic data transmission. For reducing the data communication requirements among the agents, an aperiodic self-triggered communication-based coordinated control is proposed. Centralized as well as distributed self-triggered coordinated control is implemented. The performance evaluation results show that self-triggered aperiodic communication requires lower data rates, while delivering the same performance as that of periodic sampled data control.

This paper presents a proposal to modify power supply systems currently used in ac-fed railways with neutral zones (NZs), in order to allow power-flow routing. The proposed system complements the existing infrastructure with additional power-electronic devices connected in parallel to both sides of the NZs, allowing control of power flow through adjacent electrical sections. The description and control of such a modified railway system is outlined in this paper. In addition, a mixed-integer programming optimization problem is formulated, which minimizes the investment and the operation costs, while ensuring the power supply to the train traffic. This optimization model is used to allow a systematic evaluation of the benefits of implementing such a railway smart grid system. Finally, a section of the high-speed line between Madrid and Barcelona is used as a case study, and the advantages of the proposed system are quantified in two different scenarios.

The design and simulation results of three-phase differential current-source inverter is presented in this paper. It is shown that three modules of current-source converter could be configured and controlled to obtain a symmetrical three-phase at the output. The current source inverter could be used with lower voltage sources. It could also be possible to integrate all three transformers and reduce the 60 Hz component saturation effect. The structure is modular and can be used as three dc-dc converter in parallel, one symmetrical three phase inverter, single-phase inverter or split-phase inverter based on the application.

This paper1outlines a methodology [1] to analyze the reaching condition of a class of switching system, which will be referred to as switching power converter (SPC), using a piecewise linear (PWL) model and multiple Lyapunov method. The reaching criteria for orbital existence are formulated as a linear matrix inequality (LMI), which is solved using a convex optimization solver. Further, the criterion is modified to distinguish the different modes of convergences of the reaching dynamics. A case study using a basic SPC is provided to illustrate the applicability of the proposed methodology.

A modulation-based fault-tolerant switching scheme (FTSS) is outlined for an isolated high-frequency-link (HFL) inverter. It comprises a front-end Dc/pulsating-Dc converter followed by an Ac/Ac converter. At first, the lost phase in the Dc/Ac Converter is identified based on the type of distorted output line voltages and then modulation references are changed accordingly so as to restore the output line voltages. The proposed FTSS ensures operation under different types of fault conditions in the front-end Dc/pulsating-Dc converter without redundant circuits. Detailed results on the switching scheme as well as experimental validation using a fabricated prototype are presented.

Wireless networks, for distributed control systems in general and interactive power networks (IPNs) in particular, demand a fresh look to minimize communication delays and improve throughput performance, while transmitting data having size on the order of few bytes. Using variable packet length time division multiple access (TDMA) approach at the medium access control (MAC) protocol layer we propose a merged-packet routing (MPR) algorithm at the network layer for an all-to-all broadcast to improve the throughput and delay performances. Taking into account the effect of channel noise we show that the idea of packet merging provides noticeable performance gain for the throughput without compromising the delay. The delay performance is further improved by adjusting the transmitter power according to the packet length.

Delay, one of the most important parameters in the wireless control networks, is largely dependent on the packet length when using contention based medium access control (MAC) protocol in single hop networks. In this paper, we have developed a three-state discrete time Markov-chain based model for the performance evaluation of MAC protocol. Using this Markov-chain model, we analyze the carrier sense multiple access (CSMA) MAC protocol for its delay and throughput characteristics in the presence of transmitter power control. Using simulations, we measure the probability of capture (which gives a measure of the effectiveness of transmitter power control due to the capture effect.) and validate these simulated data experimentally. To analyze the effect of transmitter power variation, the Markovchain model is modified by incorporating the probability of capture. Numerical results are obtained to measure the performance improvement.