Peng Zhang

This paper explores the implementation of Traveling Wave Protection (TWP) in microgrids through the integration of Internet of Things (IoT) technologies and a Spiking Recurrent Neural Network (SRNN). Microgrids present unique fault de-tection challenges, as conventional protection techniques can be hindered by reduced fault currents, bidirectional power flow, and communication latency. By leveraging high-frequency traveling wave signals, TWP offers rapid and precise fault localization. In parallel, IoT-enabled sensing provides real-time data acquisition and decentralized decision-making. The proposed SRNN further enhances fault classification and location accuracy by combining spiking neuron dynamics with recurrent memory. Hardware-in-the-loop experiments on both simplified and complex microgrids demonstrate the method’s effectiveness in minimizing misclas-sification while maintaining low latency and reduced power consumption. This work extends our previous IoT-based TWP research by adopting a neuromorphic framework suitable for microgrid edge deployments, paving the way for more adaptive and robust protection solutions in modern distribution networks.

This letter introduces a novel quantum Newtonian power flow (QNPF) algorithm. The QNPF features a more general quantum circuit that can process non-Hermitian matrices with fewer required qubits and eliminates the need for iterative optimization of gate parameters. Our contributions include: 1) Developing a quantum state-based Newton's power flow framework to enhance accuracy, convergence, and versatility; 2) Integrating quantum singular value transformation to efficiently solve each iteration of QNPF with scalable quantum circuits; and 3) Devising a block-rescaling technique to ensure computational accuracy in ill-conditioned cases. Test results validate the accuracy, scalability and numerical stability of QNPF, underscoring its potential to advance quantum power flow computation.

The combinatorial nature of restoration decisions, especially under increased grid-forming inverter-based resources (IBRs), renders classical optimization intractable for large systems. To potentially address this, this paper develops a coordination-free distributed quantum computing (DQC) framework for rapid and reliable distribution system restoration. A two-stage approach is developed, where microgrids (MGs) first restore loads locally using distributed quantum solvers, followed by a network restoration. To enable execution on noisy intermediate-scale quantum hardware, a compact quantum-compatible formulation is constructed via a tailored variable-reduction strategy and a constraint-scaling penalization method that yields minimal quadratic unconstrained binary optimization models with high fidelity. Case studies on modified IEEE 37- and 123-node feeders demonstrate that the proposed method accelerates restoration by up to 30% while maintaining high restoration optimality, and is compatible with both annealing- and circuit-based quantum platforms.

Unprecedented extreme climate events cause devastating infrastructure outages within power systems. Comprehensive outage identification is essential for the identification of critical components to ensure the uninterrupted power supply in a secure manner to withstand extreme weather events. Accurate outage identification, however, requires simulations of a large number of outage scenarios necessitating highly scalable computations thus challenging classical computing paradigms. Quantum computing provides a promising resolution by exploiting exponential scalability achieved through superposition and entanglement of voltage states. This paper devises a quantum contingency analysis (QCA) method to identify outage scenarios on Noisy Intermediate-Scale Quantum (NISQ) devices. Advanced quantum circuits incorporating Pauli-twirling, dynamic decoupling, and matrix-free measurement are designed to mitigate hardware-induced errors. A preconditioned hybrid method is devised to alleviate the computation burden of parameter optimization of quantum gates. Case studies identify line and generation outages via QCA in typical power systems. Our research underscores that quantum computing exhibits exponential scalability in identifying power grid outages and critical components.

The paper pioneers and optimizes a systematic approach to developing Physics-Informed neuro-Models (PIM) for the transient analysis of power grids interconnected with inverter-based resources. PIM serves as an effective online digital twin of power components, incorporating physical knowledge and preserving the system’s nonlinear differential structure while requiring only minimal data for training. Three contributions are presented: 1) An Physics Informed Neural Network (PINN)-enabled neuro-modeling approach for constructing an accurate ElectroMagnetic Transient (EMT) model; 2) A data-physics hybrid, multi-neural learning structure that demonstrates PIM’s adaptability at varying levels of data availability; 3) A balanced-adaptive PIM automatically optimizes the learning process while ensuring alignment with physical principles. Tests on rotating and static electrical components, as well as an IEEE test system, validate its efficacy for transient grid analysis under diverse operational scenarios.

This letter introduces a novel compact and lossless quantum microgrid formation (qMGF) approach to achieve efficient operational optimization of the power system and improvement of resilience. This is achieved through lossless reformulation to ensure that the results are equivalent to those produced by the classical MGF by exploiting graph-theory-empowered quadratic unconstrained binary optimization (QUBO) that avoids the need for redundant encoding of continuous variables. Additionally, the qMGF approach utilizes a compact formulation that requires significantly fewer qubits compared to other quantum methods thereby enabling a high-accuracy and low-complexity deployment of qMGF on near-term quantum computers. Case studies on real quantum processing units (QPUs) empirically demonstrated that qMGF can achieve the same high accuracy as classic results with a significantly reduced number of qubits.

Traveling wave protection based on the Internet of Things (TWP-IoT) is developed to enable ultraresilient microgrids. TWP-IoT adopts a directional traveling wave (TW) approach that utilizes the voltage and current modal components obtained from discrete Hilbert transform (DHT) to identify the fault features via ultralightweight IoT hardware. The main contributions of this work include 1) a novel TWP-IoT for microgrid and distribution networks that can achieve the time-of-arrival fault detection and localization with composite wave impedance and conductance using DHT; 2) a cost-effective smart IoT prototype with functionalities outperforming commercial relays; and 3) a real-time hardware-in-the-loop prototype based on real time digital simulator that verifies the robustness and efficacy of TWP-IoT subject to a diverse set of working conditions. TWP-IoT is found to have excellent performance under a wide spectrum of internal and external fault conditions across a myriad of microgrid locations, which is unattainable by today’s TWP products.

Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids. However, the design of SCs usually depends on specific application requirements and may not be adaptive enough to the frequently-changing grid conditions caused by the transition from conventional to renewable power generation. This paper devises a software-defined virtual synchronous condenser (SDViSC) method to address the challenges. Our contributions are fourfold: 1) design of a virtual synchronous condenser (ViSC) to enable full converter wind turbines to provide built-in SC functionalities; 2) engineering SDViSCs to transfer hardware-based ViSC controllers into software services, where a Tustin transformation-based software-defined control algorithm guarantees accurate tracking of fast dynamics under limited communication bandwidth; 3) a software-defined networking-enhanced SDViSC communication scheme to allow enhanced communication reliability and reduced communication bandwidth occupation; and 4) Prototype of SDViSC on our real-time, cyber-in-the-loop digital twin of large-wind-farm in an RTDS environment. Extensive test results validate the excellent performance of SDViSC to support reliable and resilient operations of wind farms under various physical and cyber conditions.