Abstract:Solid oxide fuel cell (SOFC) is an efficient and environmentally friendly energy technology, used in distributed generation, transportation and residential systems. However, high-temperature operation, multi-physics coupling, and long-term degradation issues create significant control challenges. This paper reviews recent advances in SOFC control with key findings showing a shift from classical methods to intelligent systems. Parameter optimization currently employs algorithms such as the response surface method to obtain optimal control objectives. Fault diagnosis and performance prediction combine physical models with machine learning. Control strategies are evolving from traditional control toward intelligent control. Furthermore, application studies guide the tailoring of these advanced controllers to real-world settings. Despite these progresses, challenges remain due to insufficient control intelligence or weak fault tolerance. To address these gaps, this paper outlines two future research paths, including lifecycle digital twins for predictive health management, and explainable AI control for creating trustworthy systems by embedding physical constraints and ensuring decision transparency.

Abstract:Microgrid (µG) systems have emerged as a rapidly expanding concept in modern power systems due to its inherent capability to integrate multiple distributed generators (DGs) and renewable energy sources (RESs). However, the intermittent and dynamic nature of these sources, whose outputs depend heavily on weather conditions, introduces several operational challenges to µG systems, among which arc faults (AFs) represent a critical safety concern. Despite increasing research interest in µG protection, comprehensive review studies focusing on hybrid AC/DC microgrids (HAC/DCµGs), particularly regarding AF classification, detection, and protection strategies, remain limited. In HAC/DCµG systems, the simultaneous operation of AC and DC subsystems introduces additional complexity, as AFs occurring on either side can significantly compromise system reliability and operational safety. Accordingly, this paper provides a systematic review of AF phenomena, fault types, and reported case studies, along with existing mitigation strategies and their technical limitations. Furthermore, AF detection approaches based on metaheuristic optimization are critically reviewed, with particular emphasis on their effectiveness in improving detection accuracy and reducing misclassification. The review reveals that no single detection technique can reliably address AFs under diverse operating conditions and uncertainties, thereby highlighting the need for adaptive, multi-domain, and optimization-assisted protection frameworks to ensure reliable HAC/DCµG operation.

Abstract:Zero-carbon islanded microgrids (ZC-IMGs) operating without fossil-fuel generators, face major challenges in maintaining frequency and voltage stability due to the lack of synchronous inertia and reliable voltage reference. The integration of inverter-based grid-forming energy storage systems (GFM-ESSs) provides a viable solution; however, their coordination requires communication-efficient and scalable control strategies. To address these issues, this paper proposes a novel event-triggered distributed control framework for ZC-IMGs. First, a detailed nonlinear state-space model is developed to accurately characterize the dynamic behavior of the microgrid and provide a solid foundation for control design. Then, adaptive event-triggering and distributed parameter estimation mechanisms are introduced, which significantly reduce communication requirements, ensure minimum inter-event intervals, and eliminate Zeno behavior without centralized coordination. Subsequently, a distributed priority-based charging protocol is designed to guarantee fair and stable state-of-charge management across multiple GFM-ESS units. Simulation results show that the proposed method achieves excellent frequency and voltage regulation, fast recovery from disturbances, and effective state-of-charge management. At the same time, it reduces communication events by 92% compared with traditional continuous distributed control. The proposed framework therefore provides a scalable solution with significantly reduced communication requirements.

Abstract:The rapid advancements in cloud computing and other next-generation information and communication technologies have fueled the swift development of data centers (DCs). Aggregating these DCs to form virtual power plants (VPPs) for supporting grid frequency regulation has substantial potentials. However, previous research on DC frequency regulation has overlooked two major issues: inadequate consideration of clustering analysis for geographically dispersed DCs with similar load characteristics, and neglecting the influence of uncertainty within communication networks on power dispatch strategies for frequency regulation. To address these issues, this paper proposes a novel co-design method for power dispatch and communication transmission to facilitate frequency regulation for multiple VPPs based on DC aggregation. First, a DC aggregation strategy based on Ng-Jordan-Weiss spectral clustering algorithm is devised to construct the VPPs aggregated by DCs and determine their aggregation regulation capability. Based on this, a joint design approach is developed, which integrates a power dispatch strategy considering communication network uncertainty with a routing path optimization scheme for mitigating the network uncertainty. The method obtains precise power regulation tasks, thereby ensuring the reliability of VPPs' frequency regulation. Finally, comprehensive simulation results substantiate the viability and efficacy of the proposed methodology.

Abstract:Existing oscillation suppression strategies based on parameter tuning involve complex modeling processes, and their parameter adjustment ranges are constrained by the fundamental frequency performance. Meanwhile, oscillation suppression strategies based on control structure optimization fail to meet the suppression requirements under varying oscillation frequency conditions. Therefore, neither approach can achieve rapid and stable suppression of oscillations in permanent magnet synchronous generator (PMSG) connected voltage source converter-based multi-terminal direct current (VSC-MTDC) transmission systems under diverse operating conditions and scenarios. To address this problem, the response characteristics of different control system links under disturbances are analyzed, revealing the mechanism by which sending-end disturbances induces oscillatory instability. Based on the growth rates of interaction energy amplitudes between subsystems, the dominant interaction energy components responsible for oscillatory instability are identified. Then, using the output power of the static var generator and super capacitor as feedback signals, a control strategy is proposed to regulate the reference values of their inner current loops and compensate for the dominant interaction energy components. By suppressing the increasing of stored energy, this method can quickly suppress the oscillation. Simulation tests conducted on an RT-LAB semi-physical platform verify that, the proposed method can effectively suppress oscillations caused by sending-end disturbances within hundreds of milliseconds, while preserving the fundamental-frequency response characteristics of system power and voltage.

Abstract:Single-signal analysis is typically used to monitor the condition of oil-immersed transformers (OITs) for post-fault diagnosis. However, existing methods lack sensitivity to detect early-stage operational deviations that precede equipment failures. This paper focuses on proactive health monitor and detects pre-fault anomalies through multi-physics signal analysis. Current, oil temperature, and box amplitude of OITs are systematically analyzed considering their cross-domain interactions. A novel Transformer-Kolmogorov-Arnold networks (Transformer-KAN) architecture is developed to predict OIT electrical-thermal-vibration signal, and detect pre-fault anomalies. First, the cross-correlation analysis method is used to accurately quantify the delay time between OIT current, oil temperature, and vibration signals. Then, a prediction model is constructed based on Transformer self-attention mechanism and KAN nonlinear feature decomposition capability. Finally, reconstruction error is introduced to jointly optimize the OIT pre-fault anomaly detection model with the prediction error. The prediction task and anomaly detection task are combined. An on-site 500 kV OIT is used as a case study. The results show that the proposed method can significantly enhance the prediction accuracy of multi-dimensional OIT operation data and effectively detect pre-fault anomalies, providing support for OIT health monitoring and early warning.

Abstract:The open environment of cyber-physical power systems (CPPS) exposes the safe operation of power grids to the threat of false data injection attacks (FDIA). Most existing detection methods for such cyberattacks suffer from limitations such as insufficient feature learning ability, slow detection speed, and inability to accurately identify attack locations. Therefore, this paper proposes a FDIA localizing detection method based on sparrow search algorithm with circle chaos initialization and firefly disturbance strategy improved multi-layer extreme learning machine, i.e., CFSSA-ELMML. The multi-layer extreme learning machine (ELMML) is adopted as the deep feature extraction model and the basic classifier to address the limitation of insufficient feature learning ability of existing detection methods. Moreover, an improved sparrow search algorithm (i.e., CFSSA) with strong local search ability is utilized to optimize the initial weight and bias of the multi-layer extreme learning machine, enabling fast and accurate localization of false data injection attacks. The effectiveness of the proposed method is verified through simulations on the IEEE14-bus and 57-bus systems. It is verified that the proposed method results in better location performance evaluation criteria compared with other detection methods.

Abstract:Large-scale renewable generation bases transmitted via voltage source converter (VSC)-HVDC system (RGBTVS) have emerged as a key approach for renewable power transmission in China's desert regions. The controller parameters of the VSCs significantly affect the small-signal stability (SSS) of RGBTVS. In this paper, a robust controller parameter optimization (RO) model for improving the SSS of a RGBTVS considering the injected power uncertainty is established. A support vector machine regression surrogate model is employed to approximate the highly nonlinear relationship of minimum damping ratio (MDR) and both controller parameters and renewable energy station (RES) output, thereby eliminating the need for matrix inversion and eigenvalue calculation in the SSS constraints. The RO problem is decomposed into two single-layer optimization problems which are solved iteratively using column-and-constraint generation (C&CG) algorithm. The master problem focuses on optimizing controller parameters to improve the system SSS under the worst scenario of RES output, while the sub-problem searches the RES output corresponding to the worst MDR under the specific controller parameters within the uncertainty set. A case study based on a practical RGBTVS in China verifies the correctness and effectiveness of the proposed method.

Abstract:Existing studies on large-signal stability of grid-following (GFL) converters predominantly focus on the AC current control timescale, often assuming a constant DC-link voltage or relying on chopper control. In contrast, this paper addresses the overlooked dynamics within the DC-link voltage control timescale, where the cumulative effects of multiple control loops critically influence system stability. First, a novel dynamic model of the GFL converter is proposed. This model uniquely captures the transient trajectory of the internal voltage, enabling a physics-based quantification of stability through average acceleration during a swing. The transient stability margin of the GFL converter is then quantified based on whether the average acceleration of the internal voltage swing is positive or negative. This trajectory-based analysis provides a unique method for investigating transient stability. Furthermore, the large-signal stability of the GFL converter connected to a weak grid is analyzed under various physical scenarios. This is achieved by analyzing the average acceleration during the swing process in conjunction with phase portraits, which together provide valuable insights into the transient stability mechanism of this nonlinear system. The research reveals that, in a weak grid, the large-signal stability of the GFL converter diminishes as the DC-link voltage control bandwidth approaches the phase-locked loop (PLL) bandwidth. Conversely, stability improves as these two bandwidths diverge. Time-domain simulations and experimental tests are conducted to validate the theoretical analysis.

Abstract:The data used for intraday wind power forecasting (WPF) are often collected in non-stationary environments. Accuracy can be substantially reduced due to concept drift caused by significant changes in operational conditions of wind farms or in the probability distribution of samples. An adaptive intraday WPF model considering multi-timescale concept drift detection is proposed to address the above challenges. First, a spatio-temporal forecasting model is constructed to achieve intraday WPF. Then, an integrated mask-reconstruction representation learning pretraining strategy is employed to extract hidden representations of input historical wind power measurements and numerical weather prediction data. The degree of concept drift in the sample stream is quantified by measuring the cosine similarity between current and historical hidden representations. Finally, two concept drift detection modules with different time-scales are employed to guide the model in performing multi-stage adaptive update, enabling it to accommodate varying degrees of concept drift and achieve a balance between accuracy and flexibility during the detection process. Case studies based on 3 real wind farm clusters demonstrate the proposed method's superior forecasting accuracy and computational efficiency.

Abstract:Fault behaviors of inverter-interfaced renewable energy sources (IIRESs) are primarily governed by the inverter control strategy. Uncertainties introduced by control response and network topology change pose challenges to the protection design of islanded microgrids. First, this paper establishes equivalent positive-sequence fault component (PSFC) models of IIRESs with and without activation of fault ride through (FRT) control. Their equivalent impedance angles are then analyzed under high- and low-resistance faults, revealing the dominant differences in fault direction features between forward and reverse faults. Subsequently, binary state fault direction indicators are constructed using the base frequency phase difference and high frequency cosine similarity between the PSFC voltage and current. A hybrid frequency direction feature-based protection scheme is then proposed, and its robustness against variations in FRT control response, fault scenarios, and network topology is demonstrated. The exchanging of binary state indicators between line terminals requires only low communication bandwidth. Finally, simulation results on a 10 kV islanded microgrid system validate the effectiveness of the proposed scheme under various conditions, including different fault resistances, fault inception angles, measurement noises, network topological changes, and IIRES control strategies.

Abstract:Pantograph-rigid catenary (PC) systems are used for transferring electric energy to locomotives via a sliding contact between the contact wire and the pantograph slide. Contact surface ablation caused by frequent offline arcs significantly affects the service life of the PC system. While previous studies mainly focus on PC arcs at static separation distances, this paper discusses the ablation characteristics of the contact wire caused by transient arcing during a single complete dynamic separation process of the PC system. First, the multi-physics theory of PC arcs is introduced, and a corresponding magnetohydrodynamic model is established to accurately simulate the arc occurrence, development, and extinction. Next, by accounting for heat loss due to material evaporation, an ablation model for transient arcing effects on the contact wire is developed, and the model is verified using experimental data from a metro line. Finally, by introducing different dynamic separation behaviors, the ablation characteristics of the contact wire under various factors are revealed. The results indicate that the ablation of the contact wire due to transient arcing is directly proportional to the current and inversely proportional to the crosswind velocity. Furthermore, the ablation is more severe during the re-contact process of the PC system than during its separation process.