Detecting inter-turn short circuits in ultra-high voltage shunt reactors is challenging due to weak fault signatures. This paper establishes an accurate three-segment fault model and derives, for the first time, explicit analytical expressions for the short-circuit phase equivalent resistance $(\boldsymbol{R}_{\mathbf{eq}})$ as a function of both short-circuit turn ratio $(\boldsymbol{\alpha})$ and transition resistance $(\boldsymbol{R}_{\mathbf{k}})$. In particular, this model reveals specific $\boldsymbol{R}_{\mathbf{eq}}$ variation laws: monotonic decrease with $\boldsymbol{\alpha}$ for metal faults and non-monotonic behavior for transition resistance faults. Leveraging these insights, an innovative identification method is proposed. Its core innovation is a theory-driven, flexible threshold $(\boldsymbol{R}_{\mathbf{set}})$ calculation based solely on the derived $\boldsymbol{R}_{\mathbf{eq}}=\boldsymbol{f}(\boldsymbol{\alpha},\boldsymbol{R}_{\mathbf{k}})$ function and user-defined pa-rameters (minimum detectable $\boldsymbol{\alpha}_{\mathbf{min}}$ and maximum tolerable $\boldsymbol{R}_{\mathbf{k}\_\mathbf{max}}$), thereby reducing reliance on empirical values. Simulation results validate the proposed model and method, demonstrating superior sensitivity and ac-curacy, especially for small-turn inter-turn short circuits with transition resistance, compared to conventional ze-ro-sequence methods. This provides a robust theoretical foundation for practical fault detection.
With the increasing penetration of renewable energy, ensuring reliability and security of power supply has become a significant challenge. In this paper, a novel photovoltaic (PV) intraday power supply guarantee capability forecasting method is proposed. Different from the conventional PV power forecasting methods, it can provide diverse forecasting information, including guarantee power, low output power period, and power supply guarantee probability. PV guarantee power which represents a conservative prediction of PV power is forecasted based on convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) model with a compound loss function. PV low output power period represents the period when the power gap between the theoretical maximum PV power and the actual PV power is higher than a specific threshold. It is forecasted using hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model based on an improved spatiotemporal feature encoding method. The power supply guarantee probability which represents the risk of PV power shortage is obtained using quantile regression model based on gradient boosted regression tree (GBRT) considering different PV power supply demands. Furthermore, new indexes, including guarantee rate, guarantee energy ratio, success index, forecasting gain index, and probability forecasting accuracy are proposed to evaluate the forecasting performance. The effectiveness of the proposed method is verified based on actual operation data in a province in Northwest China.
Non-intrusive load monitoring (NILM) has gained widespread attention for improving residential energy efficiency by analyzing appliance-level energy consumption. Although machine learning-based NILM methods have demonstrated excellent performance, their effectiveness heavily relies on the availability of sufficient training data. Federated learning (FL) has emerged as a promising approach for collaboratively training NILM models by aggregating distributed knowledge across clients, effectively harnessing decentralized data reserves. However, due to significant data distribution discrepancies among clients, the global model aggregated through FL often deviates from the client-specific optimum. To address this challenge, this paper proposes a directed knowledge transfer-based personalized model learning method. In this method, clients acquire eligible models through peer-to-peer communication and perform cross-architecture knowledge transfer via knowledge distillation. Furthermore, a data-driven model trust evaluation mechanism is designed to pre-screen candidate models and guide directed knowledge transfer, thereby reducing communication overhead and improving transfer efficiency. Additionally, consistency learning is introduced to mitigate potential overfitting during personalized model training. Extensive experiments on three public datasets, PLAID, WHITED and HOUIDI, demonstrate that the proposed method achieves superior training efficiency and performance compared to existing methods.
With the continuous expansion of wind power integration, dual-sequence synchronization stability has become a critical concern for wind farms under asymmetrical grid faults. In particular, in multi-parallel systems, stability analysis is further complicated by various coupling effects. To address this challenge, a dual-sequence nonlinear dynamic model is developed to identify and characterize three types of coupling interactions. By incorporating the equal area criterion, the impact of coupling effects on dual-sequence synchronization stability under different scenarios is analyzed. On this basis, a method for calculating the coupling critical point is developed under accordance with grid code requirements, which can be utilized to determine appropriate current injection levels for wind farms. Finally, simulation results are presented to validate the effectiveness of the theoretical analysis and the proposed calculation method.
The reliability of All-DC wind farms is of great significance for enhancing the consumption capacity of renewable energy and promoting the construction and development of new power systems. This paper conducts a detailed analysis of the topological structure of All-DC wind farms. Based on the sequential Monte Carlo method, a reliability assessment model is constructed. From dimensions such as space and objects, reliability assessment indicators of hierarchical classes, object classes, time limit classes, and degree classes are defined, and a multi-level and multi-link reliability index system is established. At the same time, the cloud droplet influence value integrated cloud model method is introduced. By using the normal cloud generator, the randomness of index data are effectively processed, and a comprehensive assessment of the multi-dimensional reliability of All-DC wind farms is achieved. Finally, a 100 MW wind farm in Northwest China is taken as an example for simulation verification. The results fully demonstrate the effectiveness and superiority of the proposed method, when compared to conventional approaches. The proposed method can provide key technical support for the planning, operation, and maintenance of All-DC wind farms and the integration of new energy into the grid.
Although carbon flow is a powerful tool for assigning emission responsibility to consumers, it has not been explored in hybrid AC-DC systems. This paper presents a novel probabilistic carbon-flow model to quantify the distribution of carbon intensity in such systems. To further characterize the importance of uncertain inputs, such as renewable energy and loads, on probabilistic carbon flow, a global sensitivity analysis (GSA) strategy is introduced. To alleviate the computational burden of traditional Monte Carlo methods in quantifying these metrics, it incorporates an adaptive polynomial chaos expansion (PCE)-based surrogate model. This significantly reduces the computing burden while maintaining high statistical accuracy. Simulations validate the proposed carbon flow model in the hybrid AC-DC system and reveal the excellent performance of the PCE-based GSA method.
To improve the state-of-charge (SOC) balancing ability and reduce the power loss, this paper proposes a discontinuous pulsewidth modulation (DPWM)-based battery power management method for cascaded H-bridge converter-based battery energy storage systems (CHB-BESS). First, two types of voltage-clamping principles are designed for each submodule (SM) of the CHB-BESS. Second, to address the limitations of conventional DPWM with fixed low power adjustment in CHB converter applications, the proposed method determines the maximum number of voltage-clamping SMs according to their SOC values and the output voltage references of the CHB-BESS. As a result, the SOC balancing ability can be fully exploited under both active and reactive power operation conditions, and the total power loss of the CHB-BESS can be substantially reduced. Finally, voltage-clamping SMs are selected according to their SOC sorting results, and the output voltage references of remaining non-voltage-clamping SMs are recalculated for modulation. Simulation and experimental results indicate that under varying active and reactive power operation conditions, the proposed method can improve the SOC balancing speed and reduce the power loss of the CHB-BESS.
With the rapid adoption of electric vehicles (EVs), the inadequate deployment of urban charging infrastructure has intensified the mismatch between charging demand and power supply capacity. This paper proposes a multi-objective joint optimization planning method for electric vehicle charging stations (EVCSs) and electric vehicle swapping stations (EVSSs). First, a dynamic path optimization method based on real road network topology is presented. Then, considering EV travel characteristics and power consumption models, a spatial-temporal charging demand forecasting model is proposed. Furthermore, electric-traffic coupling principles are integrated into the planning framework to meet the refined requirements of power grid and road network coupling. Finally, the proposed approach is validated using the road network of a city in southern China, which is divided into multiple subregions based on points of interest, together with the IEEE 118-bus test system. The forecasting results reveal a bimodal temporal pattern of charging demand, with peak loads concentrated in public, workplace, and commercial areas during the daytime and shifting predominantly to residential areas at night. The planning result shows that 8 EVCS and 5 EVSS should be deployed within the study area.
Protection challenges are common issues in low-voltage direct current systems, particularly in scenarios involving high-impedance faults and arc faults. These faults may escalate into flashovers, accompanied by current distortion, thereby causing severe damage to equipment. To address these issues, this paper proposes a backup protection scheme based on the discrete wavelet transform to reliably detect and classify such faults in distribution lines. Distinguishing fault conditions from resistive load is challenging due to their similar current magnitudes, the proposed method introduces a technique for square pulse injection into the power lines. This active approach effectively differentiates these cases, thereby preventing erroneous tripping. The proposed standalone approach can be easily integrated into existing intelligent electronic devices to enhance conventional current-based protection methods. The algorithm generates binary signals representing the magnitude of current waveform distortion at both positive and negative poles. By analyzing these signals through decoders, the system state is accurately identified, triggering protective actions as necessary. This ensures robust protection and continuous power delivery despite challenging fault conditions.
When single-phase grounding (SPG) faults occur in lines with inverter-based resources (IBRs) at both ends, traditional non-unit protections may malfunction due to the limited amplitude and controlled phase of zero-sequence current. To address this issue, this paper proposes an integrated sequence component-based non-unit protection scheme. By calculating the remote zero-sequence current using only local components, the method eliminates the need for real-time communication. The resulting fault distance calculation is highly resilient to fault resistance and is independent of positive-sequence current injections by IBRs. The proposed method seamlessly meets the reactive power support requirements of various grid codes. Simulation results confirm the effectiveness of the proposed method for SPG fault protection for lines with IBRs at both ends.
During grid short-circuit faults, voltage sags at the point of common coupling affect the power delivery of grid-following (GFL) converters, thereby increasing the risk of converter disconnection. This paper analyzes the causes of power oscillation in GFL converters after short-circuit faults, elucidates the critical fault voltage condition leading to the loss of synchronization. Subsequently, it proposes a frequency compensation strategy for the phase-locked loop (PLL) based on power synchronization control. By suppressing the increase in the post-fault PLL frequency, the proposed strategy prevents power oscillation and reduces the risk of converter disconnection. The effectiveness of the proposed control strategy is verified through electromagnetic transient simulations using PSCAD/EMTDC and hardware-in-the-loop experiments. The results provide a foundation for developing fault ride-through control strategies for GFL converters.
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.
Microgrid (& micro;G) 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 & micro;G systems, among which arc faults (AFs) represent a critical safety concern. Despite increasing research interest in & micro;G protection, comprehensive review studies focusing on hybrid AC/DC microgrids (HAC/DC & micro;Gs), particularly regarding AF classification, detection, and protection strategies, remain limited. In HAC/DC & micro;G 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/DC & micro;G operation.
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.
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.
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.
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.
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.
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.
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.
