Abstract:Power system protection has evolved significantly due to the ongoing energy transition and digitalization. The development and standardization of information and communication technologies (ICTs) used for power system protection, monitoring, and control have led to the digitalization of substations and the introduction of new protection and control schemes. These include virtualized centralized protection and control for intra-substation applications, as well as advanced wide-area monitoring, protection, and control (WAMPAC) for inter-substation applications. This paper reviews the development of virtualized centralized protection, with a focus on key practical advancements, emerging technologies, and state-of-the-art studies in centralized protection and control (CPC) and W AMP AC systems. It also identifies directions for future research.

Abstract:Hybrid commutation converters (HCCs) utilizing reverse-blocking integrated gate commutation thyristors (IGCTs) have gained significant attention due to their immunity to commutation failure. Leveraging the recovery enhancement characteristics of IGCTs, HCCs demonstrate superior performance at reduced extinction angles, thereby minimizing reactive power consumption. This study presents a comprehensive investigation into reactive power control strategies for HCCs operating at small extinction angles. First, the topological configuration and commutation principle of HCC are elucidated. Subsequently, the mechanism of HCC reactive power control is analyzed, and a reactive power control strategy is proposed by combining the converter transformer taps with extinction angles. Moreover, the relationship between transformer taps and reactive power exchange under different rated extinction angles is calculated, and the theoretically rated extinction angle is proposed. Finally, to validate the proposed control strategy, a four-terminal ultra-high voltage direct current power grid incorporating HCC technology is modeled and simulated using PSCAD/EMTDC. The simulation results demonstrate that the proposed strategy effectively supports AC systems by reducing reactive power absorption in HCCs, while simultaneously exhibiting enhanced reliability and economic efficiency.

Abstract:The increasing significance of text data in power system intelligence has highlighted the out-of-distribution (OOD) problem as a critical challenge, hindering the deployment of artificial intelligence (AI) models. In a closed-world setting, most AI models cannot detect and reject unexpected data, which exacerbates the harmful impact of the OOD problem. The high similarity between OOD and in-distribution (IND) samples in the power system presents challenges for existing OOD detection methods in achieving effective results. This study aims to elucidate and address the OOD problem in power systems through a text classification task. First, the underlying causes of OOD sample generation are analyzed, highlighting the inherent nature of the OOD problem in the power system. Second, a novel method integrating the enhanced Mahalanobis distance with calibration strategies is introduced to improve OOD detection for text data in power system applications. Finally, the case study utilizing the actual text data from power system field operation (PSFO) is conducted, demonstrating the effectiveness of the proposed OOD detection method. Experimental results indicate that the proposed method outperformed existing methods in text OOD detection tasks within the power system, achieving a remarkable 21.03% enhancement of metric in the false positive rate at 95% true positive recall (FPR95) and a 12.97% enhancement in classification accuracy for the mixed IND-OOD scenarios.

Abstract:During sending-end faults in the hybrid cascaded HVDC (HC-HVDC) system, the transient voltage drop characteristics under the interaction of the AC/DC hybrid system remain unclear, and the reactive power support provided by the HC-HVDC to the sending-end AC system requires further investigation. To address this problem, the reactive power interaction coupling mechanism between the sending-end AC system and the HC-HVDC is revealed, and the transient voltage mathematical model considering fault severity and duration is established. Under the dynamic change of the AC system voltage, the difference between the reactive power provided only by the reactive power compensation devices and by the combined modular multilevel converters (MMC) and reactive power compensation devices is analyzed. It is concluded that using MMC to provide a proportion of reactive power enhances the reactive power support to the AC system during faults. Then, the transient voltage model considering the reactive power support of MMC is established, and the critical reactive power consumption of line commutated converter (LCC) is quantified. It is concluded that the reactive power consumption of LCC exceeding its critical value deteriorates the transient voltage. A coordinated support strategy for the sending-end AC system based on reactive power support of MMC and reactive power regulation of LCC is proposed. It can effectively address the challenge of weakened reactive power support to the AC system due to voltage drop, thereby preventing the unbalanced reactive power from deteriorating the transient voltage, and realizing active support of the transient voltage. Finally, a simulation model is established on the PSCAD/EMTDC platform, and the simulation results validate the effectiveness of the proposed strategy in supporting the transient voltage, under different fault types, durations, severities, and locations.

Abstract:The incorporation of high percentages of renewable resources into integrated energy systems (IES) is accelerating, and it becomes challenging to identify low-carbon economic dispatch options with significant uncertainties. This paper proposes an enhanced structure that combines hydrogen storage, power-to-gas, carbon capture and storage, and hydrogen fuel cells to extend CO2 reduction pathways. The structure is embedded within an IES that considers multi-energy network constraints. First, the low-carbon economic dispatch model is formulated as a multi-objective interval optimization problem minimizing the total fuel cost and carbon emissions of the IES comprising electricity, heat, gas, and hydrogen subsystems. Then, the multi-objective optimization problem is solved by set-based group search interval optimizer (Set-GSIO) to construct an interval-based Pareto frontier while preserving the uncertainty information for decision-making. In addition, a decision support method based on Shannon entropy and the technique of ordering preferences for similarity of ideal solutions (TOPSIS) evaluates the interval solutions in terms of convergence, stability, and security. Finally, case studies are conducted on a modified IEEE30-bus system integrated with a 15-node gas network and a 32-node heat network to verify the effectiveness of the proposed architecture and approach. Furthermore, the proposed approach is demonstrated on a larger-scale test case, and simulation results verify its scalability.

Abstract:With the widespread adoption of digital equipment in intelligent substations, testing digital signals in power systems has become an important role for relay protection test equipment. Testing and calibrating digital signals require high accuracy. However, existing methods have low precision, cannot be calibrated at full range for all indexes, and have complex configuration, making them unsuitable for routine calibration work. To solve the above problems, a novel calibration method is designed and implemented using field programmable gate array (FPGA) to achieve accurate input and output time control. Accurate calibration relies on multiple forms of traceability including theoretical value traceability based on waveform comparison, time scale value traceability based on accurate time stamps, and algorithm traceability based on typical algorithms. Compared with other existing methods, the proposed approach reduces the mean absolute error of action time and time measurement by 92.88%, effectively addressing a key industry challenge and offering a valuable reference for further research, application, and standardization.

Abstract:Battery energy storage systems bolster power grids” absorption capacity, however, battery safety issues remain a formidable challenge. Timely and precise fault diagnosis, coupled with early-stage fault warnings., is crucial. This study introduces an eigen decomposition-based multi-fault diagnosis approach for lithium-ion battery packs, enabling online diagnosis of short circuits, electrical connection faults, and voltage sensor malfunctions. By incorporating an interleaved measurement topology, precise fault type differentiation is achieved. Eigenvector matching analysis is employed to increase sensitivity to fault characteristics and enhance robustness. The interleaved topology can be seamlessly integrated using common voltage measurement solutions, eliminating the need for additional design complexities, while sensor number redundancy enhances fault tolerance of battery management systems (BMS). A cloud-side collaboration method is proposed, where the BMS functions as an edge device for specific data computations, while the parameters are fine-tuned by the server through big data analytics. This approach circumvents cumber-some server calculations, thereby curbing server cost escalation. The edge computing process is divided into two steps, with partial calculations often sufficient to evaluate battery safety, thus reducing the computational load on edge devices. Several battery tests are conducted, and the results confirm the method's capability, feasibility, and validity in early-stage fault diagnosis.

Abstract:The transient behavior of DC-link voltage (DCV) significantly affects the low-voltage ride-through for phase-locked loop (PLL)-based grid-connected doubly-fed induction generator (DFIG) systems. This study investigates the DCV transient behavior of a PLL-based DFIG system under asymmetrical grid faults. First, by considering the coupling characteristics of positive and negative sequence (PNS) components, a nonlinear large-signal model of DCV is developed. Furthermore, the transient characteristics of DCV under varying parameters are analyzed using phase trajectory diagrams. In addition, the transient stability (TS) mechanism of DCV during asymmetrical faults is examined through an energy function approach. The analysis indicates that the transient instability of DCV is primarily associated with the control characteristics of PNS PLLs, while the TS level of DCV is mainly determined by the power coordination control between the rotor side converter and grid side converter. Moreover, a coordinated control strategy is proposed to enhance the TS of DCV under asymmetrical grid faults. Finally, both simulation and experimental results are presented to validate the theoretical analysis and the effectiveness of the proposed strategy.

Abstract:This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity, gas, and heating networks. Introducing a cutting-edge stochastic gradient-enhanced distributionally robust optimization approach, this study integrates deep learning models, especially generative adversarial networks, to adeptly handle the inherent variability and uncertainties of re-newable energy and fluctuating consumer demands. The effectiveness of this framework is rigorously tested through detailed simulations mirroring real-world urban energy consumption, renewable energy production, and market price fluctuations over an annual period. The results reveal substantial improvements in the resilience and efficiency of the grid, achieving a reduction in power distribution losses by 15% and enhancing voltage stability by 20%, markedly outperforming conventional systems. Additionally, the framework facilitates up to 25% in cost reductions during peak demand periods, significantly lowering operational costs. The adoption of stochastic gradients further refines the framework's ability to continually adjust to real-time changes in environmental and market conditions, ensuring stable grid operations and fostering active consumer engagement in demand-side management. This strategy not only aligns with contemporary sustainable energy practices but also provides scalable and robust solutions to pressing challenges in modern power network management.

Abstract:Traveling wave (TW) fault location technology has been widely used in transmission systems due to its high accuracy and simplicity. Recently, there has been growing interest in applying this technology to medium voltage (MV) distribution lines. However, current practices in its deployment, signal measurement, and threshold setting are usually from the application experiences in transmission lines, despite significant differences in fault-induced wave characteristics between transmission and distribution systems. To address these issues, this paper investigates the feasibility and applicability of TW fault technology in MV overhead distribution lines through characteristic analysis of fault-induced TWs. The propagation characteristics of aerial mode and zero mode TWs on overhead distribution lines are studied. Furthermore, it evaluates the influence of critical distribution network components including distribution transformers, multi-branch configurations, and busbar structures on wave propagation characteristics. Deployment strategies for traveling wave fault location (TWFL) devices is proposed to address the unique challenges of distribution networks, while the fault location method is also improved. Field test results demonstrate the effectiveness of the proposed methodology, showing improved fault detection accuracy and system reliability in distribution network applications. This research provides practical implementation suggestions for TWFL technology in distribution networks.

Abstract:The annual compliance cycle of the carbon trading system allows generation companies (GenCos) to decouple the timing of carbon allowance purchases from their actual emissions. However, trading a large volume of allowances within a single day can significantly impact on carbon prices. Faced with uncertain future carbon and electricity prices, GenCos must address a challenging multistage stochastic optimization problem to coordinate their carbon trading strategies with daily power generation decisions. In this paper, a two-layered hybrid mathematical-deep reinforcement learning (DRL) optimization framework is proposed. The upper DRL layer tackles the stochastic, year-long carbon trading and allowance usage optimization problem, aiming for long-term optimality and providing guidance for short-term decisions in the lower layer. The lower mathematical optimization layer addresses the deterministic daily power generation schedule problem while en-forcing strict technical constraints. To accelerate learning of the annual compliance cycle, a decision timeline transfer learning method is proposed, enabling the DRL agent to progressively refine its policy through sequentially training on monthly, weekly and daily decision environments. Case studies demonstrate that, with these methods, a GenCo can reduce emission costs and increase profits by effectively leveraging carbon price fluctuations within the compliance cycle.

Abstract:With the prevalence of renewable distributed energy resources (DERs) such as photovoltaics (PVs), modern active distribution networks (ADNs) suffer from voltage deviation and power quality issues. However, traditional voltage control methods often face a trade-off between efficiency and effectiveness, and rarely ensure robust voltage safety under typical state perturbations in practical distribution grids. In this paper, a robust model-free voltage regulation approach is proposed which simultaneously takes security and robustness into account. In this context, the voltage control problem is formulated as a constrained Markov decision process (CMDP). A safety-augmented multi-agent deep deterministic policy gradient (MADDPG) algorithm is the trained to enable real-time collaborative optimization of ADNs, aiming to maintain nodal voltages within safe operational limits while minimizing total line losses. Moreover, a robust regulation loss is introduced to ensure reliable performance under various state perturbations in practical voltage controls. The proposed regulation algorithm effectively balance efficiency, safety, and robustness, and also demonstrates potential for generalizing these characteristics to other applications. Numerical studies validate the robustness of the proposed method under varying state perturbations on the IEEE test cases and the optimal integrated control performance when compared to other benchmarks.