Iet Generation Transmission & Distribution最新文献

In active distribution networks (ADNs), power electronic devices like inverters introduce noise interference into fault monitoring signals, compromising identification accuracy. This challenge is particularly pronounced in high-impedance grounding faults, where weak fault signatures degrade conventional detection performance. This study proposes an improved residual CNN architecture, ResNet-Kolmogorov-Arnold-network (R-KAN), for accurate single line-ground fault (SLGF) identification. The method leverages the rich fault features contained in transient zero-sequence current (ZSC) and zero-sequence voltage (ZSV) waveforms following SLGFs in DG-integrated systems. The model employs superimposed ZSC-ZSV images as inputs and replaces standard ReLU activation with KAN functions, reducing linear components and computational burden. A comprehensive dataset generated through PSCAD simulations trains the R-KAN alongside conventional neural networks. Comparative evaluations demonstrate R-KAN's superior classification performance across multiple metrics. Rigorous testing, including high-resistance fault scenarios, noise interference conditions, and missing data cases confirms the model's enhanced generalization capability. Field validation using actual recorded waveforms further verifies the model's practical effectiveness in real-world SLGF identification. The proposed approach addresses critical challenges in modern ADNs by combining advanced network architecture with optimized feature extraction from transient zero-sequence components.

This paper presents a novel ferromagnetically shielded fault current limiter (FS-FCL) configuration and control strategy for power systems. The proposed system introduces a controlled DC-reactor design that effectively limits fault currents without significantly affecting the system voltage during normal operation. The main novelty of this work lies in the development of a fully ferromagnetically shielded core and the series connection of the FS-FCL with the power line, which provides an immediate response to fault conditions without delay. In this configuration, the reactor's magnetic flux density is distributed throughout the ferromagnetically shielded core, which increases the effective inductance and prevents magnetic core saturation during fault events. In addition, a control-oriented model of the FS-FCL is developed, together with an improved control algorithm based on reference voltage and current thresholds. Both qualitative analyses and quantitative simulations were performed to evaluate the system's transient and steady-state performance. Simulation results under various fault conditions confirm that the proposed controlled FS-FCL achieves faster current limitation and lower voltage distortion compared with uncontrolled configurations, demonstrating its feasibility for practical power grid applications.

The penetration of renewable energy sources (RES) in Cyprus's power system has increased significantly in recent years. However, this growth poses substantial challenges, primarily due to limitations in distribution networks arising from network congestion and voltage security issues. This paper presents a comprehensive review of existing solutions for enhancing RES hosting capacity, together with a robust methodology for evaluating their effectiveness. To address voltage-related challenges, centralised voltage control strategies at power transformers are combined with adaptive inverter settings to ensure stable operation under increasing RES penetration. Network reinforcements and medium-voltage (MV) upgrades are also considered as complementary measures. At the low-voltage (LV) level, export limitation schemes (ELS) tailored for residential prosumers are proposed, with optimal limits determined for both single- and three-phase installations at targeted RES penetration levels. The effectiveness of the proposed solutions is validated using real MV and LV networks from the Cyprus distribution system, ensuring alignment with the strategic planning framework of the distribution system operator of Cyprus. The findings provide a scientific basis for optimising RES integration, addressing both operational and strategic challenges in modern power systems.

Distribution system operators are facing distributed energy resources connection requests in low voltage distribution networks. Since low voltage networks are traditionally not planned, the requests are assessed by comparing them to the rules defined in the national regulations. We analyze the Croatian regulatory framework and investigate the distributed energy resources connection rules defined in the Croatian regulations. Furthermore, we present an analytical framework to calculate the static hosting capacity and dynamic operating envelopes defined as dynamic hosting capacity. Multiple case studies and scenarios are defined to assess the possibility of the distributed energy resources connection in the real-world low voltage network model using smart meter electricity demand measurements. The results show shortcomings of the traditional approach since the grid code rules either underestimate hosting capacity or allow the distributed energy resources connection that endangers the network's safe operation. Based on the objective function, strictly following regulatory limits can result in an underestimation of possible generation by up to 360% or an overestimation of around 10%. Similar trends are observed for additional demand, with the network's capacity to accommodate new units being overestimated by nearly 300% or underestimated by more than 70% on a daily basis. Furthermore, the results suggest distribution system operators should implement novel concepts due to a more precise assessment of the distributed energy resources integration, allowing new installations and ensuring low voltage networks are operated within the boundaries. The steps for implementing the presented concepts in distribution systems are discussed. Findings of the study enable distribution system operators to move away from traditional approaches and end-users to further invest in distributed energy resources units, creating economic and environmental benefits.

To ensure stable and reliable operation of interconnected DC microgrids (DC MGs), the development of advanced interlink converters (ICs) is essential. These converters facilitate effective power transfer between DC MGs under varying operational conditions, such as droop based power transfer, maximum and zero power transfer. This work is concerned with designing an IC system flexible across all the interconnected DC MGs. To ensure its performance, the system stability under diverse voltage scenarios must be assessed where there is incomplete knowledge of each interconnected subsystem. Moreover, a single IC struggles to handle increased system capacity, which is exacerbated by the rising integration of distributed energy resources. This paper introduces two major advancements to address these challenges. First, it develops a new control strategy, combining droop based control and a novel state machine, for ICs that adapts to multiple operational scenarios, using just the information of the local grid voltages, ensuring system stability. Second, it implements a distributed consensus control algorithm, supplemented by a sparse communication network, to manage multiple ICs operating in parallel, thereby enhancing overall system reliability. Various case studies, simulated using MATLAB/SIMULINK demonstrate the effectiveness of these approaches.

Environmental concerns in recent years have encouraged governments worldwide to integrate large proportions of renewable energy sources (RESs) into electricity networks. However, the intermittent nature of the RESs has introduced new challenges, such as the imbalance between electrical supply and demand. The power-to-gas (P2G) and gas-fired units (GFUs) are practical solutions to overcome these challenges. Therefore, this paper presents a bi-level bidding strategy for GFUs, P2G facilities and RESs in a coordinated energy market considering the demand response program (DRP). To enhance the flexibility of the system, DRP is modelled as a price-responsive bidding mechanism for shifting loads. Furthermore, the stochastic nature of RESs and load is modelled using a scenario tree concept. In the bi-level model, the upper-level problem maximises the profits of the GFUs and P2G facility, while the lower-level problem maximises social welfare in the energy market. The nonlinear bi-level formulation is changed into a single-level mixed-integer linear programming problem by substituting the lower-level optimisation by its Karush-Kuhn-Tucker (KKT) optimality conditions and converting nonlinearities to linear equivalents. The CPLEX solver in the GAMS optimisation program is utilised to solve the model. The beneficial effect of the suggested model was evaluated using an adjusted twenty-node Belgium gas network and an adjusted sixteen-bus UK distribution system. Numerical results show that when natural gas demand is limited, the production of gas from the P2G facility can improve the secure and cost-effective functioning of the coordinated electricity and gas networks. It further shows that by converting renewable electricity into natural gas, P2G facilities can enable a broader penetration of volatile renewable energy.

With the increasing penetration of renewable energy in multi-energy microgrids (MEMG), the uncertainty caused by the randomness and intermittency of renewable power output has become increasingly prominent, posing significant challenges to the low-carbon and economic operation of MEMG. To address these issues, this paper proposes a two-stage distributionally robust low-carbon economic optimisation model based on Kullback–Leibler (KL) divergence. First, a two-stage distributionally robust optimisation (DRO) framework and objective function for the MEMG are constructed. Then, a linearised tiered carbon trading model, equipment models and an integrated demand response (IDR) model of MEMG are developed. Furthermore, to address the uncertainty in the distribution probability of wind power and photovoltaic (PV) output, a KL divergence-based ambiguity set is constructed, and a KL divergence-based min-max-min two-stage DRO model is proposed. Finally, a column-and-constraint generation (C&CG) algorithm is employed to decompose the DRO model into a master problem and slave problem for iterative solving. A case study based on a real MEMG located in Hunan Province, China, demonstrates that the proposed two-stage distributionally robust low-carbon economic optimisation model can effectively address the uncertainty caused by renewable energy, also balancing both economic and robust performance.

This paper proposes a novel travelling wave (TW) fault location method for multi-terminal transmission lines (MTTLs). First, the red-billed blue magpie optimisation (RBMO)-based successive variational mode decomposition (SVMD) is employed to decompose the aerial-mode current TW signal, thereby obtaining a series of intrinsic mode components (IMF) and the corresponding centre frequency of each IMF. Second, the Teager energy operator (TEO) is utilised to extract the energy mutation point from the IMF with the maximum kurtosis value, enabling the determination of the TW front arrival time. Then, based on the principle of double-terminal TW fault location, the fault distance matrices A and B for MTTLs are constructed using frequency-related TW velocity. Finally, the fault section identification and location for MTTLs are achieved using the constructed fault distance matrices. Extensive simulation studies conducted on the PSCAD/EMTDC platform demonstrate that the proposed method exhibits excellent adaptability and robustness across various fault scenarios. Compared with existing TW-based methods, the proposed method achieves superior fault location accuracy, with location errors maintained below 150 m.

The increasing penetration of renewable energy resources such as wind power into modern power systems presents great challenges to the design of conventional model-based damping controllers, for example, inaccurate/unavailable model structure and parameters due to business privacy. In view of this, the paper proposes a data-driven adaptive damping (DDAD) control method of doubly fed induction generators (DFIG) for damping enhancement, which depends only on real-time input/output data and can accommodate variations of system operating conditions. In the method, a pseudo-gradient-based data-driven model is established via a partial form dynamic linearization technique. A pseudo-gradient updater is then developed to adaptively adjust pseudo-gradients based on online data. Furthermore, two control modules, that is, a sliding mode control module to ensure robustness and an optimal predictive control module to improve damping performance, are designed and the DDAD control law is then derived. Afterwards, simulation results based on the DFIG-integrated New England 10-machine 39-bus system are used to validate the effectiveness and robustness of the proposed method for various operating scenarios.

Z. Yin, R. Zhu, S. Cui, et al., “Optimisation Scheduling of Integrated Electricity-Heat-Oxygen Energy Systems Considering Demand Response and Carbon Trading Based on IGDT,” IET Generation, Transmission & Distribution 19, no. 1 (2025): e70126.

In the ‘Funding/Acknowledgements’ section, the text ‘Tibet Agriculture and Animal Husbandry College (YJS2025-42)’ was incorrect. This should have read: ‘Graduate Education Innovation Program Funding Project of Tibet Agriculture and Animal Husbandry University (YJS2025-42).’

We apologize for this error.