Search Results (124)

This paper presents a measurement-based framework for studying and mitigating control interactions between power system stabilizers (PSSs) and HVDC modulation damping controllers in hybrid AC/DC systems. Using frequency-response data obtained from small-signal injections, the method embeds driving-point and transfer impedance directly into the control loops, eliminating reliance on simplified analytical models. A lightweight optimizer adjusts controller gains and lead–lag angles to enhance damping at the inter-area mode while ensuring HVDC-to-PSS dominance, magnitude-crossing consistency, and a minimum damping margin across the 0.3–1.5 Hz band. The approach, implemented in ETAP 16.0 and MATLAB R2024a (MathWorks, Natick, MA, USA), successfully improves damping and maintains stability under all tested conditions, providing a practical co-design strategy for coordinated PSS–HVDC control in weakly interconnected networks. Full article

The increasing penetration of converter-interfaced renewable energy resources is accelerating the transition of conventional distribution networks toward hybrid AC/DC architectures, where photovoltaic generation, battery energy storage, electric mobility, and mixed AC/DC loads are coupled through multiple power electronic interfaces. While these architectures offer important advantages in flexibility and integration efficiency, they also introduce tighter interactions between AC-side and DC-side operating behavior, making coordinated assessment increasingly important under variable operating conditions. Despite growing interest in hybrid AC/DC systems, comparative studies that jointly examine system-level stability and voltage-quality-related behavior across renewable penetration levels and stressed operating scenarios remain limited. This paper proposes a reduced-order comparative screening framework for renewable-rich hybrid AC/DC distribution systems, using stability- and voltage-quality-related indicators based on a representative reduced-order benchmark model. The adopted framework combines scenario-based simulation with unified AC-side, DC-side, transient, and composite performance indicators to evaluate how different converter coordination strategies influence operating robustness under renewable intermittency, abrupt load changes, converter operating-point variations, and different renewable penetration levels. The considered indicators include voltage deviation, overshoot, violation duration, transient fluctuation, converter utilization, and composite operating-robustness measures; they are intended as system-level voltage-dynamics proxies rather than as a complete harmonic or standards-based power-quality assessment. The results indicate that adaptive coordinated control provides the strongest DC-side robustness under stressed conditions, whereas droop-based coordination often offers a favorable practical compromise between AC-side and DC-side performance. The analysis also reveals a clear trade-off between DC-side regulation and AC-side voltage-quality-related behavior, highlighting the need for joint multi-domain evaluation. In particular, the improved DC-side robustness obtained with adaptive coordination is accompanied by slightly higher AC-side voltage-quality-related deviations in several scenarios. Within the scope of the adopted reduced-order benchmark, the proposed framework provides a practical and reproducible basis for identifying critical operating regions and for supporting higher-fidelity future studies on robust renewable integration in hybrid AC/DC distribution networks. Full article

This paper presents a Nyquist-based method for assessing control interactions between a Power System Stabilizer (PSS) and an HVDC Modulation Supplementary Damping Controller (MSDC) in hybrid AC/DC networks. Loop-at-a-time perturbations are applied to reveal how one controller deforms the other’s Nyquist contour, directly exposing frequency-dependent coupling. A spectral-radius margin is introduced as a quantitative robustness indicator. Reduced-order transfer functions identified using the Matrix Pencil Method enable accurate frequency-response analysis from transient-stability data. Application to Kundur’s two-area system with an embedded LCC–HVDC link demonstrates that the method clearly exposes controller dominance, interaction severity, and gain-sensitivity effects. The proposed framework thus provides a practical and measurement-compatible means for visualizing and coordinating damping controllers in weak hybrid AC/DC networks. Full article

This paper presents the design and validation of an adaptive islanding detection method (AIDM) for an AC/DC hybrid microgrid integrated with a hybrid energy storage system (HESS) comprising a supercapacitor and a battery. The proposed AIDM combines dual-tree complex wavelet transform (DTCWT), synthetic minority oversampling technique (SMOTE), and long short-term memory (LSTM) network to effectively detect islanding and non-islanding conditions in the microgrid following faults/disturbances. Fault and disturbance signals are captured at the point of common coupling, following which they are extracted and decomposed using DTCWT. The SMOTE algorithm is employed for data preprocessing to balance the dataset and enhance the accuracy of the intelligent classifier. Finally, LSTM is used for training and testing the AIDM for different faults/disturbance classification and detection. Two categories of datasets, $T_{D1}$ and $T_{D2}$, are used for testing the AIDM. The results obtained from MATLAB/Simulink show that datasets incorporated with HESS achieve higher detection accuracy of 100% compared to datasets without HESS with average accuracy of 99.77% under sudden load increase. It is also established that the proposed AIDM maintains robustness when exposed to noise signals, confirming its reliability under noisy conditions. Full article

With the advancement of carbon peaking and carbon neutrality goals, the increasing penetration of renewable energy sources such as wind and photovoltaic power poses severe challenges to the power supply reliability of AC/DC hybrid distribution networks due to their fluctuating, intermittent, and stochastic outputs. This paper proposes a reliability assessment method for AC/DC hybrid distribution networks under large-scale renewable energy integration based on clustering of typical operating scenarios. The net load duration curve is adopted as the feature variable to characterize typical operating scenarios. An improved t-distributed Stochastic Neighbor Embedding (t-SNE) nonlinear dimensionality reduction method with Kullback–Leibler (KL) divergence elbow correction is proposed for effective reduction of high-dimensional time-series data. An adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) parameter optimization method based on the k-nearest-neighbor curve and a secondary K-means clustering method based on entropy-weighted multi-objective optimization are further developed, forming a hybrid t-SNE-DBSCAN–K-means clustering algorithm. The power supply reliability is then assessed based on the clustered typical operating scenarios. A typical AC/DC hybrid distribution network is used as the test system. Results show that the DB index of the proposed clustering method improves by at least 22% compared with conventional methods, the maximum relative error between the typical-day-based and full time-series simulation results is less than 6%, and the computational efficiency improves by about 8.8 times, achieving a good balance between accuracy and efficiency. Full article

Convolutional Neural Networks (CNNs) excel at extracting local features, but due to their restricted receptive fields, they often struggle to capture large-scale global context. Transformers leverage self-attention mechanisms to facilitate global interactions, yet the computational cost of standard self-attention scales quadratically with image resolution. To overcome these limitations, we propose MFDA-UNet, which adopts a hybrid architecture of convolution and linear attention for synergistic feature processing. To fully leverage their respective strengths, we design the Mamba-inspired Frequency-Decoupled Attention (MFDA) block. Through frequency decoupling, this block utilizes convolutions to process high-frequency local information, while employing linear attention to model the long-range dependencies of low-frequency global information. To enhance the feature representation capability of linear attention, we construct the Mamba-Enhanced Linear Attention (MELA) block. Inspired by MILA, this block injects Positional Encoding to substitute the forget gate functionality of Mamba and integrates the Mamba block structure into the linear attention mechanism. This design effectively strengthens representational power, accomplishing long-range dependency modeling with highly efficient linear complexity. Furthermore, we introduce the Gated Cross-Scale Attention (GCSA) module to optimize traditional skip connections. It aggregates features via cross-scale linear attention and incorporates Mamba’s high-performance gating mechanism for adaptive feature filtering, achieving precise feature fusion and selection. We conducted extensive experiments on four multi-modal benchmarks: ISIC 2017, ISIC 2018, Synapse, and ACDC. MFDA-UNet achieved improvements in the DSC by 0.44%, 0.15%, 0.53%, and 0.84% across the respective datasets compared to the second-best models. By capturing local and global multi-scale semantics with relatively low computational overhead, MFDA-UNet provides an efficient and robust solution for medical image segmentation. Full article

Smart power management practices are needed for a sustainable EV charging infrastructure due to the fast use of renewable energy resources. An innovative power management structure for a small grid-connected solar PV system-based AC and DC charging station, combined with a backup purpose battery energy system (BESS), is demonstrated in this paper’s study. The sustainability transition is associated with integrating renewable energy resources with a battery storage system, providing a helpful solution for managing large power-demanding entities (EV, microgrid, etc.). In this study, a solar PV system takes 500 datasets (based on data availability or to prevent overfitting) of PV voltage, solar irradiance, and air temperature, and the performance of controlling for the maximum power point tracker by training these datasets using Levenberg–Marquardt (LM), which was implemented in the ANN toolbox and created this technique in MATLAB 2016 or Simulink. Also, using this technique for the estimation and forecasting of the datasets of solar PV systems and EVs obtains better results for achieving further targets. To enhance decision-making capability through optimized technique, we have to find it before forecasting PV power generation and EV datasets throughout the day (24 h). The optimized power flows among solar PV power generation, EV charging demand (including AC charging and DC fast charging), the BESS, and the utility/small grid under several priority operating scenarios. A famous technique for optimization, mixed-integer linear programming (MILP), is applied. In this technique, the objective function is used for the solution of problem formation and compliance with system constraints such as the power balancing equation, charging/discharging limits, SOC limits, and grid export/import exchange limits: basically, equality, inequality, and bounds limits. Optimized results show that the coordinated power flow operations are consented to by EV users, by prioritizing some key points, such as solar PV use at the maximum, reducing the grid power dependency, and the first power flow towards EV charging demand. The verified MILP-based solutions boost the maximum utilization of renewable energy resources, feasible EV charging demand, and scaling power flow among these entities. The key contribution of this study is suitable for different powered EV charging stations based on both AC and DC, with different ratings of EVs (including fast and slow charging). Most solar PV-based generation supports the EVCS and backup for ranking-wise BESS, and grid support for the EVCS. Also, the key contribution of hybrid techniques in this article is divided into two stages: in the first stage, an artificial neural network (ANN) is utilized for estimating the PV voltage at the maximum point and forecasting, while in the second stage, mixed-integer linear programming (MILP) employs optimal power management. Full article

This paper presents three hybrid AC/DC topologies for the CIGRE European low-voltage benchmark grid to evaluate their impact on voltage regulation, current compliance, and power-sharing capability under realistic operating conditions. The proposed topologies integrate a dedicated DC network in parallel with the existing AC infrastructure through voltage source converters (VSCs), enabling controlled power exchange between the two subsystems. This structure facilitates improved voltage support and more flexible integration of distributed renewable energy resources, many of which inherently operate in DC. A decentralized droop-based control strategy is employed as a uniform baseline to control the VSCs and assess the intrinsic performance of each topology. The proposed architectures are evaluated using realistic 24-h load profiles under scenarios with and without droop control. The results demonstrate significant improvements in voltage stability and feeder current management, particularly under high DC penetration conditions. Overall, the study provides a reproducible benchmark framework for topology-level comparison of hybrid AC/DC low-voltage distribution networks. Full article

Power system blackouts remain a major concern for modern electricity networks, as they often result from cascading failures that lead to substantial load shedding and widespread service disruptions. This paper presents a dynamic resilience assessment of hybrid AC/DC power systems and investigates the effectiveness of voltage-source-converter-based high-voltage direct current (VSC-HVDC) technology in enhancing system resilience under outage contingencies. The study contributes by integrating protection devices and their settings into the analysis and by providing a quantitative evaluation of the system response to N-2 and N-3 contingencies using PSS^®E simulations. The demand not served index is used as a measure of resilience, and its cumulative distribution functions are computed to compare the performance of AC and DC interconnections. The results underscore the importance of VSC-HVDC links in mitigating cascading failures, highlighting their potential as a resilience-enhancing component in modern power grids. Full article

Intelligent soft open points (SOPs) and energy storage systems (ESSs) are effective ways to absorb distributed new energy in the spatial and temporal dimensions, and play an important role in improving the new-energy-carrying capacity of distribution networks. Existing planning models for SOPs and ESSs in distribution networks are often nonlinear and non-convex, and are usually transformed into a mixed-integer second-order cone optimization (MISOCP) model. However, this transformation often needs stringent relaxation conditions, and the solution speed and convergence performance of the model are poor. These disadvantages make traditional MISOCP models unsuitable for optimal planning for complex hybrid networks. To overcome these limitations, a joint planning method for AC/DC hybrid networks based on an improved DC power flow (IDCPF) algorithm is proposed in this paper. The proposed method transforms the original nonlinear model into an approximate linear model, improving the solution speed and accuracy of the model. The effectiveness of the proposed method is validated through case studies on an improved AC/DC 43-node network, which demonstrates the accuracy and numerical stability of the planning model. Full article

This paper proposes a hybrid quantum–classical framework for distribution network reconfiguration (DNR) under high distributed generation (DG) penetration, integrating nonlinear AC power-flow validation with the Quantum Approximate Optimization Algorithm (QAOA). Unlike prior quantum-assisted studies that rely on simplified DC or surrogate models, the proposed approach embeds AC-feasible loss evaluation directly within the combinatorial optimization loop. The methodology first evaluates all admissible switching configurations of the IEEE 33-bus system under DG integration using full AC power flow. The resulting loss landscape is compressed into a Quadratic Unconstrained Binary Optimization (QUBO) representation and mapped to an Ising Hamiltonian, enabling variational optimization via QAOA. The dominant configuration suggested by the quantum layer is subsequently validated through AC feasibility analysis. Simulation results show that the coordinated DG + QAOA strategy reduces active power losses from 282.938 kW (baseline) to 95.773 kW, corresponding to a 66.15% reduction relative to the original topology and an additional 20.62% improvement beyond DG-only operation. The minimum bus voltage increases from 0.8828 p.u. to 0.9531 p.u., satisfying IEEE 1547 limits, while requiring only two switching operations. These results demonstrate that embedding AC-consistent validation within a hybrid QAOA framework enhances physical realism, scalability, and solution quality for combinatorial optimization in active distribution networks. Full article

The global shift toward decarbonized power systems is driving unprecedented penetration of variable renewable energy sources, especially wind and solar PV. Legacy grid architectures, built around centralized, dispatchable synchronous generation, are ill-suited to manage the bidirectional power flows, reduced inertia, and new stability constraints introduced by inverter-based resources. Existing research offers deep but fragmented insights into individual elements of this transition, such as advanced power electronics, microgrids, or market design, but rarely integrates them into a coherent architectural vision for resilient, high-renewable grids. This review closes that gap by synthesizing technical, architectural, and institutional perspectives into a unified framework for resilient grid design toward 2030 and beyond. First, it traces the evolution from traditional hierarchical grids to smart, prosumer-centric, and modular multi-layer architectures, highlighting the implications for reliability and resilience. Second, it critically examines the core technical challenges of high VRES penetration, including stability, power quality, protection, and operational planning in converter-dominated systems. Third, it reviews the enabling roles of advanced power electronics, hierarchical control and wide-area monitoring, microgrids, and hybrid AC/DC networks. Case studies from Germany, China, and Egypt are used to distil context-dependent pathways and common design principles. Building on these insights, the paper proposes a scalable multi-layer framework spanning physical, data, control, and regulatory/market layers. The framework is intended to guide researchers, planners, and policymakers in designing resilient, converter-dominated grids that are not only technically robust but also economically viable and socially sustainable. Full article

The increasing integration of hybrid AC/DC networks in modern power systems introduces new challenges in fault detection and grounding scheme design, necessitating advanced techniques for stable and reliable operation. This paper investigates fault detection and grounding schemes in hybrid AC/DC networks using a machine learning (ML) approach to enhance accuracy, speed, and adaptability. Traditional methods often struggle with the dynamic and complex nature of hybrid systems, leading to delayed or incorrect fault identification. To address this, we propose a data-driven ML framework that leverages features such as voltage, current, and frequency characteristics for real-time detection and classification of faults. Additionally, the effectiveness of various grounding schemes is analyzed under different fault conditions to ensure system stability and safety. Simulation results on a hybrid AC/DC test network demonstrate the superior performance of the proposed ML-based fault detection method compared to conventional techniques, achieving high precision, recall, and robustness against noise and varying operating conditions. The findings highlight the potential of ML in improving fault management and grounding strategy optimization for future hybrid power grids. Full article

Hybrid AC/DC microgrids (HMGs) are increasingly recognized as a solution for the transition toward future energy systems because they can combine the efficiency of DC networks with an AC system. Despite these advantages, HMGs still have challenges in protection, cybersecurity, and reliability. Conventional protection schemes often fail due to reduced fault currents and the dominance of power electronic converters in islanded or dynamically reconfigured topologies. At the same time, IEC 61850 protocols remain vulnerable to advanced cyberattacks such as Denial of Service (DoS), false data injection (FDIA), and man-in-the-middle (MITM), posing serious threats to the stability and operational security of intelligent power networks. Previous surveys have typically examined these challenges in isolation; however, this paper provides the first integrated review of HMG protection across three complementary dimensions: traditional protection schemes, cybersecurity threats, and artificial intelligence/machine learning (AI/ML)-based approaches. By analyzing more than 100 studies published between 2012 and 2024, we show that AI/ML methods in simulation environments can achieve detection accuracies of 95–98% with response times under 10 ms, while these values are case-specific and depend on the evaluation setting such as network scale, sampling configuration, noise levels, inverter control mode, and whether results are obtained in simulation, hardware in loop (HIL)/real-time digital simulator (RTDS), or field conditions. Nevertheless, the absence of standardized datasets and limited field validation remain key barriers to industrial adoption. Likewise, existing cybersecurity frameworks provide acceptable protection timing but lack resilience against emerging threats, while conventional methods underperform in clustered and islanded scenarios. Therefore, the future of HMG protection requires the integration of traditional schemes, resilient cybersecurity architectures, and explainable AI models, along with the development of benchmark datasets, hardware-in-the-loop validation, and implementation on platforms such as field-programmable gate array (FPGA) and μPMU. Full article

Hybrid AC/DC grids with High Voltage Direct Current (HVDC) systems enhance grid resilience and enable efficient long-distance power transfer, asynchronous network interconnection, and seamless integration of offshore renewable energy sources. However, ensuring secure and reliable operation of these complex hybrid systems, particularly under contingency scenarios, presents significant challenges. This paper proposes a novel and computationally efficient two-stage linearized decoupled formulation for security-constrained redispatch in hybrid AC/DC grids. The methodology explicitly addresses N-1 security criterion, incorporating constraints from both the AC and DC subsystems, as well as the DC/AC converters. Simulation results on a test power system demonstrate the effectiveness of the proposed approach in mitigating the impact of both transmission line and generator outages, validating its applicability for enhancing grid resilience. Full article