Search Results (9,404)

This study investigated the fault ride through capability of inverter-based resources in weak distribution networks and proposes a fault-oriented reactive power compensation strategy using only point of common coupling voltage measurements. The proposed strategy determines the reactive power command based on the minimum phase voltage, which represents the most severely depressed phase during unbalanced faults, without fault type detection or sequence component analysis. As a result, the same control framework can be applied to single-line-to-ground, double-line-to-ground, and three-phase faults. A detailed MATLAB/Simulink model of a Korean distribution feeder was developed using actual system parameters. The proposed strategy was compared with a no control case and a conservative fixed capacity reactive power injection scheme derived from commonly adopted power factor limits. Simulation results show that the no control case provides no voltage support, while the fixed capacity approach yields limited improvement in weak grids. In contrast, the proposed strategy maintains stable inverter operation and improves voltage recovery. At locations with an extremely low weighted short circuit ratio of 0.303, the proposed strategy prevents inverter tripping during temporary faults and satisfies low voltage ride through requirements, demonstrating its practical effectiveness. Full article

Modern power distribution networks are increasingly challenged with nonlinear operating conditions, the high penetration of distributed energy resources, and conflicting operational objectives such as loss minimization and voltage regulation. Existing load-flow optimization approaches often suffer from slow convergence, premature stagnation in non-convex search spaces, and limited robustness when handling conflicting multi-objective performance criteria under fixed network constraints. To address these challenges, this paper proposes a Fractional Multi-Objective Load Flow Optimizer (FMOLFO), which integrates a fractional-order numerical regularization mechanism with an adaptive Pareto-based Differential Evolution framework. The fractional-order formulation employed in FMOLFO operates over an auxiliary iteration domain and serves as a numerical regularization strategy to improve the sensitivity conditioning and convergence stability of the load-flow solution, rather than modeling the physical time dynamics or memory effects of the power system. The optimization framework simultaneously minimizes physically consistent active power loss and voltage deviation within existing network operating constraints. Extensive simulations on IEEE 33-bus and 69-bus benchmark distribution systems demonstrate that FMOLFO achieves an up to 27% reduction in active power loss, improved voltage profile uniformity, and faster convergence compared with classical Newton–Raphson and metaheuristic baselines evaluated under identical conditions. The proposed framework is intended as a numerically enhanced, optimization-driven load-flow analysis tool, rather than a control- or dispatch-oriented optimal power flow formulation. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Ocean current energy, owing to its predictability and stability, is regarded as an ideal power source for distributed marine observation networks and underwater intelligent equipment. However, conventional ocean current energy devices that rely on rigid turbines and electromagnetic generators generally suffer from high cut-in flow velocity, bulky size, high maintenance costs, and significant environmental disturbance, making them unsuitable for deep-sea, miniaturized, and long-duration power supply scenarios. These limitations highlight the urgent need for flexible and low-speed energy harvesters capable of autonomous, long-term operation. In recent years, nanogenerator technology has provided new opportunities for distributed and low-power ocean current energy harvesting. PENGs and TENGs can directly convert weak mechanical energy into electricity, enabling energy harvesting in small-scale and low-velocity flow fields. PENGs offer high durability and mechanical robustness, whereas TENGs exhibit superior output performance in low-speed and intermittent flows. This paper provides a comprehensive review of structural designs, material innovations, interface engineering, hybrid energy-conversion architectures, and power-management strategies for PENG- and TENG-based ocean current energy harvesters. Overall, future progress will rely on the integration of intelligent materials, multi-field coupling mechanisms, and system-level engineering strategies to achieve durable, scalable, and autonomous ocean current energy harvesting for distributed marine systems. Full article

Under the “Dual Carbon” strategy, high-penetration integration of distributed generators (DG) into distribution networks has triggered bidirectional power flow and reactive power-voltage violations. This phenomenon undermines the accuracy guarantee of conventional relaxation models (represented by second-order cone programming, SOCP), causing solutions to deviate from the AC power flow feasible region. Notably, ensuring solution feasibility becomes particularly crucial in engineering practice. To address this problem, this paper proposes a collaborative optimization framework integrating convex inner approximation (CIA) theory and a solution recovery algorithm. First, a system relaxation model is constructed using CIA, which strictly enforces ACPF constraints while preserving the computational efficiency of convex optimization. Second, aiming at the conservatism drawback introduced by the CIA method, an admissible region correction strategy based on Stochastic Gradient Descent is designed to narrow the dual gap of the solution. Furthermore, a multi-objective optimization framework is established, incorporating voltage security, operational economy, and renewable energy accommodation rate. Finally, simulations on the IEEE 33/69/118-bus systems demonstrate that the proposed method outperforms the traditional SOCP approach in the 24 h sequential optimization, reducing voltage deviation by 22.6%, power loss by 24.7%, and solution time by 45.4%. Compared with the CIA method, it improves the DG utilization rate by 30.5%. The proposed method exhibits superior generality compared to conventional approaches. Within the upper limit range of network penetration (approximately 60%), it addresses the issue of conservative power output of DG, thereby effectively promoting the utilization of renewable energy. Full article

Renewable energy sources like wind turbines and solar panels are integrated into modern power grids as Distributed Energy Resources (DERs). These DERs can operate independently or as part of microgrids. Interconnecting multiple microgrids creates Networked Microgrids (NMGs) that increase reliability, resilience, and independent power generation. However, the trustworthiness of individual DERs remains a critical challenge in NMGs, particularly when integrating previously deployed or geographically distributed units managed by entities with varying expertise. Assessing DER trustworthiness ensuring reliability and security is essential to prevent system-wide instability. Thisresearch addresses this challenge by proposing a lightweight trust metric generation system capable of processing structured and semi-structured DER data to produce key trust indicators. The system employs a Small Language Model (SLM) with approximately 16 million parameters for textual data understanding and metric extraction, followed by a regression head to output bounded trust scores. Designed for deployment in computationally constrained environments, the SLM requires only 64.6 MB of disk space and 200–250 MB of memory that is significantly lesser than larger models such as DeepSeek R1, Gemma-2, and Phi-3, which demand 3–12 GB. Experimental results demonstrate that the SLM achieves high correlation and low mean error across all trust metrics while outperforming larger models in efficiency. When integrated into a full neural network-based trust framework, the generated metrics enable accurate prediction of DER trustworthiness. These findings highlight the potential of lightweight SLMs for reliable and resource-efficient trust assessment in NMGs, supporting resilient and sustainable energy systems in smart cities. Full article

Supercapacitors have attracted significant interest as increased energy storage devices due to their high power density, rapid charge/discharge performance, and long cyclability. In this study, NiO, Co₃O₄, NCO, and NCO/rGO composite electrodes were prepared and evaluated for high-performance supercapacitor applications. The uniform distribution of elements and the effective incorporation of rGO into the composite were confirmed by structural and morphological characterizations. Among the evaluated materials, the NCO/rGO electrode exhibited high electrochemical performance, delivering a specific capacitance of 998 F g⁻¹ in a three-electrode configuration, attributed to the enhanced redox activity of NiCo₂O₄ coupled with the enhanced electrical conductivity of rGO. Additionally, an asymmetric supercapacitor device with activated carbon as the negative electrode and NCO/rGO as the positive electrode showed a power density of 750 W kg⁻¹, an energy density of 29.2 Wh kg⁻¹, and a specific capacitance of 93.7 F g⁻¹. After 5000 charge/discharge cycles, the device maintained 85% of its initial capacitance and a coulombic efficiency of 99%, demonstrating exceptional cyclability. These results highlight the strong potential of the NiCo₂O₄/rGO composite as an advanced electrode material for next-generation energy storage systems. Full article

The traditional ultrasonic bonding technique for IGBT T2 copper terminals often causes physical damage to ceramic substrates, severely compromising the reliability of power modules. Meanwhile, T2 copper laser welding faces inherent challenges including low laser absorption efficiency and unstable molten pool dynamics. To address these issues, this study targets the high-quality connection of IGBT T2 copper terminals and proposes a welding solution integrating a Fiber-Diode Hybrid Laser system with galvo-scanning technology. Comparative experiments between galvo-scanning and traditional oscillation methods CNC scanning were conducted under sinusoidal and circular trajectories to explore the regulation mechanism of welding quality. The results demonstrate that CNC scanning lacks precision in thermal input control, resulting in inconsistent welding quality. Galvo-scanning enables precise modulation of laser energy distribution and molten pool behavior, effectively reducing spatter and porosity defects. It also promotes the transition from columnar grains to equiaxed grains, significantly refining the weld microstructure. Under the sinusoidal trajectory with a welding speed of 20 mm/s, the Lap-shear strength of the galvo-scanned joint reaches 277 N/mm², outperforming all CNC-scanned joints. This research proposes a non-contact welding strategy targeted at eliminating the mechanical failure mechanism associated with conventional ultrasonic bonding of ceramic substrates. It establishes the superiority of galvo-scanning for precision welding of high-reflectivity materials and lays a foundation for its potential application in new energy vehicle power modules and microelectronic packaging. Full article

This paper presents a PSCAD-based analysis of short-circuit faults and protection characteristics in a real distribution-level microgrid that integrates a 1 MWh battery energy storage system (BESS) with a 500 kW power conversion system (PCS) and a 500 kW photovoltaic (PV) plant connected to a 22.9 kV feeder. While previous studies often rely on simplified inverter models, this paper addresses the critical gap by integrating actual manufacturer-defined control parameters and cable impedances. This allows for a precise analysis of sub-millisecond transient behaviors, which is essential for developing robust protection schemes in inverter-dominated microgrids. The PSCAD model is first verified under grid-connected steady-state operation by examining PV output, BESS power, and grid voltage at the point of common coupling. Based on the validated model, DC pole-to-pole faults at the PV and ESS DC links and a three-phase short-circuit fault at the low-voltage bus are simulated to characterize the fault current behavior of the grid, BESS and PV converters. The DC fault studies confirm that current peaks are dominated by DC-link capacitor discharge and are strongly limited by converter controls, while the AC three-phase fault is mainly supplied by the upstream grid. As an initial application of the model, an instantaneous current change rate (ICCR) algorithm is implemented as a dedicated DC-side protection function. For a pole-to-pole fault, the ICCR index exceeds the 100 A/ms threshold and issues a trip command within 0.342 ms, demonstrating the feasibility of sub-millisecond DC fault detection in converter-dominated systems. Beyond this example, the validated PSCAD model and associated data set provide a practical platform for future research on advanced DC/AC protection techniques and protection coordination schemes in real BESS–PV microgrids. Full article

The increasing penetration of renewable energy has led to a decrease in system inertia, challenging grid stability and frequency regulation. This paper presents a dynamic estimation framework for load-side virtual inertia, supported with high-power-density electrical double-layer supercapacitors (EDLCs). By leveraging the fast response and high power density of EDLCs, the proposed method enables the real-time emulation of demand-side inertial behavior, enhancing frequency support capabilities. A hybrid estimation algorithm has been developed that combines demand forecasting and adaptive filtering to track virtual inertia parameters under varying load conditions. Simulation results, based on a 150 kVA distributed system with 27% renewable penetration and 33% demand variability, demonstrate the effectiveness of the approach in improving transient stability and mitigating frequency deviations within ±0.1 Hz. The integration of ESS-based support offers a scalable and energy-efficient solution for future smart grids, ensuring operational reliability under real-world variability. Full article

The growing incorporation of distributed energy resources (DER) in power distribution grids, although pivotal to the energy transition, increases operational variability and amplifies the exposure to disturbances that can compromise resilience and the continuity of service during contingencies. Addressing these challenges requires both a shift toward flexible distribution architectures and the adoption of advanced power electronics interfacing systems. In this setting, this paper proposes a resilience-oriented strategy for medium-voltage (MV) distribution systems and clustered hybrid AC/DC microgrids interfaced through solid-state transformers (SSTs). When a fault occurs along an MV feeder segment, the affected microgrids naturally transition to islanded operation. However, once their local generation and storage become insufficient to sustain autonomous operation, the proposed framework reconfigures the power routing within the cluster by activating an emergency low-voltage DC (LVDC) power path that bypasses the faulted MV section. This mechanism enables controlled power sharing between microgrids during prolonged MV outages, ensuring the supply of priority loads without oversizing SSTs or reinforcing existing infrastructure. Experimental validation on a reduced-scale SST prototype demonstrates stable grid-forming and grid-following operation. The reliability of the proposed scheme is supported by both steady-state and transient experimental results, confirming accurate voltage regulation, balanced sinusoidal waveforms, and low current tracking errors. All tests were conducted at a switching frequency of 50 kHz, highlighting the robustness of the proposed architecture under dynamic operation. Full article

Efficient cooling systems are crucial for achieving high efficiency and power density in electric vehicle motors. To enhance motor cooling performance, a novel oil spray cooling system was developed, referred to as the centripetal-inclined oil spray (CIOS) cooling system. The CIOS cooling system features axial oil channels evenly distributed on the surface of the stator core, with each channel connected at both ends to stepped oil channels. This configuration allows for direct oil spraying towards the center at specific inclined angles without the need for additional components such as nozzles, oil spray rings, and oil spray tubes, which reduces costs, minimizes the risk of oil leakage, and enhances motor reliability. Electromagnetic and computational fluid dynamic simulations were conducted on the motor with the CIOS cooling system. The results indicated that the CIOS cooling system adversely impacted core losses and torque, while these effects were minimized after optimization, with losses increasing by up to 0.29% and torque decreasing by up to 0.45%. The CIOS cooling system achieved stable oil spraying, forming oil films on the end-winding with a maximum formation rate of 49.4% and an average thickness of 1.56 mm. Compared to the motor with oil spray rings, the motor with the CIOS cooling system exhibited lower temperatures across all components and more uniform cooling. Finally, the cooling performance of the CIOS cooling system was verified through experiments, and the results showed that the measured temperature closely matched the simulated results, with a maximum error of 5.9%. The findings in this study are expected to provide new insights for optimizing oil cooling systems in electric vehicle motors. Full article

The high penetration of renewable energy brings significant power fluctuations and operational uncertainties to distribution networks. Traditional power allocation methods for hybrid energy storage systems (HESSs) exhibit strong parameter dependency, limited frequency-domain recognition accuracy, and poor dynamic coordination capability. To overcome these limitations, this study proposes an adaptive power allocation strategy for HESSs under renewable energy integration scenarios. The proposed method employs the Grey Wolf Optimizer (GWO) to jointly optimize the mode number and penalty factor of the Variational Mode Decomposition (VMD), thereby enhancing the accuracy and stability of power signal decomposition. In conjunction with the Hilbert transform, the instantaneous frequency of each mode is extracted to achieve a natural allocation of low-frequency components to the battery and high-frequency components to the supercapacitor. Furthermore, a multi-objective power flow optimization model is formulated, using the power commands of the two storage units as optimization variables and aiming to minimize voltage deviation and network loss cost. The model is solved through the Particle Swarm Optimization (PSO) algorithm to realize coordinated optimization between storage control and system operation. Case studies on the IEEE 33-bus distribution system under both steady-state and dynamic conditions verify that the proposed strategy significantly improves power decomposition accuracy, enhances coordination between storage units, reduces voltage deviation and network loss cost, and provides excellent adaptability and robustness. Full article

Background/Objectives: Pyrus ussuriensis Maxim. has been cultivated in many regions worldwide. This plant is also regarded as a profitable fruit crop for the development of many food and functional products. There is limited research on the application of the LC-MS associated reaction method for screening active compounds. In this study, we developed an analytical technique employing an ultra-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UHPLC-ESI-MS/MS) system. Methods: The metabolite annotation procedure was used to interpret and validate data analysis via spectral matching against public databases. Results: As a result, metabolites from P. ussuriensis water and EtOH extracts were identified, and their quantities were further evaluated. The established method was employed to determine antioxidant capacity using a pre-incubation UHPLC-2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, thereby identifying antioxidant ingredients. The antioxidative interference of active constituents was predicted by calculating the decrease in the peak areas of the chemical composition detected in chromatograms between treated and non-treated samples. Furthermore, drug-likeness was also assessed via pharmacokinetics (absorption, distribution, metabolism, and excretion: ADME) evaluation. Conclusions: The online UHPLC-MS-DPPH method would be a powerful tool for the rapid characterization of antioxidant ingredients in plant extracts. The current study highlights the value of P. ussuriensis for improved health benefits. Full article

Grid-forming converters have gained significant attention for their ability to form grid voltage and provide essential grid-supportive services. However, managing harmonics generated by nonlinear loads remains a critical challenge in weak grids. A single grid-forming converter active power filter offers limited compensation capacity, and under heavy nonlinear loading its performance is restricted by converter ratings, leading to reduced stability margins, higher harmonic distortion, and weakened voltage/frequency regulation. To overcome these limitations, this paper presents a novel distributed control approach for multiple-parallel grid-forming converters active power filters that integrates voltage and frequency regulation with scalable harmonic attenuation. The proposed method extracts harmonic components at the point of common coupling and generates harmonic voltage commands to each unit so the parallel units collectively create a near short-circuit impedance for harmonics, preventing harmonic currents from propagating into the grid. Beyond improved harmonic performance, the multi-unit system enhances effective inertia, damping, and short-circuit capacity while avoiding complex parameter tuning, enabling a simple and scalable deployment. Simulation results demonstrate effective harmonic attenuation at the point of common coupling and accurate active/reactive power sharing. Full article