Search Results (233)

The increasing penetration of Doubly Fed Induction Generator (DFIG)-based wind energy systems raises major concerns regarding voltage stability and Fault Ride-Through (FRT) capability under grid disturbances and wind speed variations. This paper proposes a coordinated control framework for a grid-connected DFIG system, where a Static Synchronous Compensator (STATCOM) based on discrete-time deadbeat current control is integrated with a Supercapacitor Energy Storage System (SCES) connected to the DC link through a bidirectional DC-DC converter governed by cascaded Active Disturbance Rejection Control (ADRC). The deadbeat-controlled STATCOM provides fast reactive current injection for voltage support during sag and swell events, while the cascaded ADRC enhances DC-link voltage regulation and suppresses rotor-speed oscillations. Comprehensive MATLAB/Simulink simulations are carried out under variable wind speed and severe grid disturbances up to 80% voltage sag and 50% voltage swell. For voltage regulation, the proposed method is compared with SVC and PI-based STATCOM. In addition, SCES control performance is evaluated by comparing PI, single ADRC, and cascaded ADRC in terms of DC-link voltage overshoot, undershoot, and ripple. The results show clear improvements in voltage response and transient performance. Under a 20% voltage sag, the proposed deadbeat-controlled STATCOM significantly improves the dynamic response, where the undershoot is reduced from 0.125 p.u. (with SVC) to 0.04 p.u., and the settling time is shortened from 0.04 s to 0.025 s. Under a severe 80% sag, the overshoot is limited to 0.02 p.u., compared with 0.13 p.u. for the SVC and 0.15 p.u. for the PI-based STATCOM. Similarly, under a 50% voltage swell, the overshoot is reduced to 0.20 p.u., compared with 0.46 p.u. for the SVC and 0.27 p.u. for the PI-based STATCOM. Regarding the DC-link performance under 80% sag, the proposed cascaded ADRC-based SCES limits the overshoot and undershoot to 6 V and 2 V, respectively, compared with 39 V and 32 V for the PI-based SCES. These results confirm the superior damping, disturbance rejection, and FRT enhancement achieved by the proposed strategy. Full article

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary αβ frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov–Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 μs per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity. Full article

Droplet manipulation constitutes a fundamental operation in numerous bio-microfluidic applications, including but not limited to medical diagnostics and targeted drug delivery. Among the various technologies developed for this purpose, magnetic digital microfluidics (MDMF) has emerged as a compelling approach due to its inherent advantages of contamination-free actuation, low cost, and configurational flexibility. Nevertheless, conventional MDMF remains constrained by its reliance on bulky instrumentation and substantial power consumption for generating controllable magnetic fields, which limit its in-field applications. To address these limitations, this work presents a programmable and portable electromagnetic microfluidic droplet manipulation platform that synergistically integrates static and dynamic magnetic fields to enable non-contact, high-precision droplet control under ultra-low power conditions. The proposed system comprises an electromagnetic actuation module, a permanent magnet, and a glass substrate coated with Teflon film. The entire system is secured by a PMMA support structure, within which a glass substrate is mounted and spatially separated from the permanent magnet. The PMMA support is fabricated using a milling process, offering a simple manufacturing procedure and high structural reusability and reproducibility. The control logic is implemented on a field-programmable gate array (FPGA) development board, facilitating fully autonomous operation powered by a standard battery. The platform operates at a low voltage of 3.5 V and a driving current of 180 mA, corresponding to a total power consumption of merely 0.63 W, while achieving robust manipulation of droplets in the volume range of 0.5 to 5 μL. A maximum average droplet velocity of up to 0.6 cm/s was attained under optimal conditions. The proposed platform offers a scalable and energy-efficient solution for portable droplet-based assays and holds significant promise for integration into point-of-care diagnostic tools and field-ready biochemical analysis systems. The platform demonstrates excellent operational stability and reproducibility, as validated by repeated actuation experiments with a positioning deviation of approximately 0.1 mm under optimized conditions. The fabrication process also exhibits high reliability with consistent performance across multiple experimental runs. Full article

Maintaining voltage stability and minimizing power losses for remote marine loads powered by long submarine cables is the challenging context of this paper. Flexible Alternating Current Transmission Systems (FACTS) are well-studied for terrestrial grids. However, their comparative performance and efficiency in the context of high-capacity submarine links remain a gap in the literature. This paper presents a rigorous analysis of the performance of a Unified Power Flow Controller (UPFC), Static Synchronous Series Compensator (SSSC), and Thyristor Controlled Series Capacitor (TCSC). A mathematical framework is developed to introduce the “solution circle” concept, which demonstrates that the series impedance values required to maintain a specific load voltage define a circle in the complex plane. A theoretical analysis is performed, revealing that the UPFC, with its two degrees of freedom, is significantly more efficient because it can select the minimum impedance magnitude on this circle. In contrast, SSSC and TCSC are limited to the reactive axis, which, under certain operating conditions, may not cross the solution circle; therefore, they may not meet the power quality objective. The results of a practical case study show that UPFC requires approximately half the rated power (22.4 MVA) compared to its counterparts (39.4 MVA) to achieve the same control objectives. Full article

High penetration of renewable energy sources (RESs) introduces significant power fluctuations, threatening voltage and frequency stability in modern power systems. This paper presents an integrated framework for static voltage stability assessment and stability-constrained optimization of under-frequency load shedding (UFLS) in renewable-dominated grids. A low-conservativeness analytical criterion is first derived for static voltage stability margin assessment. Then, a hybrid Deep Belief Network–Long Short-Term Memory (DBN–LSTM) model is developed for accurate renewable power forecasting, capturing temporal variability and uncertainty. Finally, UFLS-based stability-constrained dispatch is formulated to prevent voltage collapse, enhance the system stability, and minimize RES curtailment. Simulations on a modified IEEE benchmark system demonstrate that the proposed approach improves voltage and frequency stability while maintaining high renewable energy utilization. Full article

Flexible interconnection systems (FISs) improve distribution flexibility, yet they remain vulnerable to pronounced nonlinear instability and potentially severe DC-link voltage collapse during large disturbances such as constant power load (CPL) surges. Conventional linear control methods are often unable to prevent deep transient voltage dips under these conditions. To address this issue, this paper proposes a novel large-signal stability criterion based on mixed potential function (MPF) theory. Unlike conventional Lyapunov-based approaches, the proposed formulation explicitly incorporates the dynamics of the DC capacitor, thereby enabling the derivation of a closed-form stability boundary. On this basis, the proportional gains of the outer voltage loop are first optimized to guarantee an adequate static stability margin. Subsequently, a power differentiation feedforward control strategy is developed. Rather than passively counteracting transients, the proposed method dynamically adjusts the DC voltage reference according to the rate of change in power, thereby actively reshaping the transient trajectory. In this way, the simple PI control framework is preserved while avoiding the heavy computational burden associated with advanced methods such as model predictive control. Simulation results show that the proposed strategy increases the permissible CPL step power by 8.7%, from 92 kW to 100 kW. Moreover, under severe load surges and weak grid conditions, the method prevents voltage collapse and maintains the transient trajectory above the practical 600 V safe-operation threshold. This computationally efficient strategy significantly improves the robustness and continuity of operation of practical FISs. Full article

As the oil price has skyrocketed, the attraction towards electric vehicles has gone up. This scenario has also increased the demand for charging infrastructure. This paper proposes a novel charging infrastructure for electric vehicles which is energized by a solar photovoltaic unit, integrated with a distribution static compensator. The output of the photovoltaic array is regulated by a DC–DC converter, which uses maximum power point tracking to support optimal solar energy conversion. The compensator is integrated into the grid through a zigzag-star transformer, which helps with neutral current compensation, promoting balanced and distortion-free operation. The control algorithm is designed to ensure superior power quality during grid synchronization and sustainable energy management. This novel architecture ensures bidirectional power flow, enabling the charge–discharge dynamics of the electric vehicles, which can be termed Grid-to-Vehicle and Vehicle-to-Grid modes. Better grid flexibility and resilience are ensured by this dynamic power exchange. The control strategy based on the Linear Kalman Filter provides reactive power balance and maintains steady voltage at the point of common coupling, and it ensures enhanced power quality during power flow, resulting in efficient and reliable grid operations. The effectiveness of the control algorithm is tested and validated under Grid-to-Vehicle, Vehicle-to-Grid, nonlinear, unbalanced, and isolated solar conditions. Analytical tuning of the gains in the controller, by using the conventional methods, is not efficient under dynamic conditions and nonlinear loads. An optimization technique is used to estimate the proportional–integral control gains, which avoids the difficulty of tuning the controllers. Simulation of the system is carried out using MATLAB 2022b/SIMULINK. Simulation results under diverse operating scenarios confirm the system’s capability to sustain superior power quality, maintain grid stability, and support a robust and reliable charging infrastructure. By enabling regulated bidirectional energy exchange and autonomous operation during grid disturbances, the charger operates as a resilient grid-support asset rather than as a passive electrical load. Full article

To address the rapidly growing demand for charging stations (CSs) and the associated challenges posed by the expansion of electric vehicles (EVs), this study proposes a collaborative planning method integrates user demand considerations with operational constraints at the grid level. Based on graph theoretical principles, static topology models of the road network and distribution grid were constructed. A dynamic origin–destination (OD) prediction framework was then formulated by jointly considering traffic flow variations, battery energy consumption, user charging behavior, and ambient temperature, in which an enhanced gravity model is coupled with the Floyd algorithm. Charging load characteristics were quantified through Monte Carlo simulation, and K-means++ clustering was further applied to identify spatial charging demand hotspots. On this basis, a multi-objective optimization model was established to simultaneously balance the annualized cost of charging stations, user costs, and voltage deviation in the distribution network. To solve the resulting high dimensional problem, a collaborative optimization mechanism was designed by integrating a weighted Voronoi diagram with a multi-objective particle swarm optimization (MOPSO) algorithm, enabling dynamic service area partitioning and global capacity optimization. Case analysis demonstrates that the proposed method reduces user time costs by 15.8%, optimizes queue delay by 42.2%, and improves voltage stability, maintaining fluctuations within 5%. It also balances the interests of charging station operators, users, and distribution networks, with only a slight increase in construction costs. These results offer valuable theoretical and practical insights for charging infrastructure planning. Full article

With the increasing penetration of inverter-based photovoltaic (PV) generation, utility-scale grid-connected PV plants are frequently exposed to voltage regulation and voltage stability challenges driven by intermittent irradiance and limited reactive power flexibility under operating constraints. Conventional static Volt/VAR control schemes are typically designed for quasi-steady conditions and therefore struggle to respond to fast variations in PV output and network states. This paper presents a digital twin (DT)-enabled framework for dynamic Volt/VAR optimization in large PV plants. A four-layer DT architecture is developed to achieve real-time cyber-physical synchronization through multi-source data acquisition, secure transmission, fusion, and quality control. To balance model fidelity and computational efficiency, a hybrid physics–data-driven model is constructed, and a local voltage stability L-index is incorporated as an explicit security constraint. A multi-objective optimization problem is formulated to minimize node voltage deviations and reactive power losses while maximizing the static voltage stability margin. The problem is solved using an adaptive parameter particle swarm optimization (AP-PSO) algorithm with dynamic inertia and learning coefficients. Case studies on modified IEEE 33-bus and 53-bus systems demonstrate that the proposed method reduces the voltage profile index by up to 68.9%, improves the static voltage stability margin by 76.5%, and shortens optimization time by up to 30.3% compared with conventional control and representative meta-heuristic or learning-based baselines. The framework further shows good scalability and robustness under practical uncertainties, including irradiance forecast errors and measurement noise. Overall, the proposed approach provides a feasible pathway to enhance operational security and efficiency of grid-connected PV plants under high-penetration scenarios. Full article

Stable circumstances and an improved voltage profile need power compensators integrated with energy storage elements in AC power systems. The control of these compensators is of paramount importance for obtaining high accuracy, reliability, and better system dynamics, which involves careful controller design considerations and small-signal analysis. This paper focuses on the use of a static synchronous compensator (STATCOM) and supercapacitor energy storage system (SCESS) for achieving voltage stability, grid support, and better system dynamics. After the primary load is shifted to the grid, real power assistance is promptly injected into the AC grid to enhance the DC-link voltage, as well as the grid voltage, and reduce supply current from the grid using a vector control technique. The SCESS is handled with the help of a bidirectional DC–DC converter, which facilitates charging and discharging during boost and buck operations, respectively. Using small-signal modeling, the stable system is designed to obtain a reliable and stable output, which is confirmed by the systematic simulations and experiments. Full article

Distinct from the traditional Modular Multilevel Converter (MMC) which focuses on fundamental frequency operation, the Static Var and Filter (SVF) within the Multi-Source Line-Commutated Converter (SLCC) system is tasked with the core function of high-frequency harmonic filtering. This paper reveals a unique engineering reliability issue stemming from this functional difference: to satisfy the Nyquist sampling theorem for precise tracking and elimination of high-frequency harmonics, the update frequency of the capacitor voltage balancing algorithm in the SLCC-SVF system is forced to increase significantly. Mathematical modeling and quantitative analysis demonstrate that this strong coupling between harmonic tracking demands and the voltage sorting strategy directly drives an abnormal surge in the average switching frequency (reaching over five times that of the fundamental condition), severely threatening device safety. To address this, an optimized adaptive hybrid modulation strategy is proposed. The system operates under Nearest Level Modulation (NLM) in normal conditions and automatically transitions to Carrier Phase-Shifted PWM (CPS-PWM)—leveraging its closed-loop balancing capability—when switching frequency or junction temperature exceeds safety thresholds. Furthermore, a non-integer frequency ratio optimization theory for low-modulation indices is constructed specifically for SVF conditions to prevent low-frequency oscillations. PLECS simulation results validate the theoretical analysis, showing that the proposed strategy effectively reduces the average switching frequency by approximately 20% under complex harmonic conditions, significantly enhancing thermal stability and operational reliability while guaranteeing filtering performance. Full article

This study investigates the physical and cyber-physical resilience of smart grids with a high share of renewable energy sources (RESs) dominated by permanent magnet synchronous generators (PMSGs). The originality of this work lies in the development and unified evaluation of five integrated control strategies, the PLL with grid following, VSG with grid shaping, VSG+BESS, VSG+STATCOM, and VSG+BESS+STATCOM, implemented within a coherent simulation framework based on Python. Unlike previous works that analyze these methods in isolation, this study provides a comprehensive quantitative comparison of their dynamic characteristics, including frequency root mean square deviation, maximum deviation, and composite resilience index (RI). To extend the analysis beyond static conditions, a multi-generator (multi-PMSG) scenario with heterogeneous inertia constants and variable load profiles is introduced. This dynamic model allows the evaluation of natural inertia diversity and the effects of inter-generator coupling compared to the synthetic inertia emulation provided by VSG-based control. The combined VSG+BESS+STATCOM configuration achieves the highest synthetic resilience, improving frequency and voltage stability by up to 15%, while the multi-PMSG system demonstrates comparable or even higher RI values due to its inherent mechanical inertia and decentralized response behavior. In addition, a cyber-physical scenario is included to evaluate the effect of communication delays and false data injection (FDI) on VSG frequency control. The results show that a communication delay of 50 ms reduces RI by approximately 0.2%, confirming that even minor cyber disturbances can affect synchronization and transient recovery. However, hybrid control architectures with local energy buffering (BESS) show superior resilience under such conditions. The main technical contribution of this work is the establishment of an integrated analytical and simulation framework that enables the joint assessment of synthetic, natural, and cyber-physical resilience in converter-dominated smart grids. This framework provides a unified basis for the analysis of dynamic stability, hybrid control interaction, and the impact of cyber uncertainty, thereby supporting the design of low-inertia, resilient, and secure next-generation power systems. 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

Oxide transistors have attracted significant interest in the field of integrated circuits (ICs). Among various oxide semiconductors, InAlZnO (IAZO) stands out as a promising candidate due to its potential for high mobility and excellent stability. In this work, we fabricate high-performance IAZO transistors with a field-effect mobility of 56.60 cm²/V·s, a subthreshold swing of 82.59 mV/decade, an on-to-off current ratio over 10⁷, and a small threshold voltage shift of 0.09 V and −0.03 V under positive and negative bias stress, respectively. Based on these transistors, Pseudo-Enhancement-Load (PEL) inverters were constructed. An adjustable bias voltage (V_BIAS) was also introduced as an additional control parameter, which allows for flexible control of the trade-off between circuit performance and power consumption. The resulting inverters achieve a balance between static and dynamic performance, exhibiting a voltage gain of 1.83 V/V and a relatively low power consumption of 2.58 × 10⁻⁶ W (V_BIAS = 1.0 V). Our work demonstrates the potential of IAZO transistor-based PEL inverters for high-performance, low-power oxide IC applications. Full article

The integration of high-penetration renewable energy sources (RESs) and electric vehicles (EVs) increases the risk of voltage fluctuations in distribution networks. Traditional static partitioning strategies struggle to handle the intermittency of wind turbine (WT) and photovoltaic (PV) generation, as well as the spatiotemporal randomness of EV loads. Furthermore, existing scheduling methods typically optimize EV active power or reactive compensation independently, missing opportunities for synergistic regulation. The main novelty of this paper lies in proposing a spatiotemporally coupled voltage-stability optimization framework. This framework, based on an hourly updated electrical distance matrix that accounts for RES uncertainty and EV spatiotemporal transfer characteristics, enables hourly dynamic network partitioning. Simultaneously, coordinated active–reactive optimization control of EVs is achieved by regulating the power factor angle of three-phase six-pulse bidirectional chargers. The framework is embedded within a hierarchical model predictive control (MPC) architecture, where the upper layer performs hourly dynamic partition updates and the lower layer executes a five-minute rolling dispatch for EVs. Simulations conducted on a modified IEEE 33-bus system demonstrate that, compared to uncoordinated charging, the proposed method reduces total daily network losses by 4991.3 kW, corresponding to a decrease of 3.9%. Furthermore, it markedly shrinks the low-voltage area and generally raises node voltages throughout the day. The method effectively enhances voltage uniformity, reduces network losses, and improves renewable energy accommodation capability. Full article