Hasil untuk "Distribution or transmission of electric power"

Soumyadeep Nag, Tanveer Hussain, S. M. Shafiul Alam et al.

Representing water head information in power system model files, can provide a more realistic model of the system and thereby inform operation and planning personnel in the decision-making process. This article describes a procedure for modifying the power system model files (steady-state and dynamic) to represent water head information. Additionally, the impact of representing the water head on power system reliability studies including contingency analysis, cascading failure analysis and dynamic frequency response analysis has been investigated, using the modified power system models. This paper considers the detailed Western Electricity Coordination Council model during summer and winter conditions as the test system for the impact analysis. Results show that under reduced water head: 1) the number of critical voltage and branch flow violations increases; 2) chances of cascading failure and island formation increases; and 3) frequency nadir decreases as compared to those of the base cases where the water head information is not represented.

Anton Lutsenko, Kevin G. Fripp, Lukáš Flajšman et al.

We report on nonlinear spin-wave dynamics in magnonic Fabry-Pérot resonators composed of yttrium iron garnet (YIG) films coupled to CoFeB nanostripes. Using super-Nyquist sampling magneto-optical Kerr effect microscopy and micromagnetic simulations, we observe a systematic downshift of the spin-wave transmission gaps as the excitation power increases. This nonlinear behavior occurs at low power levels, reduced by a strong spatial concentration of spin waves within the resonator. The resulting power-dependent transmission enables neuron-like activation behavior and frequency-selective nonlinear spin-wave absorption. Our results highlight magnonic Fabry-Pérot resonators as compact low-power nonlinear elements for neuromorphic magnonic computing architectures.

Sebastian Dambone Sessa

Saad Ullah Khan, Muhammad Sajid Khan, Hamza Farooq

Xinan Wang, Di Shi, Fengyu Wang

This paper presents a novel three-stage framework for real-time foreign object intrusion (FOI) detection and tracking in power transmission systems. The framework integrates: 1) a YOLOv7 segmentation model for fast and robust object localization, 2) a ConvNeXt-based feature extractor trained with triplet loss to generate discriminative embeddings, and 3) a feature-assisted IoU tracker that ensures resilient multi-object tracking under occlusion and motion. To enable scalable field deployment, the pipeline is optimized for deployment on low-cost edge hardware using mixed-precision inference. The system supports incremental updates by adding embeddings from previously unseen objects into a reference database without requiring model retraining. Extensive experiments on real-world surveillance and drone video datasets demonstrate the framework’s high accuracy and robustness across diverse FOI scenarios. In addition, hardware benchmarks on NVIDIA Jetson devices confirm the framework’s practicality and scalability for real-world edge applications.

Ozan Akdag

Ayman M. O. Mohamed, Ramadan El‐Shatshat

Abstract Under the envisioned smart grid paradigm, there is an increasing demand for a fast, accurate, and efficient power flow solution for distribution system operation and control. Various solution techniques have been proposed, each with its own unique formulation, solution methodology, advantages, and drawbacks. Motivated by challenges associated with the integration of renewable distributed energy resources and electric vehicles into distribution systems and further by the speed and convergence limitations of existing tools, this paper presents a novel graph‐based power flow solution for smart grid's real‐time operation and control, named Flow‐AugmentationPF algorithm. The proposed method formulates a power flow problem as a network‐flow problem and solves it by using a maximum‐flow algorithm, inspired by the push‐relabel max‐flow technique. The performance of the proposed algorithm is tested and validated using several benchmark networks of different sizes, topologies, and parameters and compared against the most commonly used solution techniques and commercial software packages, namely PSS/E and PSCAD. The proposed formulation is simple, accurate, fast, yet computationally efficient, as it is based on matrix‐vector multiplication, and is also scalable, considering the formulation works as a graph‐based method, which, inherently, allows for parallel computation for added computational speed.

Lixian Shi, Qiushi Cui, Yang Weng et al.

Incipient faults (IFs) are abnormal states before the permanent failure of power equipment. IFs are typically transient and generally do not trigger the operation of relay protection devices. This leads the difficulty in capturing IF data from waveform monitoring or recording devices. However, traditional detection methods cannot achieve satisfactory performance when faced with limited data. Besides, some signal analysis methods based on waveform conversion to images cannot obtain understandable image data and cannot analyze both current and voltage signals simultaneously. To resolve these problems, a few-shot meta-learning framework for incipient fault detection (FSMLF-IFD) is proposed in this paper. For better data processing, a waveform image conversion strategy is proposed to convert waveforms into understandable images from the time domain perspective. Then, an adaptive image fusion strategy is developed to concurrently analyze voltage and current images. Next, at the meta-training stage, an adaptability-enhancing weighting initialization strategy is constructed to address the data differences between the meta-training stage and IF detection stage. Finally, an IF detection model based on convolutional neural networks (CNNs) is obtained through the fine-tuning process. In the numerical results, the IF detection and classification accuracy of FSMLF-IFD reached 0.9720 and 0.9840 based on simulation and field IF data, which validates the effectiveness of the proposed method.

Xiangwu Yan, Weilin Wu, Chen Shao et al.

Abstract To solve the problem of negative‐sequence and reactive power in electrified railway, this paper proposes a comprehensive compensation method based on V/v transformer and electromagnetic single‐phase var compensator (ESVC) for co‐phase power supply. First, based on the principles of static var generator and single‐phase rotary phase shifting transformer, the topology and model of ESVC have been established, and the compensation mechanism has been analysed. Second, the topological structure and mathematical model of co‐phase power comprehensive compensation device (CPCD) are put forward based on the principles of V/v transformer and ESVC, and the operation mode of CPCD in multiple scenarios is designed. Then the strategy of CPCD with double closed loop control is analysed: corresponding compensation mode is selected according to negative‐sequence unbalance degree and expected power factor value. In this strategy, the compensated current output by ESVC is taken as the quantity of external loop control, and the rotation angle of ESVC is taken as the quantity of internal loop control to realize double‐closed loop control of CPCD. Finally, the comprehensive CPCD compensation model is built on the simulation platform and validated the accuracy of the mathematical model and the effectiveness of the control strategy.

Tai Li, Sunan Sun, Mengjie Li et al.

Abstract This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system.

Antonin Colot, Elisabetta Perotti, Mevludin Glavic et al.

This paper considers an incremental Volt/Var control scheme for distribution systems with high integration of inverter-interfaced distributed generation (such as photovoltaic systems). The incremental Volt/Var controller is implemented with the objective of minimizing reactive power usage while maintaining voltages within safe limits sufficiently often. To this end, the parameters of the incremental Volt/Var controller are obtained by solving a chance-constrained optimization problem, where constraints are designed to ensure that voltage violations do not occur more often than a pre-specified probability. This approach leads to cost savings in a controlled, predictable way, while still avoiding significant over- or under-voltage issues. The proposed chance-constrained problem is solved using a successive convex approximation method. Once the gains are broadcast to the inverters, no additional communication is required since the controller is implemented locally at the inverters. The proposed method is successfully tested on a low-voltage single-phase 42-nodes network and on the three-phase unbalanced IEEE 123-node test system.

David Gaspard, Arthur Goetschy

We develop a field-theoretic framework, called radiant field theory, to calculate the distribution of transmission eigenvalues for coherent wave propagation in disordered media. At its core is a self-consistent transport equation for a $2\times 2$ matrix radiance, reminiscent of the radiative transfer equation but capable of capturing coherent interference effects. This framework goes beyond the limitations of the Dorokhov-Mello-Pereyra-Kumar theory by accounting for both quasiballistic and diffusive regimes. It also handles open geometries inaccessible to standard wave-equation solvers such as infinite slabs. Analytical and numerical solutions are provided for these geometries, highlighting in particular the impact of the waveguide shape and the grazing modes on the transmission eigenvalue distribution in the quasiballistic regime. By removing the macroscopic assumptions of random matrix models, this microscopic theory enables the calculation of transmission statistics in regimes previously out of reach. It also provides a foundation for exploring more complex observables and physical effects relevant to wavefront shaping in realistic disordered systems.

Aryeh Kontorovich, Amichai Painsky

We present novel bounds for estimating discrete probability distributions under the $\ell_\infty$ norm. These are nearly optimal in various precise senses, including a kind of instance-optimality. Our data-dependent convergence guarantees for the maximum likelihood estimator significantly improve upon the currently known results. A variety of techniques are utilized and innovated upon, including Chernoff-type inequalities and empirical Bernstein bounds. We illustrate our results in synthetic and real-world experiments. Finally, we apply our proposed framework to a basic selective inference problem, where we estimate the most frequent probabilities in a sample.

A. Paramane, M. Awais, Thirumurugan Chandrasekaran et al.

The high-temperature superconducting (HTS) ac and dc cables are gradually finding their way toward commercialization due to their ability to transfer bulk electric power with negligible dielectric loss. In this context, this article critically reviews the different aspects of HTS cables used in various applications and related accessories. The operational, laboratory-scale, and prototype HTS cable projects are discussed in detail. Different dielectrics, electrical insulations, superconductors, cooling systems, cryogens, and upcoming projects of HTS cables are reviewed. The underlying challenges and future directions in ac and dc cables, electric aircraft and ships, associated costs, cryogenic insulation materials, cooling technologies, superconductors, and electrical insulations of HTS cables are discussed in detail. The space charge performance of presently used polypropylene laminated paper insulation and future insulation, i.e., Kapton material, is experimentally analyzed, to show the future challenges in the insulation design. The technology readiness level, market trends, and relevant test standards are explored. The research and market trends suggest that HTS cables can replace conventional transmission methods. However, their consistent growth and adaptation of HTS cables will depend on the design of cryocoolers, raw material cost of superconductors, and large-scale production.

Nadia Panossian, M. Muratori, B. Palmintier et al.

Farahnaz Ahmadi, Yazdan Batmani, Hassan Bevrani

Abstract In an AC microgrid, the active/reactive power is usually shared among its distributed generators (DGs) based on the frequency‐active power (F−P) droop and the voltage‐reactive power (V−Q) droop. By increasing the resistant/inductance ratio (R/X) of feeder lines; however, adverse effects of interactions between these two control loops are intensified. In this paper, an adaptive multi‐input multi‐output (MIMO) current control structure is proposed to tackle this problem in AC microgrids with arbitrary numbers of DGs in the primary control level. A deep analysis based on the relative gain array (RGA) matrix and the diagonal dominance concept is provided to systematically design MIMO controllers. The proposed technique is based on the Lyapunov's stability theory, and the asymptotic stability of the whole microgrid is guaranteed. For each DG, the suggested design procedure is started by defining a model reference in which the desired control objectives, including the settling time and the steady‐state error, are considered. Then, a feedback‐feedforward controller is established where its gains are adaptively tuned by some rules derived from a Lyapunov function. Moreover, a predictor is used to estimate the adverse effects of other DGs which are taken into account as external disturbances during the design process of the adaptive controller. By considering some realistic scenarios through time‐domain simulations in MATLAB/SIMULINK, it is shown that the proposed strategy can be successfully used to solve the power sharing problem in AC microgrids.

Yang Chen, Jianxue Wang, Siyuan Wang et al.

Abstract The integrated electricity and heat system (IEHS) is an emerging demand‐side flexible resource for power systems. IEHS operators participating in electricity markets considering their capabilities in reserve provision will face the reserve deliverability risk due to the energy‐limited storage nature of heat systems. To address this challenge and increase profitability, a distributionally robust joint chance‐constrained mechanism with enhanced quantifications is adopted for the heating system and reserve deployment uncertainties. Detailed pipeline storage representation for thermal networks and integrated demand response are incorporated into this strategic participation model. A two‐stage distributionally robust joint chance constrained program is then incorporated to effectively manage the reserve deliverability risk by addressing uncertainties from local distributed energy resources and real‐time reserve requests. The L‐shaped algorithm is then customized by incorporating bi‐linear Benders’ decomposition and modified scenario filtering method to efficiently tackle solution challenges for the sophisticated model. Numerical results show the advantages of our approach in virtual thermal storage utilization, risk management, computational performance enhancement and scalability.

Rachel Hunter-Rinderle, Matthew Y. Fong, Baihua Yang et al.

Electricity-supply interruptions can be costly and disruptive. Electricity-supply reliability and resilience can be enhanced by customers having on-site energy storage, which supplements electricity-system supply. This paper proposes a two-stage stochastic optimization model that can be used in a rolling-horizon fashion to schedule such use of energy storage. We demonstrate the model with a case study that combines electricity-supply-reliability data for a real-world electric utility, survey data regarding residential customers’ willingnesses to pay for backup energy during electricity-supply disruptions, and a highly resolved Markov chain model of building-occupant behavior and associated electricity use that is calibrated to census data. We find that the low probability of an electricity-supply disruption occurring during any given time-step limits the charging of the energy storage in anticipation of possible disruptions. We demonstrate two approaches to reduce this myopic use of energy storage. Our case study shows that penalty parameters can be used to control the conservatism of the model in using as opposed to retaining stored energy during an electricity-supply disruption. Overall, we show the viability of on-site energy storage to enhance electricity-supply reliability and resilience and the feasibility of our model and algorithm for real-time control of energy storage for such a real-world application.

Rida Fatima, Hassan Zahid Butt, Xingpeng Li

The aim of distribution networks is to meet their local area power demand with maximum reliability. As the electricity consumption tends to increase every year, limited line thermal capacity can lead to network congestion. Continuous development and upgradation of the distribution network is thus required to meet the energy demand, which poses a significant increase in cost. The objective of this research is to analyze distribution network topologies and introduce a topology reconfiguration scheme based on the cost and demand of electricity. Traditional electrical distribution networks are static and inefficient. To make the network active, an optimal dynamic network topology reconfiguration (DNTR) is proposed to control line switching and reconnect some loads to different substations such that the cost of electricity can be minimized. The proposed DNTR strategy was tested on a synthetic radial distribution network with three substations each connecting to an IEEE 13-bus system. Simulation results demonstrated significant cost saving in daily operations of this distribution system.