The Texas electric power crisis that occurred in February 2021 has drawn great attention internationally due to its severity and for not having been *** this rapid communication,we classify the 2021 Texas electric pow...
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The Texas electric power crisis that occurred in February 2021 has drawn great attention internationally due to its severity and for not having been *** this rapid communication,we classify the 2021 Texas electric power crisis as an energy insufficiency-caused power crisis,which alerts of a new blackout *** from capacity insufficiencycaused power crises in the past,the Texas electric power crisis of 2021 directly resulted from the long-duration extreme cold weather as well as fundamentally from the insufficiency of sustainable supply capability of *** begin this paper with a brief retrospect of the event and its *** of energy/capacity insufficiency-caused power crises are given,as well as an overview of the supply and demand during the event,based on realistic operation *** simulations are then conducted to reveal the underlying reasons for the power crisis and reveal how to better prepare for the ***,several insights and suggestions on handling the new mode of blackout in the future are proposed and discussed.
Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induc...
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Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset. Current state-of-the-art approaches are based on supervised learning with a conditioned policy. However, th...
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Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset. Current state-of-the-art approaches are based on supervised learning with a conditioned policy. However, these approaches fall short in real-world applications that involve complex tasks with rich temporal and logical structures. In this paper, we propose temporal logic Specification-conditioned Decision Transformer (SDT), a novel framework that harnesses the expressive power of signal temporal logic (STL) to specify complex temporal rules that an agent should follow and the sequential modeling capability of Decision Transformer (DT). Empirical evaluations on the DSRL benchmarks demonstrate the better capacity of SDT in learning safe and high-reward policies compared with existing approaches. In addition, SDT shows good alignment with respect to different desired degrees of satisfaction of the STL specification that it is conditioned on. Copyright 2024 by the author(s)
Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which...
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Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.
In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a n...
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In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating ***, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.
Tuning Fork Gyroscopes (TFGs) are vital for high-precision Strap-Down Inertial Navigation systems (SINS). However, their effectiveness can be compromised by bias drift due to factors like self-heating and environmenta...
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With the increasing complexity and scale of digital VLSI designs, ensuring reliability in IC design necessitates effective fault detection processes during the pre-silicon stage. Many fault detection algorithms lead t...
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Detecting unknown samples is a crucial task for deep learning applications, especially when considering open-set problems such as autonomous driving or disease classification. To improve DL models’ robustness in iden...
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作者：
Mahmoud, AbdulrahmanBabiker, AhmedMohamed, MazenAhmed, IjazKhalid, Muhammad
Control and Instrumentation Engineering Department Saudi Arabia
Department of Electrical Engineering Islamabad Pakistan
Electrical Engineering Department Dhahran31261 Saudi Arabia KFUPM
Interdisciplinary Research Center for Sustainable Energy Systems Saudi Arabia
DC microgrids (MGs) have recently garnered significant interest due to their efficient power conversion and simpler control systems compared to AC MGs. However, managing DC MGs presents specific challenges, especially...
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Encouraging citizens to invest in small-scale renewable resources is crucial for transitioning towards a sustainable and clean energy *** energy communities(LECs)are expected to play a vital role in this ***,energy sc...
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Encouraging citizens to invest in small-scale renewable resources is crucial for transitioning towards a sustainable and clean energy *** energy communities(LECs)are expected to play a vital role in this ***,energy scheduling in LECs presents various challenges,including the preservation of customer privacy,adherence to distribution network constraints,and the management of computational *** paper introduces a novel approach for energy scheduling in renewable-based LECs using a decentralized optimization *** proposed approach uses the Limitedmemory Broyden–Fletcher–Goldfarb–Shanno(L-BFGS)method,significantly reducing the computational effort required for solving the mixed integer programming(MIP)*** incorporates network constraints,evaluates energy losses,and enables community participants to provide ancillary services like a regulation reserve to the grid *** assess its robustness and efficiency,the proposed approach is tested on an 84-bus radial distribution *** indicate that the proposed distributed approach not only matches the accuracy of the corresponding centralized model but also exhibits scalability and preserves participant privacy.
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