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Energy Management and Operational control Methods for Grid Battery Energy Storage systems

CSEE Journal of Power and Energy systems 2021年第5期7卷 1026-1040页

作者： Xiangjun Li Shangxing Wang State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems Energy Storage and Electrical Engineering DepartmentChina Electric Power Research Institute.Beijing 100192China.

Energy storage is one of the key means for improving the flexibility,economy and security of power *** is also important in promoting new energy consumption and the energy ***,energy storage is expected to support distributed power and the micro-grid,promote open sharing and flexible trading of energy production and consumption,and realize multi-functional *** recent years,with the rapid development of the battery energy storage industry,its technology has shown the characteristics and trends for large-scale integration and distributed applications with multi-objective *** a grid-level application,energy management systems(EMS)of a battery energy storage system(BESS)were deployed in real time at utility control centers as an important component of power grid *** on the analysis of the development status of a BESS,this paper introduced application scenarios,such as reduction of power output fluctuations,agreement to the output plan at the renewable energy generation side,power grid frequency adjustment,power flow optimization at the power transmission side,and a distributed and niohile energy storage system at the power distribution *** studies and application status of a BESS in recent years were *** energy management,operation control methods,and application scenes of large-scale BESSs were also examined in the study.

关键词： Battery energy storage system(BESS) energy management systems(EMS) multi-objective collaborative control new energy integration power grid ancillary service.

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

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Mitigating Undesirable Oscillatory Behavior in Barge-type Floating Offshore Wind Turbines through Oscillating Water Columns

Mitigating Undesirable Oscillatory Behavior in Barge-type Fl...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Payam Aboutalebi Dong Trong Nguyen Aitor J. Garrido Izaskun Garrido Department of Automatic Control and Systems Engineering Engineering School of Bilbao - EIB/BIE Automatic Control Group - ACG University of the Basque Country - UPV/EHU Bilbao Spain Department of Marine Technology Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798350318098

ISBN: (纸本)9798350318104

This paper addresses the challenge of reducing undesired oscillations in barge-type floating wind turbines (FWTs) caused by wind and wave forces, which result in fluctuations in generator power and system oscillations. These oscillations can negatively impact the system’s lifespan, power generation, maintenance costs, and component stress. To mitigate these issues, oscillating water columns (OWCs) are integrated within the FWT substructure. A control strategy is proposed for the OWCs based on response amplitude operators, aiming to minimize oscillations in a 5 MW wind turbine structure. A considered case study is conducted for above-rated wind speeds to evaluate the effectiveness of the controllers and to compare the performance of an uncontrolled FWT against a controlled FWT integrated with OWCs. The findings demonstrate the efficacy of the control strategy in pitch oscillations reducing for 29.9% and generated power fluctuations reducing for 23.03% and highlight the advantages of integrating OWCs in floating offshore wind turbines.

关键词： Vibrations Fluctuations Wind speed Switches Wind power generation Valves control systems

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Optimizing Foundation Model Inference on a Many-Tiny-Core Open-Source RISC-V Platform

IEEE Transactions on Circuits and Systems for Artificial Int...

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IEEE Transactions on Circuits and systems for Artificial Intelligence 2024年第1期1卷 37-52页

作者： Viviane Potocnik Luca Colagrande Tim Fischer Luca Bertaccini Daniele Jahier Pagliari Alessio Burrello Luca Benini Integrated Systems Laboratory (IIS) of ETH Zürich ETZ Züurich Switzerland Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

Transformer-based foundation models have become crucial for various domains, most notably natural language processing (NLP) or computer vision (CV). These models are predominantly deployed on high-performance GPUs or hardwired accelerators with highly customized, proprietary instruction sets. Until now, limited attention has been given to RISC-V-based general-purpose platforms. In our work, we present the first inference results of transformer models on an open-source many-tiny-core RISC-V platform implementing distributed Softmax primitives and leveraging ISA extensions for SIMD floating-point operand streaming and instruction repetition, as well as specialized DMA engines to minimize costly main memory accesses and to tolerate their latency. We focus on two foundational transformer topologies, encoder-only and decoder-only models. For encoder-only models, we demonstrate a speedup of up to 12.8 $\times$ between the most optimized implementation and the baseline version. We reach over 79% FPU utilization and 294 GFLOPS/W, outperforming State-of-the-Art (SoA) accelerators by more than 2 $\times$ utilizing the HW platform while achieving comparable throughput per computational unit. For decoder-only topologies, we achieve 16.1 $\times$ speedup in the Non-Autoregressive (NAR) mode and up to 35.6 $\times$ speedup in the Autoregressive (AR) mode compared to the baseline implementation. Compared to the best SoA dedicated accelerator, we achieve 2.04 $\times$ higher FPU utilization.

关键词： Frequency modulation Transformers Computer architecture Computational modeling Kernel Artificial intelligence Hardware

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Stochastic subgradient projection methods for composite optimization with functional constraints

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 12146-12180页

作者： Ion Necoara Nitesh Kumar Singh Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest Romania and Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest Romania Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest Romania

In this paper we consider optimization problems with stochastic composite objective function subject to (possibly) infinite intersection of constraints. The objective function is expressed in terms of expectation operator over a sum of two terms satisfying a stochastic bounded gradient condition, with or without strong convexity type properties. In contrast to the classical approach, where the constraints are usually represented as intersection of simple sets, in this paper we consider that each constraint set is given as the level set of a convex but not necessarily differentiable function. Based on the exibility offered by our general optimization model we consider a stochastic subgradient method with random feasibility updates. At each iteration, our algorithm takes a stochastic proximal (sub)gradient step aimed at minimizing the objective function and then a subsequent subgradient step minimizing the feasibility violation of the observed random constraint. We analyze the convergence behavior of the proposed algorithm for diminishing stepsizes and for the case when the objective function is convex or has a quadratic functional growth, unifying the nonsmooth and smooth cases. We prove sublinear convergence rates for this stochastic subgradient algorithm, which are known to be optimal for subgradient methods on this class of problems. When the objective function has a linear least-square form and the constraints are polyhedral, it is shown that the algorithm converges linearly. Numerical evidence supports the effectiveness of our method in real problems.

关键词： stochastic optimization convex functional constraints stochastic subgradient rate of convergence constrained least-squares robust/sparse svm

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Accelerated Degradation Modeling of Power MOSFET Under Multiple Stresses 12

Accelerated Degradation Modeling of Power MOSFET Under Multi...

引用

12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2022

作者： Ye, Xuerong Hu, Yifan Feng, Rui Chen, Cen Zhai, Guofu Tang, Loon Ching School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Shanghai Aerospace Control Technology Institute Shanghai China Department of Industrial and Systems Engineering & Management National University of Singapore Singapore

ISBN: (纸本)9781839538360

Power MOSFET is one of the most critical components in power electronic converters. However, it often performs a lower reliability index under multiple stresses. Conventional single-stress accelerated degradation test (ADT) and corresponding statistical inference are unable to evaluate its reliability accurately. In this article, a new accelerated degradation model is designed by combining multi-stress accelerated model and the generalized Wiener process model. This comprehensive model not only establishes the relationship between drift coefficient and multiple stresses but also take the nonlinearity and uncertainties existing in degradation process into consideration. Subsequently, an ADT with temperature, electrical and vibration stresses for power MOSFET is carried out to obtain the degradation data under different stress levels, which is employed for unknown parameter estimation. Comparative analysis demonstrates that the proposed model can provide a more accurate and reasonable reliability assessment result. © 2022 IEEE.

关键词： Power MOSFET

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Artificial neural network-based direct power control to enhance the performance of a PMSG-wind energy conversion system under real wind speed and parameter uncertainties: An experimental validation

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Energy Reports 2024年 11卷 4356-4378页

作者： Majout, Btissam Bossoufi, Badre Karim, Mohammed Skruch, Pawel Mobayen, Saleh El Mourabit, Youness El Zair Laggoun, Zakaria Laboratory of Engineering Modelling and Systems Analysis SMBA University Fez Morocco Department of Automatic Control and Robotics AGH University of Krakow Poland Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco Laboratory of Electrotechnique Constantine Mentouri Brothers Constantine 1 University Constantine Algeria

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power control (DPC) approach has garnered considerable attention due to its simple and highly adaptable algorithm. This approach has been increasingly recognized in numerous applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system's performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board. © 2024 The Authors

关键词： MATLAB

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Planar Friction Modelling with LuGre Dynamics and Limit Surfaces

arXiv

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arXiv 2023年

作者： Waltersson, Gabriel Arslan Karayiannidis, Yiannis The Department of Electrical Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden The Department of Automatic Control Lund University Sweden

During planar motion, contact surfaces exhibit a coupling between tangential and rotational friction forces. This paper proposes planar friction models grounded in the LuGre model and limit surface theory. First, distributed planar extended state models are proposed and the Elasto-Plastic model is extended for multi-dimensional friction. Subsequently, we derive a reduced planar friction model coupled with a pre-calculated limit surface, that offers reduced computational cost. The limit surface approximation through an ellipsoid is discussed. The properties of the planar friction models are assessed in various simulations, demonstrating that the reduced planar friction model achieves comparable performance to the distributed model while exhibiting ∼ 80 times lower computational cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Friction

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Neural network developed for obstacle avoidance of the four wheeled electric vehicle 30

Neural network developed for obstacle avoidance of the four ...

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30th IEEE International Conference on Electronics, Circuits and systems, ICECS 2023

作者： Sciuto, Grazia Lo Kowol, Pawel Nowak, Pawel Banaś, Waclaw Coco, Salvo Capizzi, Giacomo Silesian University of Technology Department of Mechatronics Gliwice Poland University of Catania Department of Electrical Electronics and Informatics Engineering Catania Italy Silesian University of Technology Department of Automatic Control and Robotic Gliwice Poland Silesian University of Technology Faculty of Applied Mathematics Department of Mathematics Applications and Methods for Artificial Intelligence Gliwice Poland

ISBN: (纸本)9798350326499

Several solutions have been proposed for vehicle navigation in unknown environments to ensure safer driving by avoiding obstacles and collisions. In this work, an electric vehicle equipped with Mecanum 4 omnidirectional holonomic wheels, analog joystick to control driving movement, LiDAR sensors and Wi-Fi able to detect obstacles is investigated based on neural network approach. By experimental tests conducted to avoid collisions by detecting the presence of obstacles, the relation between the resistance distance and speed is described by the artificial neural network. © 2023 IEEE.

关键词： Vehicle safety

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