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2023 IEEE International Conference on Environment and electrical engineering and 2023 IEEE Industrial and Commercial Power systems Europe, EEEIC / I and CPS Europe 2023

作者： Aghmadi, Ahmed Mohammed, Osama Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798350347432

The importance of short-term solar radiation forecasting for power system use and management cannot be overstated. However, Non-stationarity and unpredictability make accurate forecasting difficult. Time series approaches are suitable for forecasting stationary time series derived from a non-stationary sequence. Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) are time-domain decomposition methods used to separate the components of the original solar radiation time series that have distinct time1-scale characteristics. As a result, the components are forecasted using the Back Propagation Neural Network (BPNN) model, which is a simple and effective machine learning method. This paper presents a hybrid EMD/EEMD-BPNN model for predicting short-term solar irradiance. To identify unique data information at different time scales, EMD/EEMD first breaks up the solar radiation time series into several stationary or fundamental sub-series. The next step is to create BPNN models with specified parameters for each subsequence to forecast new ones. Finally, each subsequence's projected value is combined to generate the final forecast result. The accuracy of solar forecasts has greatly increased, especially when utilizing hybrid methods. Furthermore, using the proposed hybrid technique for multistep forecasting resulted in even more improvement. The basic BPNN model for 15 min time step achieves predicting with a root mean square error (RMSE) of roughly 416.04 W/m2 for 15min;however, this error decreases to 65.25 W/m2 with the EMD-Hybrid Model, and 32.86 W/m2 with the EEMD-Hybrid Model. In addition, the results of the skill score have proven clearly that EMD/EEMD-BPNN Model performs better compared to typical BPNN using several meteorological inputs © 2023 IEEE.

关键词： Empirical mode decomposition

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Stochastic Characterization-Based Performance Analysis of an Emulated Communication Network for Cyber-Physical Shipboard Power systems

Stochastic Characterization-Based Performance Analysis of an...

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2023 IEEE Electric Ship Technologies Symposium, ESTS 2023

作者： Abdelrahman, Mahmoud S. Nguyen, Tung-Lam Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9781665465151

Emerging advanced and innovative Information and Communication Technologies (ICT), automation strategies, and associated algorithms in the traditional power system have transformed it into a Cyber-Physical Power System (CPPS). Energy management and control are crucial in the Shipboard Power System operation. For instance, to meet the ship's energy and other ancillary demands, especially the mission-critical loads that require practical and secure network communication. Therefore, a comprehensive modeling and testing and validation approach was developed in this paper. This enables flexible system-level aspect evaluation through numerous experiments, including virtual, real, and mixed lab settings. This paper presents a scalable and flexible three-layer cyber-physical co-simulation platform implemented in the FIU smart grid testbed. This includes real-time power system simulation using OPAL-RT, ns3 for communication network emulation, and MATLAB/SIMULINK for implementing various applications. To demonstrate the platform's effective operation, energy management with a central controller for SPS was configured and implemented to test the communication network performance by creating tools to characterize the network latency for this application. © 2023 IEEE.

关键词： Ships

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State of Charge Estimation for Zinc-Bromine Flow Batteries by Improved Long Short-Term Memory Network and Kalman Filter

State of Charge Estimation for Zinc-Bromine Flow Batteries b...

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2024 CPSS and IEEE International Symposium on Energy Storage and Conversion, CPSS and ISESC 2024

作者： Peng, Kaiwen Chen, Bichen Wang, Liguo Sidorov, Denis Dreglea, Aliona Wang, Zongjie School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Northeast Agricultural University Harbin China Melentiev Energy Systems Institute Siberian Branch of the Russian Academy of Sciences Irkutsk Russia University of Connecticut Department of Electrical and Computer Engineering Mansfield United States

ISBN: (纸本)9798350380514

In order to improve the accuracy of estimating the state of charge (SOC) of zinc-bromine flow batteries (ZBFB) in the discharge stage and overcome the problems caused by sudden voltage changes, this paper proposes an algorithm for estimating and correcting the SOC. The core contribution is the proposal of an improved Long Short-Term Memory (LSTM) neural network algorithm, which considers the effect of the circulating pump speed on the battery voltage during the discharge stage, and establishes a neural network model for the battery. The estimation result of the improved LSTM network has a mean square error of 3.322, representing a 12% improvement in estimation accuracy compared to the traditional LSTM network. Additionally, Kalman filtering is employed for error mitigation to further enhance estimation accuracy. After applying the proposed method, the maximum error between the estimated SOC and the actual SOC is 1.91%, and the average error is 0.30%. This indicates that the proposed method improves the accuracy of SOC estimation through enhanced real-time LSTM network estimation and Kalman filtering error cancellation, particularly during voltage sudden change in the discharge of ZBFB. © 2024 IEEE.

关键词： Kalman filters

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Frequency Stability Enhancement in Low-Inertia Power System Using an Optimal Control Scheme

Frequency Stability Enhancement in Low-Inertia Power System ...

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2023 IEEE International Conference on Environment and electrical engineering and 2023 IEEE Industrial and Commercial Power systems Europe, EEEIC / I and CPS Europe 2023

作者： Ali, Ola Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798350347432

Power system dynamics have significantly changed with the global integration of renewable energy sources (RESs). Traditionally, the stored kinetic energy is used for compensating the generation mismatch using the rotating mass of the Synchronous Generators (SGs). With the increase in RESs penetration, SGs are being replaced by inverter-based RESs generation systems which are decoupled from the power systems through power converters. Therefore, the total inertia of the system has decreased, and the system's stability is significantly impacted as the value of the rate of change of frequency (ROCOF) and frequency deviations will be increased. To overcome these challenges, inertia emulation from various energy storage systems (ESS) is used to act as a virtual source of inertia power. This work presents an isolated microgrid (MG) that includes conventional power generation sources and RESs, and an optimal virtual inertia (VI) control loop is proposed for frequency stability enhancement. Supercapacitors (SC) bank with a PID controller presents the VI control loop and acts as a supplementary source for inertia power to support the frequency stability of the system. A genetic algorithm (GA) is used as an optimization technique to get the optimal values of the PID controller gains. The performance of the islanded MG with the proposed optimal control scheme is examined under different operating conditions with varying levels of RESs penetration and system parameters uncertainties. The proposed technique showed its effectiveness and robustness over the system's operation without VI control for keeping the system stable under different operating conditions. © 2023 IEEE.

关键词： Genetic algorithms

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The Truncated Cone Effect in AFM Nanoindentation on Soft Materials

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Micro and Nanosystems 2023年第2期15卷 153-158页

作者： Kontomaris, Stylianos-Vasileios Malamou, Anna Department of Research and Development BioNanoTec LTD Nicosia Cyprus Faculty of Engineering and Architecture Metropolitan College Athens Greece Radar Systems and Remote Sensing Lab of the School of Electrical & Computer Engineering of National Technical University of Athens Athens Greece

Background: Atomic Force Microscopy (AFM) nanoindentation is the principal method for the characterization of soft materials at the nanoscale. In most cases, pyramidal tips are used and approximated to perfect cones. However, the extended use of the AFM tip may alter its sharp-ness. Objective: In many cases, a truncated cone shape is appropriate for tip modeling. In this technical note, the equation that relates the force with the indentation depth when indenting an elastic half-space using a truncated cone is derived. Methods: The nanoindentation equation for a truncated cone tip is derived using the fundamental differential equation that relates the sample’s contact stiffness with Young’s modulus. Results: When fitting Sneddon’s equation (which is valid for a perfect cone) on data obtained using a truncated cone-shaped AFM tip, the results show a ‘pseudo-softening’ behavior. Conclusion: The AFM tip's sharpness in nanoindentation experiments is a crucial parameter for obtaining the correct mechanical patterns of unknown samples. © 2023 Bentham Science Publishers.

关键词： Biological materials

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State of Charge Estimation Using Data-Driven Models for Inverter-Based systems

State of Charge Estimation Using Data-Driven Models for Inve...

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2023 IEEE Design Methodologies Conference, DMC 2023

作者： Hussein, Hossam Donekal, Abhishek Aghmadi, Ahmed Rafin, S M Sajjad Hossain Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798350315547

Lithium-ion batteries are playing a critical role in many applications nowadays, from small-scale electronic devices to grid-scale storage systems. To maintain its continuous operation and increase its life span, the state of charge of the battery should be determined to ensure safe operating conditions. Among the existing charge estimation methods, data-driven models are flourishing these days. This work presents a state of charge estimation for the eFlex 52.8V/5.4 kWh lithium iron phosphate battery pack at the Energy systems research Laboratory (ESRL) at FIU. Three different machine learning models were implemented and trained through Python code to achieve the most accurate SoC estimation. Although the three proposed models can efficiently estimate the battery's SoC with an acceptable error percentage, the random forest regression model has proven its outperformance among the selected models with a percentage mean square error less than 0.01. © 2023 IEEE.

关键词： Lithium-ion batteries

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ANN-Based Secure Control of Islanded Microgrid Under False Data Injection Cyber-Attack

ANN-Based Secure Control of Islanded Microgrid Under False D...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Kharchouf, Ibtissam Abdelrahman, Mahmoud S. Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798350320169

AC microgrids may be a target of cyber-attacks such as False Data Injection attack (FDIA) or Denial of Service attacks due to the use of communication technologies. These types of attacks target the communication infrastructure or the control system itself. For instance, FDIAs aim to inject false data into AC microgrids to disrupt the control system. Techniques that detect and mitigate the effect of such attacks are needed. This paper presents an AC microgrid that is structured by parallel inverters and controlled by distributed consensus control strategies to control the frequency and voltage magnitude through active and reactive power. We will investigate the FDIAs effect on the AC microgrid operation. A viable solution to address the FDIA attacks in AC microgrids is developed using an ANN-based reference tracking application to detect and mitigate the attack. The simulation results proved that the proposed strategy could detect and mitigate FDIA attacks especially if the ANN is well trained. © 2023 IEEE.

关键词： Neural networks

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Ship Platform Access Control Method Based on Zero Trust 7

Ship Platform Access Control Method Based on Zero Trust

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7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Wei, Renlong Li, Guobing Beijing University of Posts and Telecommunications Beijing China Beihang University School of Computer Science and Engineering Beijing China Cssc Systems Engineering Research Institute China

ISBN: (纸本)9798350370782

With the digital transformation of the shipbuilding industry, the interaction between ship platform systems and intelligent equipment has become closer, which has led to the increasingly blurred security boundaries of ship platform systems and increased the risk of illegal access to ship platform resources. At present, ship platforms still mainly rely on static access control methods to protect sensitive resources, which can no longer meet the growing access control needs in a de-bordered environment. In order to meet this challenge, this paper introduces the zero-trust concept into the ship platform access control system. By designing a trust evaluation algorithm and a dynamic access control algorithm, the ship platform permissions can be managed more finely, and experiments are designed to verify the effectiveness of the scheme in access control. © 2024 IEEE.

关键词： Shipyards

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Exploration of Thermoelectric Energy Harvesting for Secure, TLS-Based Industrial IoT Nodes 1

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7th International Conference on Internet of Things, ICIOT 2022 held as Part of the Services Conference Federation, SCF 2022

作者： Lauer, Frederik Schöffel, Maximilian Rheinländer, Carl C. Wehn, Norbert Department of Electrical and Computer Engineering Microelectronic Systems Design Research Group Technische Universität Kaiserslautern Kaiserslautern67663 Germany

ISBN: (数字)9783031235825

ISBN: (纸本)9783031235818

Security is one of the biggest challenges, particularly in the Industrial IoT and in critical infrastructures. Complex cryptographic computations are in contrast to the low energy budget of the devices, especially when independence from the power grid is required, as it is the case with retrofitted sensor nodes. Energy harvesting offers a promising alternative but tightens the energy constraints of the application further. In this work, we investigate how IoT edge devices can be powered by thermal energy harvesting and concurrently meet the stringent TLS-based security requirements. We analyze a thermoelectric generator system at its lowest power output region and evaluate different energy storage technologies in a representative IoT architecture. Our results show that temperature gradients as low as 1 K are sufficient to enable secure connections every 20 min in a representative IIoT application. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy harvesting

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