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13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2022

作者： Miljan, Petra Čović, Nikolina Pavić, Ivan Pandžić, Hrvoje Department of Energy and Power Systems University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9781839538445

In the European energy market, every market participant has a balance responsibility. With the expiration of feed-in tariffs, renewables are also becoming balance responsible. Since renewables, such as wind and solar, have very variable and not entirely predictable output, their imbalance management is highly challenging. This paper investigates the possibility of their imbalance management by installing battery energy storage within the renewable facility. The proposed bidding and balancing model, applicable to any type of distributed energy resource, is simulated for deterministic prices from the year 2020 and for the stochastic solar production scenarios. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Battery storage

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Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

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Intelligent Automation & Soft computing 2023年第1期35卷 957-969页

作者： T.Shanthi M.Ramprasath A.Kavitha T.Muruganantham Department of Electronics and Communication Engineering Sona College of TechnologySalem636005Tamil NaduIndia Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112TamilnaduIndia

The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport systems(IATS).Despite the IATS's benefits,security remains a significant *** technology has grown in popularity as a means of implementing safe,dependable,and decentralised independent IATS systems,allowing for more utilisation of legacy IATS infrastructures and resources,which is especially advantageous for crowdsourcing *** technology can be used to address security concerns in the IATS and to aid in logistics *** light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems,this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching(BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment ***,the deep belief network(DBN)model is used to select suitable vehicles and goods for ***,the chaotic krill herd technique is used to tune the DBN model’s *** performance of the BDL-IATS technique is validated,and the findings are inspected under a variety of *** simulationfindings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.

关键词： Blockchain ethereum intelligent autonomous transport system security deep belief network

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Laboratory Setup for Stability and Optimization Studies of Hybrid Microgrids 7

Laboratory Setup for Stability and Optimization Studies of H...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Beus, Mateo Herencic, Lin Pandzic, Hrvoje Rajsl, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9789532901160

Utilization of microgrids is an integral part of the efforts for increased resiliency and deep integration of renewable energy sources in the power systems, as they allow maximization of the use of locally available resources and operational harmonization of internally interconnected parts of the power systems - either in grid-connected or island mode. Development of laboratory setups for studying different aspects of microgrids has been recognized as a valuable tool for preparation of the real-life implementation. In this paper we present a developed AC/DC laboratory setup that incorporates solar photovoltaic systems, battery energy storage, an electric vehicle charger, a small hydro power plant, electronically controllable loads, as well as resistors and inductors that replicate the topology of the CIGRE low-voltage distribution network. Harmonized operation of the elements of the microgrid is ensured by the implemented supervisory control and data acquisition (SCADA) system. To demonstrate wide potential of the developed setup, we present the results of several experiments in this paper. © 2022 University of Split, FESB.

关键词： Solar energy

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Voltage-based Machine Learning Algorithm for Distribution of End-users Consumption Among the Phases 45

Voltage-based Machine Learning Algorithm for Distribution of...

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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Matijasevic, Terezija Antic, Tomislav Capuder, Tomislav University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9789532331035

Distribution networks are poorly observable, which is especially evident in different analyses of low voltage (LV) networks, where observability is decreased by the reduced number of smart meters and the lack of network data. Smart meters are in most cases used only for measuring consumption data, while other important information, such as the phase connection of end-users, is not adequately monitored. This aggravates the problem of phase identification for energy utilities, which consequently complicates the numerous calculations required for the smooth operation of the distribution network. In this paper, a comparison of voltage and consumption measurements-based phase identification is presented. Furthermore, a machine learning model based on the voltage measurements is extended to correctly identify the phases of end-users which are three-phase connected to an LV network. The model is tested on a simple 18-node network and the IEEE benchmark network with over 100 nodes and more than 50 end-users. Even though the results show a possibility of using both methods in simpler cases, the voltage measurement-based method is more robust and leads to smaller error in the phase detection problem but also can be extended and used in the case of three-phase connected end-users. © 2022 Croatian Society MIPRO.

关键词： Voltage measurement

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Machine learning-based forecast of secondary distribution network losses calculated from the smart meters data 7

Machine learning-based forecast of secondary distribution ne...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Matijasevic, Terezija Antic, Tomislav Capuder, Tomislav University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9789532901160

With greater integration of smart meters, an opportunity to increase the observability of the traditionally unobservable low-voltage distribution networks is created. The purpose of such meters is mainly to collect and store data on end-user consumption for billing purposes, and it is these large data flows that open up a wide range of analyses to Distribution System Operators. Leading in this is the prediction of end-user consumption, which finds its application especially in determining network losses for more efficient planning and operation of distribution networks. Due to the complicated features of the collected load series data, the application of synthetic curves for the consumption forecasting problem is abandoned and energy utilities are turning to more complex solutions, most often based on machine learning algorithms. Therefore, this paper presents a machine learning-based model for forecasting losses in a low-voltage distribution network. Power flow simulation tools are frequently used to estimate and predict active power losses but are applied only in the case of available network topology and elements data. Hence, in this paper, special emphasis is placed on a model that does not rely on network data, but only on historical measurements collected from smart meters. The model is tested on a real-world distribution network with more than 150 end-users. The results show the effectiveness of the model in forecasting active power losses of the observed network, but also highlight the sensitivity of the model to errors, which is a good basis for the implementation of additional algorithms and variables as a means to enabling near real-time operation planning of distribution networks. © 2022 University of Split, FESB.

关键词： Forecasting

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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An Enhanced Secure Authentication Scheme for the Internet of Things

An Enhanced Secure Authentication Scheme for the Internet of...

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Neetu Srivastava, Animesh Barik, Lal Bihari Dhondiyal, Shiv Ashish College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350329186

The Internet of Things (IoT) has led to the proliferation of interconnected devices, including smart appliances and industrial sensors. Nevertheless, the rapid expansion of the IoT ecosystem has given rise to apprehensions regarding security, as compromised devices have the potential to result in data breaches and cyber assaults. This paper presents an enhanced secure authentication scheme for the Io'T, which aims to tackle the issues above by building upon existing authentication methodologies. The multi-step system commences with an initial setup phase wherein each loT device is assigned a unique device ID and a confidential pre-shared key. A secure key exchange protocol generates a session key for each authenticated device. The authentication server generates a random attempt to join the network or execute specific tasks. The machine then signs the data with its private key to authenticate the challenge. The server verifies the answer by comparing it with the public key signature of the device. The proposed methodology enables devices to update their session keys periodically to safeguard against potential attacks. © 2023 IEEE.

关键词： Authentication

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Deep Learning-based Model Benchmarking of Glaucoma Segmentation Using a Novel Ibn Al-Haitham Fundus Image Dataset

Deep Learning-based Model Benchmarking of Glaucoma Segmentat...

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2024 International Conference on Smart computing, IoT and Machine Learning, SIML 2024

作者： Zedan, Mohammad J. M. Zulkifley, Mohd Asyraf Saeed, Bashar Ayad Abdani, Siti Raihanah Universiti Kebangsaan Malaysia Electronic and Systems Engineering Department of Electrical Selangor Malaysia Ninevah University Department of Ophthalmology and Surgery Mosul Iraq Universiti Teknologi MARA School of Computing Sciences Shah Alam Malaysia

ISBN: (纸本)9798350364101

Automated fundus image analysis is critical in diagnosing glaucoma, whereby the optic nerve areas that consist of optic discs and cups need to be precisely mapped, which is a process that requires considerable effort and time. Generally, the application of Artificial intelligence (AI) has made considerable advancements in the fundus imaging-based automated diagnosis of various diseases. Training and assessing the best AI models for glaucoma detection demands a large number of expert-annotated fundus images. However, there is a scarcity of freely available datasets tailored to this need, and even those that are accessible have several constraints, especially for the Middle Eastern country cases. This paper introduces a novel fundus image dataset of 3000 high-resolution, manually annotated color fundus images dedicated to boosting AI-based models for optic disc and cup segmentation. The annotation process was standardized through collaboration among medical experts, ensuring consistent and reliable results. The reliability of each image was assessed using four deep learning-based segmentation models: U-Net, SegNet, FCDenseNet, and PSPNet. The SegNet model outperformed the others, with an accuracy of 98.12%, specificity of 96.35%, and sensitivity of 98.7% for the optic disc and an accuracy of 97%, specificity of 95.4%, and sensitivity of 91.94% for the optic cup. These results not only validate the accuracy of the developed dataset but also demonstrate its potential in advancing disease detection methods, particularly in AI-assisted ophthalmology, leading to new opportunities for advancements in this field. © 2024 IEEE.

关键词： Ophthalmology

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Laboratory Testbed for Advanced Grid Applications of Interconnected Battery Energy Storage systems in Smart Grids 13

Laboratory Testbed for Advanced Grid Applications of Interco...

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2022 IEEE International Conference on Power systems Technology, POWERCON 2022

作者： Krpan, Matej Beus, Mateo Kuzle, Igor Pandzic, Hrvoje University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (数字)9781665417754

ISBN: (纸本)9781665417754

This paper presents a cyber-physical laboratory testbed based on a hierarchical control structure for education, research and development in the field of interconnected battery systems, implemented in the Smart Grid Laboratory at the University of Zagreb. The modular structure of the Smart Grid Laboratory's microgrid enables both the grid-connected and the off-grid operation, as well as hardware-in-the-loop simulations. The hierarchical control architecture enables a coordinated control of battery assets at several temporal scales. In this paper we present testbed use cases for developing distributed control algorithms for voltage and frequency control, and economically optimal centralized dispatch of the battery assets, as well as some concepts for developing educational activities for students. © 2022 IEEE.

关键词： Smart power grids

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Incorporating Settling Time Constraints of Energy Storage systems in Frequency Containment Control Optimization Problems 7

Incorporating Settling Time Constraints of Energy Storage Sy...

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7th IEEE International Energy Conference, ENERGYCON 2022

作者： Krpan, Matej Miletic, Marija Kuzle, Igor Pandzic, Hrvoje University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9781665479820

Response of energy storage units and power plants to a set-point change is not necessarily linear with a constant ramp rate, especially for frequency containment services Which are actuated on a leq 30s time scale. This is because real systems have time lag in their dynamic behavior Which will cause the output to behave nonlinearly before settling down. This paper shows how to implement this behavior into the linear programming optimization framework using settling time constants of a common energy storage system and power plants. Simulation results show that if linear programming is used to model the units participating in the frequency containment services, neglecting the settling time constraints can lead to decisions Which may not be grid-compliant in practice. © 2022 IEEE.

关键词： Energy storage

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