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Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Roman Mishenin Alexander Eryomin Tatyana Tsoy Evgeni Magid Department of Computer Engineering (DCE) Moscow Institute of Electronics and Mathematics Higher School of Economics Moscow Russian Federation Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russian Federation

ISBN: (数字)9798331517564

ISBN: (纸本)9798331517571

In this paper, we present an implementation of the CautiousBug algorithm within the Noetic distribution of the Robot Operating System (ROS). Bug algorithms address a challenge of robot navigation in unknown environments without relying on pre-existing maps or constructing new ones. These algorithms utilize odometry data, operate without a map, require minimal computational resources, and can be implemented on relatively simple hardware. A C++ software application was created to simulate a behavior of the CautiousBug algorithm in various environments within the Gazebo simulator. This application allows for an analysis of key metrics, including accumulated yaw, distance traversed and algorithm's runtime. We conducted a set of virtual experiments in the Gazebo to evaluate the CautiousBug performance.

关键词： Runtime Navigation Software architecture Working environment noise Operating systems Software algorithms Software Robustness Path planning Robots

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Predicting Energy Market Imbalance Prices with Random Forest: A Rolling Window Approach

Predicting Energy Market Imbalance Prices with Random Forest...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Andreas C. Makrides Stylianos Loizidis Chrysanthos Charalambous George Makrides George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Balancing supply and demand is essential for electricity grid stability, particularly with the increasing integration of renewable energy sources (RES). Accurate forecasting of imbalance prices can significantly enhance grid management and market efficiency. This study focuses on predicting imbalance prices in the Finnish market using a rolling window approach with a Random Forest (RF) model, addressing the variable nature of renewable energy sources (RES). The RF model demonstrated high forecasting accuracy across three cross-validation periods, achieving a highest coefficient of determination (R²) of 0.906 and a normalized root mean squared error (nRMSE) of 2.874% in the first period. When applied to the test set, the model yielded an R² of 0.90 and an nRMSE of 4.29%, effectively capturing non-linear and complex behaviors in imbalance prices. These results indicate that the RF model is a robust tool for imbalance price prediction, aiding market participants and system operators in enhancing decision-making and planning. Future research could explore the model's application in other markets and under various price conditions.

关键词： Radio frequency Adaptation models Renewable energy sources Accuracy Electricity Production Predictive models Power grids Forecasting Random forests

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Predictive Analytics for Maximizing the Photovoltaic System Performance

Predictive Analytics for Maximizing the Photovoltaic System ...

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Andreas Livera Demetris Marangis Georgios Tziolis Vasiliki Paraskeva George Makrides George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (数字)9781665464260

ISBN: (纸本)9781665475822

To ensure the smooth Terawatt (TW) photovoltaic (PV) transition and secure the TWh power generation over the service lifetime, enhanced digitalization, and automation approaches for effective operation and maintenance (O&M) of PV systems are required. This highlights the urgent need for corrective and predictive analytics to detect and predict possible anomalies in PV systems and to effectively manage and optimally schedule the field O&M activities. To fulfill this need, a failure detection algorithm along with a predictive maintenance model, that leverages statistical and machine learning (ML) principles, are proposed to accurately detect failures and to predict potential fault issues before they occur. The developed algorithm and the model were benchmarked using historical field data from a PV power plant in Greece. The obtained results showed the effectiveness of the algorithm for accurate failure detection and the capability of the predictive model for generating predictive maintenance alarms. The integration of corrective and predictive analytics into a monitoring software solution can help PV plant operators to schedule the field O&M activities, by providing valuable fault insights and actionable maintenance recommendations.

关键词： Photovoltaic systems Schedules Machine learning algorithms Software algorithms Predictive models Prediction algorithms Software Maintenance Predictive analytics Predictive maintenance

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Time-Varying Direction-of-Arrival Estimation Exploiting Mamba Network

Time-Varying Direction-of-Arrival Estimation Exploiting Mamb...

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IEEE International Conference on Radar

作者： Saidur R. Pavel Mirza A. Haider Yimin D. Zhang Yanwu Ding Dan Shen Khanh Pham Genshe Chen Department of Electrical and Computer Engineering Temple University Philadelphia PA USA Department of Electrical and Computer Engineering Wichita State University Wichita KS USA Intelligent Fusion Technology Inc. Germantown MD USA Air Force Research Laboratory Kirtland Air Force Base NM USA

ISBN: (数字)9798331539566

ISBN: (纸本)9798331539573

Direction-of-arrival (DOA) estimation for moving targets presents a significant challenge in array signal processing. Traditional DOA estimation and tracking methods often encounter limitations due to the infeasibility of acquiring large volumes of stationary data and performing subspace-based processing over many snapshots, and lead to high computational costs. Recently, deep learning techniques have been effectively applied in DOA estimation, owing to their reduced complexity during inference. In this paper, we propose the use of Mamba network as a state-space model-based approach to estimate and track DOAs that vary snapshot-by-snapshot. The proposed network is interpretable and hardware-efficient, making it advantageous for training and real-time inference.

关键词： Deep learning Training Direction-of-arrival estimation Target tracking Simulation Estimation Radar Radar tracking Real-time systems State-space methods

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Techno-economic evaluation of Energy Community configurations based on the individual economic benefit of the members: the case of Cyprus

Techno-economic evaluation of Energy Community configuration...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Georgios Yiasoumas Andreas V. Olympios George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Energy Communities (ECs) enable the participation of citizens in the energy transition, facilitating the shift towards a consumer-centric energy system. Given the voluntary and open nature of ECs, the participation of citizens can be challenging, especially in the early stages of development. This paper addresses this by evaluating the individual economic benefits of existing or potential EC members under different EC configurations. Three basic EC configurations were examined for the case of Cyprus: (i) the EC acting as an electricity producer and exporter, (ii) the EC acting as an electricity producer for maximum self-consumption, and (iii) the EC members trading energy among themselves using an EC-based local electricity market. The results showcased the potential of ECs as an alternative to traditional RES residential schemes, while also highlighting the economic challenges associated with implementing such configurations.

关键词： Electric potential Energy consumption Costs Electricity Government Ecosystems Production Benchmark testing Electricity supply industry

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Net Load Prediction Using Machine Learning Methods: A Case Study of Residential Households

Net Load Prediction Using Machine Learning Methods: A Case S...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Panagiotis Herodotou Georgios Tziolis George Makrides George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus (UCY) PHAETHON Centre of Excellence (CoE) for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

As residential solar photovoltaic (PV) systems are increasingly integrated into distribution networks globally, there is a growing necessity to shift from traditional load forecasting to net load forecasting (i.e., predicting the combination of PV and load collectively). Understanding and predicting the daily net energy of a typical household is crucial for designing and sizing appropriate renewable energy systems and storage solutions. This paper compares the performance of different machine learning methods in predicting the net load patterns of a typical household. The database contained PV production and energy load records from 68 actual households in Cyprus, over a period of one year. The results showed that the eXtreme Gradient Boosting (XGBoost) model outperformed all other models yielding a normalized root mean square error of 5.52%. Despite the inherent unpredictability of net load patterns, the findings indicate that the proposed XGBoost model can achieve high prediction accuracies for household electricity net load profiles.

关键词： Analytical models Accuracy Load forecasting Production Predictive models Data models Real-time systems Smart grids Random forests Load modeling

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Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

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Enhanced Soil Property Estimations from Earth Observation Data with Differential Evolution-Based Multi-Objective TSK Model

Enhanced Soil Property Estimations from Earth Observation Da...

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Congress on Evolutionary Computation

作者： Nikolaos L. Tsakiridis Nikiforos Samarinas Eleni Kalopesa John B. Theocharis George C. Zalidis Department of Agriculture Spectra Lab Group Laboratory of Remote Sensing Spectroscopy and GIS Aristotle University of Thessaloniki Thessaloniki Greece Automation and Robotics Laboratory Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

This paper introduces a novel approach integrating Differential Evolution (DE) with multi-objective optimization techniques for enhancing Type-1 Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems to attain both fair predictive performance and enhanced interpretability within the context of explainable artificial intelligence. The developed approach encodes the whole knowledge base into one chromosome and endeavors to concurrently optimize the prediction accuracy, the number of rules, and the selection of relevant input features. The methodology was tested on Earth observation data for the estimation of soil health parameters via infrared spectroscopy, and more concretely i) on the 2015 LUCAS topsoil dataset to predict three key soil properties (namely, soil organic carbon, clay content, and pH) from laboratory spectra, and ii) on a set of PRISMA hyperspectral images to predict soil organic carbon in a region of northern Greece. Compared to the classical Random Forest (RF) algorithm, our proposed learning algorithm attains a fair balance between accuracy and interpretability, and is statistically equivalent to RF in terms of accuracy. The findings underscore the potential of the proposed methodology in refining TSK models and its applicability in Earth observation-driven predictions, paving the way for both enhanced modeling accuracy and sparse feature selection.

关键词： Earth Radio frequency Accuracy Soil properties Estimation Predictive models Prediction algorithms

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Comparative Analysis of Different Sliding Mode Control Approaches for Load Frequency Control in Edge of the Grid System

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Aich, Pritom Ahmed, Anis Shah, Rakibuzzaman Islam, Md Rabiul Roy, Naruttam Kumar Nasir, Ali Department of Electrical and Electronic Engineering Daffodil International University Dhaka Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna Bangladesh Federation University Australia Mt HelenVIC Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW Australia Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

The integration of renewable energy sources (RESs) at the edge of the grid plays a contributory role in advancing the transition to a sustainable energy future. However, this transition introduces significant variability, which challenges the maintenance of frequency stability in power systems. To address the impacts of RES variability, various robust control strategies have been developed. This paper compares the performance of different advanced sliding mode control (SMC) approaches, i.e., super twisting (ST) algorithm-based second order sliding mode control (STSOSMC), second order sliding mode control (SOSMC), double integral sliding mode control (DISMC) and integral sliding mode control (ISMC) with conventional sliding mode control (SMC) for load frequency control with stochastic RESs such as biogas generators (BG) and wind-based doubly fed induction generators (DFIG). The impact of the advanced sliding mode controllers on the voltage and rotor angle stability of the system is also explored. The efficacy of the controllers is tested under different load disturbances, considering nonlinearities such as communication delay (CD), sensor inaccuracy (SI), governor dead band (GDB), and generation rate constraint (GRC). The results demonstrate that STSOSMC achieves superior frequency regulation with minimal chattering and outperforms other controllers in terms of settling time, overshooting, and undershooting in frequency deviation. The result also demonstrates the effectiveness of STSOSMC in managing the variability and disturbances of RESs. Therefore, it could be considered the most robust control mechanism from the edge of the grid system with high penetration of RESs. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Robust control

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Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints

arXiv

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arXiv 2023年

作者： Sun, Yuzhu Van, Mien McIlvanna, Stephen Nhat, Nguyen Minh McLoone, Seán Ceglarek, Dariusz Ge, Shuzhi Sam The Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Queen’s University Northern Ireland Belfast United Kingdom The Warwick Manufacturing Group University of Warwick Coventry United Kingdom The Department of Electrical and Computer Engineering Social Robotics Laboratory National University of Singapore Singapore

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Lyapunov functions

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