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Adaptive Two-stage Unscented Kalman Filter for Dynamic State Estimation of Synchronous Generator Under Cyber Attacks Against Measurements

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Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1408-1418页

作者： Dongchen Hou Yonghui Sun Venkata Dinavahi Yi Wang the School of Electrical and Power Engineering Hohai UniversityNanjing 210098China the Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canada the School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China the Henan Engineering Research Center of Power Electronics and Energy Systems Zhengzhou 450001China

This paper develops an adaptive two-stage unscented Kalman filter(ATSUKF)to accurately track operation states of the synchronous generator(SG)under cyber *** achieve high fidelity,considering the excitation system of SGs,a detailed 9~(th)-order SG model for dynamic state estimation is ***,for several common cyber attacks against measurements,a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in ***,to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions,a multi-dimensional adaptive factor matrix is derived to modify the noise covariance ***,a large number of simulation experiments are carried out on the IEEE 39-bus system,which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.

关键词： Cyber attack dynamic state estimation Kalman filtering synchronous generator(SG) unscented transformation

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A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

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Multimedia Tools and Applications 2025年第17期84卷 17951-17977页

作者： Karunkuzhali, D. Meenakshi, B. Lingam, Keerthi Department of Information Technology Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Gitam University Visakhapatnam India

In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "green IoT." Quality in smart cities heavily relies on efficient data gathering and communication. In this study, we propose a novel software architecture tailored for data collection and communication in IoT-enabled smart applications, emphasizing sustainability from the outset. Specifically, for smart cities utilizing IoT-enabled wireless sensor networks, we introduce a Quality of Service (QoS)-aware routing strategy. In the data collection phase, we employ Chaotic Bird Swarm Optimization (CBSO) for creating IoT sensor clusters, accompanied by Improved Differential Search (IDS) to assess the reliability of individual sensor nodes, ultimately designating the most trusted node as the Cluster Head (CH). In the data transport phase, we implement lightweight signcryption to enhance the security of IoT sensor data. Furthermore, we employ an Optimal Data Routing (ODM) approach to compute the most efficient data transmission paths between source and destination nodes within the IoT network. Finally, the performance of our QoS-aware routing strategy is evaluated through network simulations using NS2 and compared against existing approaches, offering valuable insights into its effectiveness and suitability for smart city IoT applications. The main findings of the study indicate that the proposed QoS-aware routing strategy (QOS-AWARE ROUTING) consistently outperforms existing routing algorithms in terms of energy efficiency, network longevity, and latency, both in scenarios with varying sensor node density and over multiple cycles. Additionally, it demonstrates significant advantages, such as up to 90.8% less energy consumption, 66.8% longer network lifespan, and 80.1% reduced latency when compared to the benchmarked methods. © The Author(s), under exclusive licence to S

关键词： Internet of things

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Aircraft Simulation in Game Development Using Reinforcement Learning 4

Aircraft Simulation in Game Development Using Reinforcement ...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Redwan Talha, Md. Navid Hassan, Tonmoy Sabab, Al Mithila, Shaila Sharmin Ahmed, Syed Hasib Department of Electrical and Computer Engineering Plot # 15 Dhaka1229 Bangladesh Department of Electrical and Electronics Engineering Bangladesh Dhaka1229 Bangladesh Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798331530938

The progress in technology has provided opportunities for innovative solutions to intricate challenges. One possible method is employing reinforcement learning to model flying trajectories in intricate environments. Game development is a discipline that involves intricate reasoning and dynamic interplay between the user and the game environment. By employing several gaming engines, developers are now able to replicate real-life situations through the implementation of diverse machine learning methods. Aircraft simulation in game creation using reinforcement learning involves creating a visual depiction of real-life settings where aircraft may navigate complex environments without direct input from a human user. Currently, reinforcement learning is not widely applied in game development, particularly in simulation-based path finding techniques. This algorithm approaches possess the efficacy and capacity to generate sophisticated neural networks capable of directing an agent to do certain tasks. The aim of this project is to create aircraft simulations for game development by utilizing reinforcement-learning techniques, so that it can provide a foundational idea of the usage of this algorithm in path-detection based decision-making techniques. The goal is to demonstrate the effectiveness of reinforcement learning in a real-world scenario, where the aircraft independently assesses and selects its flying trajectory. The system will undergo testing in three distinct phases, involving the utilization of Blender3D, Unity 3D, and Anaconda prompts. The results will then be compared using TensorFlow. Several training sessions will be conducted in various environments using the Anaconda environment to optimize the outcomes. In the latter stages of development, a dynamic user interface will be implemented to enhance the user's experience. The method is anticipated to produce 152% improved AI-trained data, which can be utilized for constructing extensive simulation and game-proj

关键词： Flight simulators

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From Perception to Action: Building a Robust AI System for Safe and Adaptive Autonomous Driving 15

From Perception to Action: Building a Robust AI System for S...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Taha, Adnan Sabith Hossain, Md Rafat Esha, Sheikh Aysha Khatun Pantha, Iffat Mahmud Riya, Aparna Sarker Uddin, Md.Jamil Shakur, Md. Abdus Computer Science and Engineering Bangladesh Pace University Data Science United States North South University Computer Science and Engineering Bangladesh Brac University Computer Science and Engineering Bangladesh Eastern University Electrical and Electronics Engineering Bangladesh Electrical and Electronics Engineering Bangladesh

ISBN: (纸本)9798350370249

This study explores the development of a self-driving car using a combination of deep learning (DL), machine learning (ML), computer vision (CV), and convolutional neural networks (CNN). The proposed system aims to simulate human-like decision making in response to external conditions encountered during autonomous driving. The approach involves real-time on-road testing and a self-training mechanism to enable the car to continuously learn and adapt. Furthermore, the text suggests an investigation into the fundamental principles of artificial intelligence (AI) and their role in the autonomous car's functionality. © 2024 IEEE.

关键词： Control Perception Planning Self-driving car

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Microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

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Australian Journal of electrical and electronics engineering 2024年第3期21卷 213-227页

作者： Shirley, C.P. Pattar, Jagannath Kavitha Rani, P. Saini, Sumit Ranga, Jarabala Elangovan, D. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Mechanical Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electrical Engineering School of Engineering & Technology Central University of Haryana Mahendergarh India Detartment of Electrical and Electronics Engineering Ramachandra College of Engineering Eluru India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi institute of technology Hyderabad India

By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). ©, Engineers Australia.

关键词： Energy utilization

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Modelling of Wideband Concentric Ring Frequency Selective Surface for 5G Devices

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computers, Materials & Continua 2023年第1期74卷 341-361页

作者： Ankush Kapoor Pradeep Kumar Ranjan Mishra Department of Electronics and Communication Engineering Jawaharlal Nehru Government Engineering CollegeSundernagar175018India Discipline of Electrical Electronic and Computer EngineeringUniversity of KwaZulu-NatalDurban4041South Africa Department of Electrical and Electronics Engineering University of Petroleum and Energy StudiesDehradun248007India

Frequency selective surfaces(FSSs)play an important role in wireless systems as these can be used as filters,in isolating the unwanted radiation,in microstrip patch antennas for improving the performance of these antennas and in other 5G *** analysis and design of the double concentric ring frequency selective surface(DCRFSS)is presented in this *** the sub-6 GHz 5G FR1 spectrum,a computational synthesis technique for creating DCRFSS based spatial filters is *** analytical tools presented in this study can be used to gain a better understanding of filtering processes and for constructing the spatial *** of the loop sizes,angles of incidence,and polarization of the concentric rings are the factors which influence the transmission coefficient as per the thorough investigation performed in this paper.A novel synthesis approach based on mathematical equations that may be used to determine the physical parameters ofDCRFSSbased spatial filters is *** proposed synthesis technique is validated by comparing results from high frequency structure simulator(HFSS),Ansys electronic desktop circuit editor,and an experimental ***,the findings acquired from a unit cell are expanded to a 2×2 array,which shows identical performance and therefore proves its stability.

关键词： Spatial filters double concentric ring frequency selective surface(DCRFSS) angle of incidence finite element method(FEM) polarization angles

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Deep learning and machine learning based highly accurate reflection prediction model for multi layers anti-reflection coatings

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Optical and Quantum electronics 2025年第1期57卷 1-22页

作者： Oktay, Semih Duru, İremnur Bakır, Halit Tabaru, Timuçin Emre Department of Electrical-Electronics Engineering Sivas University of Science and Technology Sivas Turkey Computer Engineering Department Sivas University of Science and Technology Sivas Turkey

This study focuses on creating an accurate reflection prediction model that will guide the design of filters with multilayer Anti-Reflection Coating (ARC) to optimize the thickness parameters using Machine Learning (ML) and Deep Learning (DL) techniques. This model aims to shed light on the design process of a multilayer optical filter, making it more cost-effective by providing faster and more precise production. In creating this model, a dataset containing data obtained from 3000 (1500 Ge–Al2O3, 1500 Ge–SiO2) simulations previously performed on a computer based on the thicknesses of multilayer structural materials was used. The data are generated using Computational Electromagnetic simulation software based on the Finite-Difference Time-Domain method. To understand the mechanism of the proposed model, two different two-layer coating simulations were studied. While Ge was used as the substrate in both coatings, Al2O3 and SiO2 were used as the second layers. The data set consists of the 3–5 µm and 8–12 µm bands typical for the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands and includes reflectance values for wavelengths ranging between these spectra. In the specified 2-layer data set, the average reflectance was obtained with a minimum of 0.36 at 515 nm Ge and 910 nm SiO2 thicknesses. This value can be increased by adapting the proposed model to more than 2 layers. Six ML algorithms and a DL model, including artificial neural networks and convolutional neural networks, are evaluated to determine the most effective approach for predicting reflectance properties. Furthermore, in the proposed model, a hyperparameter tuning phase is used in the study to compare the efficiency of ML and DL methods to generate dual-band ARC and maximize the prediction accuracy of the DL algorithm. To our knowledge, this is the first time this has been implemented in this field. The results show that ML models, particularly decision tree (MSE: 0.00000069, RMSE: 0.00083), rand

关键词： Antireflection coatings

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Deep Learning-Enhanced Beamforming for Signal Quality Improvement in Heterogeneous Wireless Networks 3

Deep Learning-Enhanced Beamforming for Signal Quality Improv...

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3rd Odisha International Conference on electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Samanta, Indu Sekhar Rout, Pravat Kumar Joshi, Sukumar Mohanty, Sarthak Panda, Subhasis Department of Computer Science and Engineering Bhubaneswar India Department of Electrical and Electronics Engineering Bhubaneswar India Department of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9798350354379

Beamforming is now a basic technique in wireless communication to improve signal quality and reduce interference. This study investigates the use of deep learning-enhanced beamforming to improve the quality of transmissions in heterogeneous wireless networks. Using a deep convolutional neural network (CNN), the suggested approach aims to predict the ideal weight vectors for antenna arrays, thereby overcoming the challenges of different network circumstances and interference kinds. The efficiency of the suggested approach in achieving higher signal-to-simulation results shows the interference-plus-noise ratio (SINR) over conventional beamforming methods. © 2024 IEEE.

关键词： Beamforming

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Toward Efficient Coordination of Power Distribution Network and Electric Vehicles: Deep Reinforcement Learning With Robust Reward Function

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8164-8176页

作者： Li, Peiyue Chen, Sheng Wei, Zhinong Wu, Qiuwei Sun, Guoqiang Jin, Jiahui Hohai University School of Electrical and Power Engineering Nanjing210098 China Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

The rapid increase in electric vehicle (EV) usage has led to an urgent need for coordinating EV charging activities with power distribution networks (PDNs) to accommodate the resulting redistributed electrical demand and implement greater operational flexibility for PDNs. However, current efforts relying on the implementation of EV charging price strategies to influence the decision-making activities of EV users at charging stations face challenges arising from the uncertainty of EV charging behavior and data privacy concerns. This work addresses these issues by developing a robust reinforcement learning approach that requires no personal EV user information to determine charging schedules and pricing strategies at charging stations, while the uncertainty in EV behavior is addressed by applying a robust reward function. The loss function is relaxed using Holder’s and Cauchy-Schwarz inequalities, which yields an upper bound for the loss caused by the worst case scenario and therefore enhances the computational efficiency of the solution process. Numerical results demonstrate that the proposed method contributes to balancing the PDN load and improving the utilization of fast charging stations (FCSs). Compared to deterministic and traditional robust methods, the proposed method reduces computation time in uncertain environments while ensuring moderate revenue of FCSs. © 2015 IEEE.

关键词： Power distribution networks

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Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

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Energy engineering 2023年第8期120卷 1747-1761页

作者： Nisha Vasudevan Vasudevan Venkatraman A.Ramkumar T.Muthukumar A.Sheela M.Vetrivel R.J.Vijaya Saraswathi F.T.Josh Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Computer Science Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Electrical and Electronics Engineering Kongunadu College of Engineering and TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Kongu Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Narasu’s Sarathy Institute of TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Panimalar Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Karunya Institute of Technology&SciencesTamil NaduIndia

MigroGrid(MG)has emerged to resolve the growing demand for *** because of its inconsistent output,it can result in various power quality(PQ)*** is a problem that is becoming more and more important for the reliability of power systems that use renewable energy ***,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply ***,this research focuses on power quality enhancement in the MG using hybrid shunt ***,the performance of the filter mainly depends upon the design,and stability of the *** efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)*** performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this ***,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in *** the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

关键词： Artificial intelligence resistive inductive load shunt hybrid filter smart grid adaptive fuzzy back-stepping power factor

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