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Load Flow Assignments' Definition from Day-Ahead Electricity Market Interconnection Power Flows: A Study for Transmission Networks

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ENERGIES 2024年第6期17卷 1391页

作者： Fresia, Matteo Minetti, Manuela Procopio, Renato Bonfiglio, Andrea Lisciandrello, Giuseppe Orru, Luca Univ Genoa Dept Elect Elect Telecommun Engn & Naval Architect I-16145 Genoa Italy Terna SpA Rete Elettr Nazl I-00156 Rome Italy

The mass introduction of renewable energy sources (RESs) presents numerous challenges for transmission system operators (TSOs). The Italian TSO, Terna S.p.A., aims to assess the impact of inverter-based generation on system inertia, primary regulating energy and short-circuit power for the year 2030, characterized by a large penetration of these sources. The initial working point of the Italian transmission network has to be defined through load flow (LF) calculations before starting dynamical analyses and simulations of the power system. Terna 2030 development plan projections enable the estimation of active power generation and load for each hour of that year in each Italian market zone, as well as cross-zonal active power flows;this dataset differs from conventional LF assignments. Therefore, in order to set up a LF analysis for the characterization of the working point of the Italian transmission network, LF assignments have to be derived from the input dataset provided by Terna. For this purpose, this paper presents two methods for determining canonical LF assignments for each network bus, aligning with the available data. The methodologies are applied to a simplified model of the Italian network, but they are also valid for other transmission networks with similar topology and meet the future needs of TSOs. The methods are tested at selected hours, revealing that both approaches yield satisfactory results in terms of compliance with the hourly data provided.

关键词： transmission system grid flexibilization load flow optimization algorithm

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OPTIONS attack detection in WSN using Optimized Multitask Multi-Attention Residual Shrinkage Convolutional Neural Network

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KNOWLEDGE-BASED SYSTEMS 2024年 300卷

作者： Selvi, S. Tamil Visalakshi, P. Kumar, M. Senthil SRM Inst Sci & Technol Dept Networking & Commun Kattankulathur 603203 Tamilnadu India SRM Valliammai Engn Coll Dept Cyber Secur Kattankulathur 603203 Tamilnadu India

The rapid increase in Internet users has made web applications essential for daily services, rendering them targets for various cyber-attacks like path traversal, zero-day attacks, and injection attacks. While traditional security measures can prevent many familiar attacks, they are often ineffective against OPTIONS attacks, which involve injecting malicious code via hyperlinks to obstruct user access to legitimate webpage content. To address this novel challenge, we propose the OAD-WSN-MMRCNN technique, leveraging an Optimized Multitask MultiAttention Residual Shrinkage Convolutional Neural Network for OPTIONS attack detection in Wireless Sensor Networks (WSNs). This approach begins by selecting a CPU parameters dataset for attack detection, followed by pre-processing with a Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter to remove redundant data. Key features such as handles, threads, processor, context switch, deferred procedure call (DPC), interrupt delta, CPU socket, and core are extracted using a variable velocity strategy particle swarm optimization algorithm. The MMRCNN, optimized with the Tyrannosaurus optimization algorithm, is then employed to detect normal and OPTIONS attacks. Implemented in Python, the efficacy of OAD-WSN-MMRCNN is evaluated using metrics such as energy consumption, target window, accuracy, precision, F-measure, recall, and CPU utilization. Experimental results demonstrate that OAD-WSN-MMRCNN outperforms existing techniques, achieving a 20 % improvement in detection accuracy and a 25 % reduction in energy consumption, highlighting its effectiveness and potential for enhancing web application cyber security.

关键词： OPTIONS attack Tyrannosaurus optimization algorithm Variable velocity strategy particle swarm optimization algorithm VBMCCKF WSN

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Mitigating Cyber Anomalies in Virtual Power Plants Using Artificial-Neural-Network-Based Secondary Control with a Federated Learning-Trust Adaptation

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ENERGIES 2024年第3期17卷 619页

作者： Taheri, Seyed Iman Davoodi, Mohammadreza Ali, Mohd Hasan Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive communication layer. FL-trust, an improved federated learning (FL) approach, has been recently introduced as a mitigation system for cyber-attacks. However, current FL-trust enhancements, relying solely on proportional-integral (PI), exhibit drawbacks like sensitivity to controller gain fluctuations and a slow response to sudden disturbances, and conventional FL-trust is not directly applicable to the non-independent and identically distributed (non-IID) datasets common in VPPs. To address these limitations, we introduce an artificial neural network (ANN)-based technique to adapt FL-trust to non-IID datasets. The ANN is designed as an intelligent anomaly mitigation control method, employing a dynamic recurrent neural network with exogenous inputs. We consider the effects of the most common VPP attacks, poisoning attacks, on the distributed cooperative controller at the secondary control level. The ANN is trained offline and tested online in the simulated VPP. Using MATLAB simulations on a HOMER-modeled VPP, the proposed technique demonstrates its superior ability to sustain normal VPP operation amidst cyber anomalies, outperforming a PI-based mitigation system in accuracy and detection speed.

关键词： virtual power plant federated learning optimization algorithm power system distributed generation artificial neural network

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An adaptive weight search method based on the Grey wolf optimizer algorithm for skin lesion ensemble classification

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第2期34卷 e23049-e23049页

作者： Liu, Luzhou Zhang, Xiaoxia Xu, Zhinan Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan Peoples R China Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China

Skin cancer is a common type of malignant tumor that poses a serious threat to patients' lives and health, especially melanoma. It may spread to other body parts, resulting in serious complications and death. In the medical field, accurate identification of skin lesion images is crucial for diagnosing different diseases. However, due to the similarity between different skin lesions, it brings some challenges to medical diagnosis. In this paper, a novel Ensemble Learning Model (EL-DLOA) based on deep learning and optimization algorithms is proposed, which uses four different deep neural network architectures to generate confidence levels for classes, and optimization algorithms are used to integrate these confidence levels to make the final predictions. To ensure the model's accuracy and reliability, it is first trained using three different learning rates to find the best classification performance of the model. Then, a new search method based on the grey wolf optimization algorithm is proposed to enhance the grey wolf search efficiency. The method improves the search mechanism by changing the grey wolf's individual position through random perturbation or adaptive mutation, which solves the problem that the grey wolf algorithm is easy to fall into local optimum. Finally, four different ensemble strategies are used to reduce individual model bias in the classification process. The proposed model is trained and evaluated using the publicly available dataset HAM10000. The experimental results show that the improved grey wolf optimization algorithm effectively avoids the premature convergence problem and improves the search combination efficiency. Furthermore, in the ensemble methods, the adaptive weight average ensemble strategy effectively improves the classification performance, yielding accuracy, precision, recall, and F1 scores of 0.888, 0.837, 0.897, and 0.862, respectively. These metrics show varying degrees of improvement over the best performing single model. I

关键词： deep learning ensemble model optimization algorithm skin cancer skin lesion classification

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Ship Trajectory Prediction Model Based on Improved Bi-LSTM

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING 2024年第3期10卷

作者： Li, Weifeng Lian, Yifan Liu, Yaochen Shi, Guoyou Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China

Ship trajectory prediction plays an important role in ensuring ship safety;through accurate ship positioning, the future trajectory of ships and their encounter time and location can be obtained, which facilitates the maritime regulatory authorities to assess the risks of ship encounters and implement effective traffic control. Meanwhile, with the rapid development of the shipping industry, the increasingly complex maritime traffic poses potential risks, which may cause serious traffic accidents and huge economic losses. To improve the accuracy of ship navigation risk prediction and ensure the safety of ship navigation, automatic identification system (AIS) data and deep learning models are used to extract the ship trajectory change feature pattern and apply it to ship trajectory prediction. This study builds the improved bidirectional long short-term memory network (Bi-LSTM) model based on rectified adaptive moment estimation (Radam) and lookahead, respectively. The AIS data of the Port of Tianjin area were selected for model training, and the results of comparison experiments show that the improved Bi-LSTM model has a stronger generalization ability, which further improves the trajectory prediction accuracy, and shows excellent predictive performance. The prediction model is feasible for the prediction of ship navigation trajectory.

关键词： Automatic identification system (AIS) Trajectory prediction Deep learning optimization algorithm

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Optimizing sustainable energy integration: A novel approach using concentrated solar plant and hybrid power supply

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 237卷

作者： Li, Mengyu Liu, Yunlong Li, Ji Li, Fei An, Yagang Gao, Xuezhe State Grid Hebei Mkt Serv Ctr Shijiazhuang 050000 Hebei Peoples R China State Grid Hebei Elect Power Supply Co Ltd Shijiazhuang 050000 Hebei Peoples R China

This study pioneers sustainable energy solutions amidst escalating demand and fossil fuel depletion. Its primary novelty lies in proposing an integrated energy system, encompassing a concentrated solar plant, thermal energy system, and hybrid power supply within the solar energy domain. This integration substantially enhances overall system performance. The architectural design integrates various energy conversion components, including wind turbines, energy storage devices, and electric heaters, fostering synergistic collaboration. The optimization of energy usage is a key focus, utilizing heating and electricity costs as pivotal signals for participation in an Integrated Demand Response program (IRD). The demand response model, encompassing both thermal and electric loads, incorporates a sophisticated price elasticity matrix. Addressing challenges associated with renewable energy intermittency, phased carbon emissions, and the advantages of demand response, this study introduces an advanced system operation optimization model to effectively curtail operational costs. In the realm of methodology, this research breaks new ground by proposing an enhanced optimization algorithm that ingeniously combines the Whale optimization algorithm (WOA) with the Wavelet Transform (WT). This novel amalgamation significantly enhances the algorithm's search capabilities, providing superior performance in tackling the complexities inherent in the optimization problem. The proposed model undergoes rigorous evaluation in a comprehensive study featuring diverse energy sources and multiple scenarios. The outcomes of this evaluation distinctly showcase substantial reductions in overall operational costs compared to conventional approaches. This multifaceted contribution positions the study at the forefront of endeavors to revolutionize energy systems for enhanced sustainability and efficiency.

关键词： Clean energy Demand response Hybrid power supply optimization algorithm Renewable energy Sustainable energy solutions

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Group sparse-based Taylor expansion method for liver pharmacokinetic parameters imaging of dynamic fluorescence molecular tomography

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PHYSICS IN MEDICINE AND BIOLOGY 2024年第11期69卷 115006-115006页

作者： Wu, Yansong He, Xuelei Chen, Zihao Wei, Xiao Liu, Yanqiu Li, Shuangchen Zhang, Heng Yu, Jingjing Yi, Huangjian Guo, Hongbo He, Xiaowei Xian Key Lab Radi & Intelligent Percept Xian Peoples R China Northwest Univ Network & Data Ctr Xian 710127 Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710062 Peoples R China

Objective. Pharmacokinetic parametric images obtained through dynamic fluorescence molecular tomography (DFMT) has ability of capturing dynamic changes in fluorescence concentration, thereby providing three-dimensional metabolic information for applications in biological research and drug development. However, data processing of DFMT is time-consuming, involves a vast amount of data, and the problem itself is ill-posed, which significantly limits the application of pharmacokinetic parametric images reconstruction. In this study, group sparse-based Taylor expansion method is proposed to address these problems. Approach. Firstly, Taylor expansion framework is introduced to reduce time and computational cost. Secondly, group sparsity based on structural prior is introduced to improve reconstruction accuracy. Thirdly, alternating iterative solution based on accelerated gradient descent algorithm is introduced to solve the problem. Main results. Numerical simulation and in vivo experimental results demonstrate that, in comparison to existing methods, the proposed approach significantly enhances reconstruction speed without a degradation of quality, particularly when confronted with background fluorescence interference from other organs. Significance. Our research greatly reduces time and computational cost, providing strong support for real-time monitoring of liver metabolism.

关键词： group sparsity Taylor expansion dynamic fluorescence molecular tomography optimization algorithm

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An Adaptive Learning Rate Deep Learning Optimizer Using Long and Short-Term Gradients Based on G-L Fractional-Order Derivative

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NEURAL PROCESSING LETTERS 2024年第2期56卷 106-106页

作者： Chen, Shuang Zhang, Changlun Mu, Haibing Beijing Univ Civil Engn & Architecture Sch Sci Beijing 100044 Peoples R China Beijing Jiaotong Univ Key Lab Commun & Informat Syst Beijing 100044 Peoples R China

Deep learning model is a multi-layered network structure, and the network parameters that evaluate the final performance of the model must be trained by a deep learning optimizer. In comparison to the mainstream optimizers that utilize integer-order derivatives reflecting only local information, fractional-order derivatives optimizers, which can capture global information, are gradually gaining attention. However, relying solely on the long-term estimated gradients computed from fractional-order derivatives while disregarding the influence of recent gradients on the optimization process can sometimes lead to issues such as local optima and slower optimization speeds. In this paper, we design an adaptive learning rate optimizer called AdaGL based on the Grunwald-Letnikov (G-L) fractional-order derivative. It changes the direction and step size of parameter updating dynamically according to the long-term and short-term gradients information, addressing the problem of falling into local minima or saddle points. To be specific, by utilizing the global memory of fractional-order calculus, we replace the gradient of parameter update with G-L fractional-order approximated gradient, making better use of the long-term curvature information in the past. Furthermore, considering that the recent gradient information often impacts the optimization phase significantly, we propose a step size control coefficient to adjust the learning rate in real-time. To compare the performance of the proposed AdaGL with the current advanced optimizers, we conduct several different deep learning tasks, including image classification on CNNs, node classification and graph classification on GNNs, image generation on GANs, and language modeling on LSTM. Extensive experimental results demonstrate that AdaGL achieves stable and fast convergence, excellent accuracy, and good generalization performance.

关键词： Deep learning optimization algorithm Adaptive learning rate Fractional-order derivative

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Day-ahead optimal scheduling considering thermal and electrical energy management in smart homes with photovoltaic-thermal systems

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APPLIED ENERGY 2024年 374卷

作者： Fiorotti, Rodrigo Fardin, Jussara F. Rocha, Helder R. O. Rua, David Lopes, Joao Abel Pecas IFES Fed Inst Espirito Santo Sao Mateus Brazil UFES Fed Univ Espirito Santo Vitoria Brazil INESC TEC INESC Technol & Sci Porto Portugal Univ Porto FEUP Fac Engn Porto Portugal

The environmental impact on the energy sector has become a significant concern, necessitating the implementation of Home Energy Management Systems (HEMS) to enhance the energy efficiency of buildings, reduce costs and greenhouse gas emissions, and ensure user comfort. This paper presents a novel approach to provide optimal day-ahead energy management plans in smart homes with Photovoltaic/Thermal (PVT) systems, aiming to achieve a balance between energy cost and user comfort. This multi-objective problem employs the Non-dominated Sorting Genetic algorithm III as the optimization algorithm and the Nonlinear Auto-regressive with External Input to forecast the day-ahead meteorological variables, which serve as inputs to predict the PVT electrical and heat production in the thermal resistance model. The HEMS benefits from the time-of-use tariff due to the flexibility provided by the energy storage from a battery bank and a boiler. Furthermore, it performs a load scheduling for 10 controllable loads based on three feature parameters to characterize occupant behavior. A study case analysis revealed a cost reduction of approximately 66% in the solution close to the knee of the Pareto curve (S3 solution). The environmental impact on the energy sector has become a The PVT heat production was sufficient to meet the thermal demand of the showers. The proposed hybrid battery management model effectively eliminated the export of electricity to the grid, reducing consumption during peak periods and the maximum peak demand.

关键词： Demand side management Day-ahead forecasting Battery energy storage system PVT system Occupant behavior modeling optimization algorithm

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Development of advanced AI-based segmentation and prediction method for air entrainment in plunging water jets

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PROGRESS IN NUCLEAR ENERGY 2024年 177卷

作者： Zhou, Wen Miwa, Shuichiro Yamashita, Susumu Okamoto, Koji Univ Tokyo Sch Engn Dept Nucl Engn & Management 7-3-1 HongoBunkyo Ku Tokyo 1138654 Japan Japan Atom Energy Agcy Nucl Sci & Engn Ctr 2-4 Tokai Mura Mito Ibaraki 3191195 Japan

Understanding air entrainment phenomena induced by plunging water jets is critical in the fields of nuclear and hydraulic engineering. Air entrainment is one of the key safety design parameters for nuclear systems. However, most existing studies rely on empirical correlations or curve-fitting models to estimate bubble penetration depth, and no agreed-upon calculation principle exists for varying jet conditions. To address these limitations, this research developed two advanced AI approaches: an improved YOLOv5 for segmenting air entrainment and the NSGA-III-BPNN method for predicting penetration depth. The improved YOLOv5 enables real-time segmentation and extraction of air entrainment motion and dynamics under diverse conditions, demonstrating high scalability and robustness. The penetration depth estimated using the improved YOLOv5 shows greater accuracy compared to conventional empirical correlationsand is more efficient than traditional image post-processing techniques for classifying shape regimes based on dynamic air entrainment patterns. To overcome the limitations of object segmentation, which typically relies on video or image data, the NSGA-III-BPNN method predicts maximum penetration depths with greater accuracy than YOLOv5, offering a more effective prediction model for air entrainment penetration depth. By leveraging advanced AI techniques, the research not only provides valuable segmentation data for refining computational fluid dynamics (CFD) modeling but also paves the way for significant advancements in both nuclear and hydraulic engineering.

关键词： Jet penetration Semantic segmentation Computer vision Neural network optimization algorithm

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