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SSRN 2024年

作者： Sun, Zhenglong Long, Hao Li, Zewei Gao, Bo Wei, Xiaoqiang Belikov, Juri Levron, Yoash Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy State Grid Jiashan County Power Supply Company Jiaxing China State Grid Heilongjiang Electric Power Company Limited Heilongjiang China The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion Israel Institute of Technology Haifa Israel Department of Software Science Tallinn University of Technology Tallinn Estonia Northeast Electric Power University China

The increasing interconnectivity of power systems and the integration of renewable energy sources into the grid have exacerbated the low-frequency oscillation problem, which directly impacts the stability of the power grid. The performance of wide-area damping control (WADC), which is an effective oscillation suppression technique , is susceptible to false data injection attacks (FDIA). This study proposes a non-intrusive data verification method using an adaptive variational autoencoder (AVAE) combined with a deep convolutional neural network (DCNN) to enhance WADC’s resistance to FDIA. The method integrates data dimensionality reduction of AVAE and reconstruction capabilities with DCNN’s feature extraction advantages. Itemploys adaptive thresholds to dynamically track grid data fluctuations, accurately distinguishing between normal and abnormal measurements and improving attack detection and performance discrimination. In addition, the study proposes an intelligent repair process based on latent representations for anomalous data in the repair module, using hierarchical clustering and Gaussian mixture models post-Bayesian inference to repair data. AVAE then reconstructs the repaired data, mitigating attack impacts. Simulations on IEEE 39-Bus, 68-Bus, and 118-bus systems demonstrate that this method effectively detects network attacks and restores manipulated signals with minimal reconstruction errors, thus ensuring grid stability and reliability. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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An Adaptive Peridynamics with Correspondence Material Model for Coupled Creep-Plastic Fracture Problems

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SSRN 2024年

作者： Zhang, Hanbo Li, Chengxuan Liu, Zhenhai Ye, Hongfei Li, Hui Zheng, Yonggang State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics School of Mechanics and Aerospace Engineering Dalian University of Technology Dalian116024 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610200 China

This paper introduces an adaptive peridynamics with a correspondence material model (A-PD-CMM) to effectively simulate the coupled creep-plastic behavior and the creep crack propagation in metal materials. In this method, to describe the coupled creep-plastic deformation with considering the creep damage, the multiaxial coupled constitutive model that integrates the Wen-Tu creep damage model and the J2 plastic model is developed. Moreover, a damage associate criterion is incorporated into the peridynamics framework to effectively capture the influence of damage on the force state. To detail the rapid damage evolution during the tertiary creep stage, a time-adaptive solving strategy is proposed. Additionally, the constitutive integration algorithm of the multiaxial coupled model is efficiently realized by decoupling it into two parts, that is, an implicit computation of creep-plasticity and an explicit evolution of creep damage. The efficacy and engineering applicability of the proposed A-PD-CMM are validated through several representative numerical examples, showcasing its potential in addressing complex material behavior. © 2024, The Authors. All rights reserved.

关键词： Crack propagation

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Coupled Axisymmetric Peridynamics with Stabilized Correspondence Material Model for Ablation and Fracture Problems

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SSRN 2024年

作者： Zhang, Hanbo Liu, Zhenhai Li, Chengxuan Ye, Hongfei Zhang, Hongwu Li, Hui Zheng, Yonggang State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics School of Mechanics and Aerospace Engineering Dalian University of Technology Dalian116024 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610200 China

A coupled axisymmetric peridynamics with a stabilized correspondence material model (CA-PD-SCMM) is proposed in this work to effectively simulate the ablation and ductile fracture behaviors of metals under high temperatures. To quantify the damage resulting from compression and shearing deformations, a strain energy density decomposition method is incorporated into the ductile damage model. Furthermore, a novel axisymmetric stabilization method based on peridynamics linearization theory is introduced to mitigate numerical oscillations arising from zero-energy modes in both thermal and mechanical scenarios. To capture the varying geometries and update the boundary conditions during ablation, a moving boundary model is developed based on temperature-associated criteria. To validate the capacity of the proposed CA-PD-SCMM, several representative numerical experiments are conducted. These examples not only affirm its ability to stabilize numerical oscillations in coupled axisymmetric thermoplastic problems but also demonstrate its capability for accurately simulating ablation and predicting crack propagation. © 2024, The Authors. All rights reserved.

关键词： Ductile fracture

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Artificial Hummingbird Algorithm For Optimal Reconfiguration Of Electrical Distribution networks

Artificial Hummingbird Algorithm For Optimal Reconfiguration...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Younes Zahraoui Anes Bouhanik Tarmo Korõtko Argo Rosin Saad Mekhilef FinEst Centre for Smart Cities Tallinn University of Technology Tallinn Estonia Electrical Engineering Departement University of Biskra Biskra Algeria Biskra Algeria Departement of Electrical Power Enginnering and Mechatronics Tallinn University of Technology Tallinn Estonia School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

This paper suggests using the Artificial Hummingbird Algorithm (AHA), a recently developed optimization technique, to solve the problem of finding the optimal distribution network reconfiguration (ODNR) to minimize power losses while meeting equality and inequality constraints. The suggested algorithm has been successfully tested on a 69-bus standard radial distribution network. The achieved results demonstrate the clear effectiveness and superiority of the AHA algorithm in comparison to various techniques documented in current scholarly publications.

关键词： Navigation Metaheuristics Optimization methods Distribution networks Minimization Distributed power generation Complexity theory

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Day-ahead PV Output power Forecasting Utilizing Boosting Recursive LightGBM-LSTM Framework

Day-ahead PV Output Power Forecasting Utilizing Boosting Rec...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Hossein Nourollahi Hokmabad Oleksandr Husev Dmitri Vinnikov Juri Belikov Eduard Petlenkov Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia

Recently, global photovoltaic (PV) system installations have surged. Precise forecasting is vital for their grid integration and carbon emission cuts. However, due to fluctuating solar radiation, predicting PV output is difficult. Machine learning models, notably Long Short-Term Memory (LSTM) networks, offer a solution. This study presents a novel framework using a boosted recursive Light Gradient Boosting Machine (LightGBM)-LSTM network to forecast daily PV output at hourly intervals. Conducted in Northern Europe—a region with significant solar radiation variability—the models trained on meteorological and historical data showed a 12% improvement in RMSE, a 13% reduction in MAPE, and a 5% increase in the R 2 score compared to standalone LSTM models.

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Energy Storage and Forecasting Error Impact Analysis In Photovoltaic Equipped Residential Nano-Grids

Energy Storage and Forecasting Error Impact Analysis In Phot...

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Compatibility in power Electronics (CPE)

作者： Hossein Nourollahi Hokmabad Oleksandr Husev Juri Belikov Dmitri Vinnikov Eduard Petlenkov Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia

Battery energy storage systems (BESS) enable many applications for photovoltaic (PV) equipped nano-grids. Stored excessive energy is utilized for energy arbitrage, demand response during blackouts, and peak shaving. This technology helped utility service providers deal with the duck-curve-effect and intermittency of renewable energy systems. In this paper, we investigate the benefits of using energy storage systems in PV nano-grids for residential sectors and evaluate the relationship between battery presence and system performance. We also analyze the effects of battery degradation on energy independence and self-sufficiency and investigate the relationship between simple payback time and state of health (SoH) levels of nano-grid components. Additionally, we propose a simple rule-based energy supervision strategy and evaluate its performance under various forecasting error levels, highlighting the importance of high-performance forecasting methods for proper energy management systems. Our contributions will help select and optimize PV-battery solutions and energy management algorithms to achieve the highest benefit and minimize investment risks.

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Application of Artificial Intelligence technology in power System Stability Assessment 3

Application of Artificial Intelligence Technology in Power S...

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3rd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Xiang, Siyu Zhen, Cao Peng, Jian Zhang, Linghao Pu, Zhengguo State Grid Sichuan Electric Power Research Institute Sichuan Chengdu610095 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611700 China College of Computer Science Sichuan University Chengdu610065 China Anhui Jiyuan Software Co. Ltd. Hefei230088 China

In order to realize power system stability assessment, the author studies a deep Convolutional neural network algorithm for power system operation stability assessment. Combining short-term simulation with neural network prediction to reduce the time required for transient stability analysis can be used in various simulation analysis scenarios. A deep Convolutional neural network algorithm is used to improve the performance of the security system. A deep Convolutional Neural Network (CNN) is used to construct a multi-layer and multi-column neural network to evaluate the stability of electrical networks. The system significantly improves the efficiency of evaluating specific faults, particularly enhancing the specificity of evaluation judgments, and reducing the additional maintenance workload caused by providing stability warnings for the operation status of faultless power grids. The experimental results indicate that, the system has shown certain advantages in terms of sensitivity to the judgment of various types of faults mentioned above, especially improving the sensitivity to other faults with unknown causes, which is 10.8 percentage points higher than the previous system. The smallest difference in the improvement of sensitivity indicators is the sensitivity to ground faults. The previous system reached 95.4%, while this system increased by 3.9 percentage points to 99.3%. It is proved that the deep CNN is applicable to the task of power system operation stability assessment. © 2023 The Authors. Published by Elsevier B.V.

关键词： Convolution

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Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

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Computers, Materials & Continua 2023年第10期77卷 1281-1301页

作者： Jigna Patel Anand Ruparelia Sudeep Tanwar Fayez Alqahtani Amr Tolba Ravi Sharma Maria Simona Raboaca Bogdan Constantin Neagu Department of Computer Science and Engineering Institute of TechnologyNirma UniversityAhmedabad382481India Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Centre for Inter-Disciplinary Research and Innovation University of Petroleum and Energy StudiesDehradun248001India Doctoral School University Politehnica of BucharestBucharest060042Romania National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Rm ValceaRamnicu Valcea240050Romania Power Engineering Department Gheorghe Asachi Technical University of IasiIasi700050Romania

The overgrowth of weeds growing along with the primary crop in the fields reduces crop *** solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used *** application of herbicide is effective but causes environmental and health ***,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary *** by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this *** key objective of this study is to detect plant and non-plant(weed)parts from crop *** the help of object detection,the precise location of weeds from images can be *** dataset is collected manually from a private farm in Gandhinagar,Gujarat,*** combined approach of classification and object detection is applied in the proposed *** Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object *** have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed *** outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

关键词： Precision Agriculture Deep Learning brinjal weed detection ResNet-18 YOLOv3 CenterNet Faster RCNN

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power load prediction of smart grid based on deep learning 3

Power load prediction of smart grid based on deep learning

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3rd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

Real electricity costs, weather, and historical load data are added as new reference data when analyzing the characteristics of the load change policy to improve the forecasting model. A load forecasting model for smart grid based on convolutional neural network(CNN) is proposed. The model first analyzes the characteristics of the variable model and determines the forecast model inputs using two methods of correlation analysis to understand the impact of actual energy costs, weather and other factors on the changes. Second, since Feedforward neural network is deficient in processing the correlation information between loads, the prediction model developed by the author was studied as CNN. After completing the training, input two sets of real datasets for final data analysis and comparison, and the results show that, LSTM, BP, SVR[UNK]MAPE[UNK]RMSE respectively 0.49%, 0.57%, 0.83%, 1.44% and 106.08 MW, 113.82MW, 169.77 MW, 285.61 MW. It can be seen that the prediction model proposed by the author has the highest accuracy and the best fit with the trend of actual load changes. It is proved that the CNN model has certain advantages when dealing with load forecasting problems related to time series. © 2023 Elsevier B.V.. All rights reserved.

关键词： Feedforward neural networks

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Feasibility Study of a DC House Connected to a Conventional AC Distribution Network

Feasibility Study of a DC House Connected to a Conventional ...

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Compatibility in power Electronics (CPE)

作者： Hossein Nourollahi Hokmabad Noman Shabir Victor Astapov Eduard Petlenkov Oleksandr Husev Juri Belikov Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia FinEST Centre for Smart Cities Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9798350318265

ISBN: (纸本)9798350318272

Leveraging advancements in power electronics, the adoption of Direct Current (dc) technology in net-Zero Energy Buildings (nZEBs) is seen as a promising approach to boost energy efficiency. Emerging dc technology aims to reduce power losses by eliminating unnecessary conversions between dc and Alternating Current (ac). This paper thoroughly assesses the effectiveness of dc and hybrid dc (partial dc topologies in comparison to conventional ac nZEBs. The hybrid solution integrates ac and dc networks and loads within the building, while the dc solution benefits from a purely dc internal electricity distribution network and exclusively dc loads. The study involves analyzing annual load profile data from 16 neighboring houses in Estonia, simulating their transition to nZEBs with dc, hybrid dc, and pure dctopologies. The study examines power conversion losses, the operational periods of power converters in relation to their maximum power capacity, and energy exchanges with the utility grid. Additionally, it explores the potential for creating energy communities based on the consumption patterns of these houses. The results indicate that pure dc nZEBs might not be as efficient as initially thought, especially when renewable resources are limited.

关键词： Measurement Renewable energy sources power engineering Network topology Net zero Buildings Hybrid power systems

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