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Sustainable Communication Networks and Application (ICSCNA), International conference on

作者： Chinthagumpala Prasanna Kalagotla Chenchireddy Nimmala Gayathri Pathkula Jayanth Penchikala Kranthi Kumar Vangala Praveen Kumar Dept of EEE Teegala Krishna Reddy Engineering College Hyderbad India

The invention and implementation of Maximum Power Point Tracking control algorithms for a Permanent Magnet Synchronous Generator-based Wind Energy Conversion System are the main objectives of this work. By precisely tracking the greatest power point under a range of wind conditions, the goal is to increase the effectiveness and energy capture of the PMSG-driven wind turbine. modeling, simulation, and experimental validation will be used to investigate and improve various MPPT algorithms and control tactics. This work improves the development of renewable energy systems, specifically in the context of wind power generation, with possible applications in the generation of clean and sustainable power.

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Multi-physical Field simulation and Parameter Inversion Method Based on Digital Twin Model 4

Multi-physical Field Simulation and Parameter Inversion Meth...

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4th International Academic Exchange conference on Science and Technology Innovation, IAECST 2022

作者： Qu, Beibei Yang, Chengpeng Shen, Wendong Li, Fei Wu, Junyun Information and Communication Branch of State Grid of Shanxi Electric Power Company Taiyuan030021 China Information and Communication Branch of State Grid of Jincheng Electric Power Company Shanxi Jincheng048000 China

ISBN: (纸本)9798350320008

As an important power equipment in the power grid, the high-voltage cable faces the hot spot temperature problem caused by excessive load fluctuation, long-term operation, and other factors. The overheating of high voltage cable not only accelerates the aging of insulation, but also causes a lot of economic losses due to serious inter-turn short circuits. The heat generated in long-term operation will also cause deformation or even burning damage to the cable core. In this paper, a digital twin of high-voltage cable based on the driving model and data-driven is proposed, which simulates the dynamic operation process of time and space with the traditional modeling, and depicts the overall structure of high-voltage cable digital space. The high-voltage cable digital twin platform is established through the multi-field coupling model and the temperature prediction model to realize the real-time multi-physical field distribution of the high-voltage cable and calculate the thermal aging situation according to the hot spot temperature, thus completing the construction of the high-voltage cable digital twin. Experiments indicate that the error between the multi-physical field simulation based on the digital twin model and the test value is very small, which meets the needs of the project, has a stable prediction ability, improves the calculation accuracy and fitting degree, and proves the feasibility of the method used. © 2022 IEEE.

关键词： Electric power distribution

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optimization of Window Positions for Wind-driven Natural Ventilation Performance

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ENERGIES 2020年第10期13卷

作者： Yoon, Nari Piette, Mary Ann Han, Jung Min Wu, Wentao Malkawi, Ali Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Harvard Univ Grad Sch Design Cambridge MA 02138 USA Tennessee State Univ Coll Engn Dept Civil & Architectural Engn Nashville TN 37209 USA

This paper optimizes opening positions on building facades to maximize the natural ventilation's potential for ventilation and cooling purposes. The paper demonstrates how to apply computational fluid dynamics (CFD) simulation results to architectural design processes, and how the CFD-driven decisions impact ventilation and cooling: (1) background: A CFD helps predict the natural ventilation's potential, the integration of CFD results into design decision-making has not been actively practiced;(2) methods: Pressure data on building facades were obtained from CFD simulations and mapped into the 3D modeling environment, which were then used to identify optimal positions of two openings of a zone. The effect of the selected opening positions was validated with building energy simulations;(3) results: The cross-comparison study of different window positions based on different geographical locations quantified the impact on natural ventilation effectiveness;and (4) conclusions: The optimized window position was shown to be effective, and some optimal solutions contradicted the typical cross-ventilation strategy.

关键词： natural ventilation window positions optimization early-design phase building simulation CFD

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Combining Data-driven and Physics-based Process Models for Hybrid Model Predictive Control of Building Energy Systems

Combining Data-driven and Physics-based Process Models for H...

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Mediterranean conference on Control and Automation (MED)

作者： Phillip Stoffel Charlotte Lö ffler Steffen Eser Alexander Kü mpel Dirk Mü ller Institute for Energy Efficient Buildings and Indoor Climate E.ON Energy Research Center RWTH Aachen University Aachen Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Model predictive control is well suited to control building energy systems efficiently. However, it still lacks commercial relevance due to the high modeling effort. This article presents a methodology to reduce the modeling effort by combining data-driven and physics-based process models in a hybrid MPC scheme. Data-driven models like artificial neural networks are generally nonconvex and nonlinear. Thus, using such models results in a nonlinear, nonconvex optimization problem. We present a workflow to efficiently solve the resulting optimization problem with gradient-based solvers using the algorithmic differentiation tool CasADi. The developed workflow is applied to an exemplary building energy system to implement an economic, hybrid model predictive controller. simulation results confirm the high potential of the proposed methodology by realizing a cost-effective operation of the controlled system.

关键词： Economics Automation simulation Buildings Artificial neural networks Predictive models Control systems

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Combining Retrospective Approximation with Importance Sampling for Optimising Conditional Value at Risk

Combining Retrospective Approximation with Importance Sampli...

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simulation Winter conference

作者： Anand Deo Karthyek Murthy Tirtho Sarker Singapore University of Technology and Design SINGAPORE

ISBN: (纸本)9781665476621

This paper investigates the use of retrospective approximation solution paradigm in solving risk-averse optimization problems effectively via importance sampling (IS). While IS serves as a prominent means for tackling the large sample requirements in estimating tail risk measures such as Conditional Value at Risk (CVaR), its use in optimization problems driven by CVaR is complicated by the need to tailor the IS change of measure differently to different optimization iterates and the circularity which arises as a consequence. The proposed algorithm overcomes these challenges by employing a univariate IS transformation offering uniform variance reduction in a retrospective approximation procedure well-suited for tuning the IS parameter choice. The resulting simulation based approximation scheme enjoys both the computational efficiency bestowed by retrospective approximation and logarithmically efficient variance reduction offered by importance sampling.

关键词： Monte Carlo methods Computational modeling Tail Approximation algorithms Computational efficiency optimization Tuning

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A Data-driven Fault Classification Method for Microgrids

A Data-driven Fault Classification Method for Microgrids

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2022 IEEE International conference on High Voltage Engineering and Applications, ICHVE 2022

作者： Wang, Jingsong Li, Yongfu Luo, Xiaoxiao Zhang, Han Sima, Wenxia Yang, Ming State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing China State Grid Chongqing Electric Power Research Institute Chongqing401123 China

ISBN: (纸本)9781665407502

This paper proposes a method that combines wavelet decomposition and deep learning for fault classification in microgrids (MGs). Random Search optimization is used to find the best wavelet function for matching traces to extract deep features of data. And these extracted deep features are used as complementary inputs to a deep learning model. One-dimension convolutional neural network (1-D CNN) is used to complete the secondary conversion of data features and high-dimensional feature mapping. The data are transformed by dimensionality and bidirectional long and short-term memory network. The data information mining is completed by using the multi-branch structure of the network to filter and merge the multidimensional effective information learned from each network branch. We also compare and analyze five different classification techniques (i.e., k-nearest neighbor(KNN), decision tree(DT), support vector machine(SVM), random forest(RF), and XGBoost(XGB)) and compare their performance statistically. After modeling the MGs system in the simulation software, the Consortium for Electrical Reliability Technology Solutions (CERTS) MG effectively illustrates the validity of our proposed method. © 2022 IEEE.

关键词： Convolutional neural networks

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Hybrid integrated modeling of thermal field temperature in Cz silicon single crystal growth process based on VMD-CNN-LSTM-ELM 40

Hybrid integrated modeling of thermal field temperature in C...

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40th Chinese Control conference (CCC)

作者： Wan, Yin Liu, Ding Ren, Junchao Xian Univ Technol Natl & Local Engn Res Ctr Crystal Growth Equipmen Xian 710048 Peoples R China Xian Univ Technol Shaanxi Key Lab Complex Syst Control & Intelligen Xian 710048 Peoples R China

ISBN: (纸本)9789881563804

Czochralski (Cz) silicon single crystal growth is a dynamic time-varying process with complex physical changes, multi-phase coupling of multiple fields, model uncertainty and large hysteresis and nonlinearity, while mechanistic models based on a large number of assumptions are difficult to be applied practically. In order to establish a stable and reliable model of crystal growth process, this paper proposes a variational modal decomposition- -convolutional neural network-long and short-term memory network-extreme learning machine (VMD-CNN-LSTM-ELM) nonlinear hybrid integrated modeling method based on the idea of data-driven modeling to construct a model between heater power and thermal field temperature. The model consists of feature extraction, deep learning network, and optimization algorithm. First, VMD is used to decompose the original data into multiple Intrinsic Mode Function (IMF) from low to high frequencies, preserving the variation characteristics of the signal at different frequencies. Second, for each eigenmode function sequence, CNN feature extraction and LSTM prediction are performed in turn. Then, ELM is used to nonlinearly integrate the prediction results of multiple sequences to obtain the final prediction output. Finally, the simulation experimental results based on industrial data show that the proposed VMD-LSTM-ELM based thermal field temperature has excellent prediction performance and generalization ability.

关键词： Cz silicon single crystal growth Data-driven modeling Thermal field temperature Hybrid integrated modeling

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Future IoT sensing: Silvaco simulation of graphene-gated p-i-n photosensor on SOI for blue and ultraviolet light

Future IoT sensing: Silvaco simulation of graphene-gated p-i...

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Information Technology (ACIT)

作者： Shonak Bansal Arnav Bansal Mohammad Aljaidi Krishna Prakash Department of Electronics and Communication Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering NRI Institute of Technology Vijayawada Andhra Pradesh India

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The Internet of Things (IoT) revolutionizes how devices interact and communicate, creating a vast network of interconnected systems with diverse applications, including healthcare, environmental monitoring, and smart cities. A critical component of these IoT systems is the ability to detect and respond to various environmental stimuli, such as light. In this context, photosensors play a pivotal role, particularly in detecting blue and ultraviolet (UV) light, essential for applications like UV index monitoring, flame detection, and secure communications. This paper proposes a lateral p-i-n photosensor design incorporating a single-layer graphene gate, built using silicon-on-insulator (SOI) technology. The device aims to enhance the detection of blue and ultraviolet light. The sensor achieves a larger depletion region with minimal gate bias voltage by employing a graphene gate, leading to superior quantum efficiency. The photosensor exhibits a photocurrent-to-dark current ratio of 1.6×10 5 , a rise time of 2.2 ns, a fall time of 2.0 ns, and an external quantum efficiency of 51.66%. Leveraging graphene's unique optical and electrical characteristics, this gated structure outperforms conventional alternatives. Its compatibility with modern integrated circuit fabrication techniques makes it a promising candidate for future IoT system integration. simulations conducted using the Silvaco ATLAS platform, a widely used simulation software for semiconductor device modeling and optimization, provide critical insights into the device's operation. The results demonstrate the potential for superior sensitivity and efficiency in IoT-driven blue and UV light detection.

关键词： PIN photodiodes Smart cities Graphene Silicon-on-insulator Voltage Logic gates Software Optical sensors Integrated circuit modeling optimization

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Artificial Intelligence Approach In Predicting Biomass-to-Biofuels Conversion Performances 23

Artificial Intelligence Approach In Predicting Biomass-to-Bi...

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23rd International Carpathian Control conference (ICCC)

作者： Mateescu, Carmen Tudor, Emil Dima, Andreea-Daniela Chirita, Ionel Tanasiev, Vladimir Prisecaru, Tudor Natl Inst R&D Elect Engn ICPE CA Dept Environm Energy & Climate Change Bucharest Romania Natl Inst R&D Elect Engn ICPE CA Dept Renewable Sources & Energy Efficiency Bucharest Romania Univ Politehn Bucuresti Dept Chem & Biochem Engn Bucharest Romania Natl Inst R&D Elect Engn ICPE CA Elect Engn Equipments Div Bucharest Romania Univ Politehn Bucuresti Dept Energy Prod & Use Bucharest Romania Univ Politehn Bucuresti Dept Thermodynam Engines Thermal & Cooling Equip Bucharest Romania

ISBN: (纸本)9781665466363

Energy and environmental biotechnologies have developed rapidly in recent decades, driven by societal challenges such as climate change, energy security and pollution control. The optimization of biotechnological processes is an essential issue for increasing the efficiency of bioconversion performances and ensuring product quality. The use of artificial intelligence and computer technology in conjunction with conventional simulation and modeling techniques may bring major benefits in defining optimal process parameters and reducing the overall process cost. This paper aims to provide insight on the potential applications of artificial intelligence in biomass-to-biofuels conversion processes, with a focus on predicting the production of biofuels by biochemical processes, given the high availability of wet bio-based resources. Appropriate artificial intelligence techniques are identified taking into account specific input and output variables for transesterification, alcoholic fermentation, anaerobic digestion and dark fermentation processes. A particular application for the computerized prediction of biomethane production generated by a mixture of selected organic waste was investigated by the authors using a two-factor central composite design methodology and STATISTICA 10 software. The predicted results were validated by performing a simultaneous lab-scale fermentation experiment that confirmed a high level of reliability in the use of computer technology for this specific application.

关键词： artificial intelligence biofuels optimization biomethane

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simulation-optimization of digital twin 21

Simulation-optimization of digital twin

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Proceedings of the Winter simulation conference

作者： Mohammad Dehghanimohammadabadi Sahil Belsare Renee Thiesing Northeastern University Simio LLC

With rapid advancements in Cyber-Physical manufacturing, the Internet of Things, simulation software, and Machine Learning algorithms, the applicability of Industry 4.0 is gaining momentum. The demand for real-time decision-making in the manufacturing industry has given significant attention to the field of Digital Twin (DT). The whole idea revolves around creating a digital counterpart of the physical system based on enterprise data to exploit the effects of numerous parameters and make informed decisions. Based on that, this paper proposes a simulation-optimization framework for the DT model of a Beverage Manufacturing Plant. A data-driven simulation model developed in Simio is integrated with Python to perform Multi-Objective optimization. The framework explores optimal solutions by simulating multiple scenarios by altering the availability of operators and dispatching/scheduling rules. The results show that simulation optimization can be integrated into the Digital-Twin models as part of real-time production planning and scheduling.

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