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7th International Hybrid Power Plants and Systems Workshop, HYB 2023

作者： Martinez-Rico, Jon de Argandoña, Ismael Ruiz Zulueta, Ekaitz Armendia, Mikel Fernandez-Gamiz, Unai Automation and Control Unit Tekniker Basque Research and Technology Alliance Eibar Spain Vitoria-Gasteiz Spain Vitoria-Gasteiz Spain

ISBN: (纸本)9781839539091

Hybrid power plants (HyPPs) combining different renewable energy sources with battery energy storage systems (BESSs) present multiple advantages and might become a key part of the energy transition, but the still high price of BESSs and varying market conditions could slow down the investment on them. In this paper, the optimal operation of a HyPP consisting of wind, solar photovoltaic and a BESS operating in the Iberian Electricity Market is analysed under the market scenarios between 2018 and 2022. The study reveals the significant effects of the market conditions regarding the revenues obtained by installing BESSs, but claims the need for new market mechanisms to ensure their feasibility. Moreover, a closer market study indicates that price difference rates between markets have not varied significantly, resulting in similar optimal operation strategies in the studied years. © 2023 IET Conference Proceedings. All rights reserved.

关键词： Investments

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Optimal Dispatch of Cogeneration with Thermal and Battery Energy Storage for BEMS Subject to Load Demand Uncertainty 19

Optimal Dispatch of Cogeneration with Thermal and Battery En...

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19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Trairat, Parichada Banjerdpongchai, David Chulalonkorn University Intelligent Control Automation of Process Systems Research Unit Dept of Electrical Engineering Bangkok10330 Thailand

ISBN: (纸本)9781665485845

This paper presents optimal dispatch of cogeneration with thermal energy storage (TES) and battery energy storage (BES) for Building Energy Management System (BEMS) subject to load demand uncertainty. The energy supply of BEMS consists of combined heat and power (CHP), an auxiliary boiler, an absorption chiller, battery energy storage (BES), spinning reserve (SR) and power grids. Previous research related to CHP for BEMS provides optimal operation to minimize total operating cost (TOC), but their operation has only CHP and TES for supply the electrical energy. In this paper, we propose to add BES and SR which aims to supply the electrical energy in the building subject to load demand uncertainty. The load demand uncertainty arises from the difference between the forecasted load demand and the actual load demand. We apply artificial neural network (ANN) to analyze the load demand uncertainty. BES and SR are utilized to tackle the load uncertainty. Then, we design economic optimal operationwith TOC as the objective function. We apply the proposed BEMS with a large shopping mall to demonstrate improvements in energy efficiency. The numerical results manifest that the proposed dispatch can reduce 12.52% of TOC compared to the previous BEMS without BES and SR. © 2022 IEEE.

关键词： Uncertainty analysis

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Smart Factory Hub – Towards a Data Mesh in Smart Manufacturing

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Procedia Computer Science 2024年 232卷 2709-2719页

作者： Roberto González-Velázquez Izaskun Fernández Rubén Ferreira Carlos Carballo Álvaro García Bruno Santamaría Diego González Tekniker Basque Research and Technology Alliance (BRTA) Intelligent Information Systems Unit Iñaki Goenaga 5 Eibar 20600 Spain Tekniker Basque Research and Technology Alliance (BRTA) Automation and Control Unit Iñaki Goenaga 5 Eibar 20600 Spain

The transition of a company towards a data-oriented organisation is a process where several key aspects must be considered. Available data must be readable and interpretable so that useful information can be extracted from it, but also data governance must be implemented with the use of an appropriate information management approach. Handling data at scale is important in this system, as well as ensuring its quality, but also agility and ease of management when new data functionalities have to be developed. New paradigms in the design of analytical architectures have emerged in recent times to respond to these challenges, as is the case of the data mesh concept. This work describes the approach followed in the design of a data information system at Tekniker research Centre called "Smart Factory Hub" using data mesh principles in the context of a smart manufacturing environment. Decentralisation by use cases, the orientation to data products, or the use of a semantic data catalogue for data discoverability, are some of the foundations used to build this platform to allow the coordination of different teams to create faster insights and value for the organisation.

关键词： Data Mesh Big Data Data Platform Smart Manufacturing

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Molecular Imprinted Polymer Based Optical Sensor for Methanol Vapor Detection

Molecular Imprinted Polymer Based Optical Sensor for Methano...

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Optica Sensing Congress 2023, AIS, FTS, HISE, Sensors, ES 2023 - Part of Optical Sensors and Sensing Congress 2023

作者： Leelasirinuwong, Mintra Denkasineecharoen, Phebe Yungyuentrrakoon, Pin Jitpratak, Pannathorn Kongsawang, Nuntaporn Pathak, Akhilesh Kumar Viphavakit, Charusluk Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Biomedical Engineering Program Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Intelligent Control Automation of Process Systems Research Unit Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

A molecular imprinted polymer-based optical sensor is proposed to detect methanol. The sensor is tested with different concentrations of methanal vapour for sensitivity and ethanol for selectivity. The result shows a ... 详细信息

ISBN: (纸本)9781957171241

关键词： Optical sensors

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Dynamic Accuracy Optimization for NC controlled Industrial Robots

Dynamic Accuracy Optimization for NC controlled Industrial R...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Asier Mandiola Mikel Armendia Diego Gonzalez Jon Eguskiza Automation and Control Unit TEKNIKER Basque Research and Technology Alliance (BRTA) Eibar Spain Mechanical Engineering Unit TEKNIKER Basque Research and Technology Alliance (BRTA) Eibar Spain

In the last years the usage of industrial robotic manipulators is being extended to new applications, like laser or machining-based manufacturing. These applications require new user interface functionalities coming from common NC control systems, as well as increased demands in path tracking, dynamics, and accuracy. In this paper, a Dynamic Accuracy Optimization strategy is applied on a NC controlled industrial manipulator to minimize robot Tool Center Point errors during path execution. A state-of-the-art laser tracker system is used for online characterization of the TCP position which is fed to the compensation algorithm integrated in the NC controller. A drastic reduction of static and dynamic errors has been observed in both linear and orientation degrees of freedom, impacting also in the resulting path errors.

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A K-Means Clustering-Based Optimization of IT2FL for Improved Regenerative Braking Performance 1

A K-Means Clustering-Based Optimization of IT2FL for Improve...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Wahid, Muhammad Rizalul Joelianto, Endra Budiman, Bentang Arief Tongroon, Manida Pratama, Mulia Gianti, Mahmudah Salwa Hernadi, Muhammad Fauzan Mechatronics and Artificial Intelligence Universitas Pendidikan Indonesia Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung Faculty of Mechanical and Aerospace Engineering Bandung Indonesia Kasetsart University Atae Research Unit Department of Mechanical Engineering Sriracha Thailand Pusat Riset Mekatronika Cerdas Organisasi Riset Elektronika dan Informatika-BRIN Bandung Indonesia

ISBN: (纸本)9798350368918

Optimizing regenerative braking systems in electric vehicles presents a complex challenge requiring precise and adaptable control. This research proposes employing K-Means Clustering to the Interval Type-2 Fuzzy Logic (IT2FL) membership functions, thereby improving overall regenerative braking efficiency and enhancing driving range. This research demonstrates that implementing a K-Means in the IT2FL control system in electric vehicles effectively recovers 35.84% of energy. By incorporating a K-means-based approach, the IT2FL controller further enhances vehicle range by 43 kilometers, achieving a total range of 108 kilometers. In comparison, an IT2FL system without K-means recovered only 26.67% of energy and achieved a total range of 93 kilometers. Notably, the IT2FL controller with K-means enabled the electric vehicle to attain an energy efficiency of 22 kilometers per kilowatt-hour. These findings underscore the significant improvement in maximum range offered by the IT2FL controller with K-means compared to alternative methods. © 2024 IEEE.

关键词： Regenerative braking

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Model predictive control of microgrid energy management system with optimal power flow constraints using second order cone programming 18

Model predictive control of microgrid energy management syst...

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18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Boonpeng, Jirapong Maneeratpongsuk, Jitpanu Banjerdpongchai, David Chulalonkom University Intelligent Control Automation of Process Systems Research Unit Dept. of Electrical Engineering Faculty of Engineering Bangkok10330 Thailand

ISBN: (纸本)9780738111278

Energy management system (EMS) is important for the planning and operation of power system as the power generation must supply enough energy for load with the minimum operating cost. This paper presents nonlinear MPC to dispatch power generation within the limitation of the power system. The objectives are to improve EMS by considering AC optimal power flow and provide a dispatch strategy to reduce total operating cost (TOC). To realize the proposed nonlinear MPC, we consider the power plant model of Mae Hong Son in the northern part of Thailand. Load data and power generation profile are utilized as the inputs to MPC of EMS to minimize TOC for one day ahead. In this work, the optimal power flow is incorporated as the second order cone constraints. It is shown that EMS with optimal power flow can operate within the rated voltage for all buses. When comparing the results in normal mode with one-shot optimization, the proposed MPC can reduce the TOC by 1.72% in the rainy season. © 2021 IEEE.

关键词： Second-order cone programming

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Techno-economic effects of electricity market conditions in the optimal operation of hybrid power plants

Techno-economic effects of electricity market conditions in ...

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7th International Hybrid Power Plants & Systems Workshop (HYB 2023)

作者： J. Martinez-Rico I. Ruiza de Argandoña E. Zulueta M. Armendia U. Fernandez-Gamiz Automation and Control Unit Tekniker Basque Research and Technology Alliance Eibar Spain System Engineering and Automation Control Department University of the Basque Country (UPV/EHU) Vitoria-Gasteiz Spain Energy Engineering Department University of the Basque Country (UPV/EHU) Vitoria-Gasteiz Spain

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Analysis of Equilibrium Point and Genetic Algorithm Design of Observer-Based PID controller for Membrane Bioreactor

Analysis of Equilibrium Point and Genetic Algorithm Design o...

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2021 International Automatic control Conference, CACS 2021

作者： Changsila, Yanita Banjerdpongchai, David Intelligent Control Automation of Process Systems Research Unit Dept. of Electrical Engineering Faculty of Engineering Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok10330 Thailand

ISBN: (纸本)9781665444125

The operation of most wastewater treatment systems is an open-loop system. There is limited feedback control of membrane bioreactors (MBR) which makes it impossible to regulate the quality of the treated water. The objectives of this paper are twofold. First, we analyze the equilibrium point of the MBR nonlinear model. Second, we utilize output feedback control with state observer and design optimal PID controllers. We determine the optimum equilibrium point and derive a linearized model of MBR. Then, we design the state observer to estimate the non-measurable state using the pole placement method. Both the measurable output and estimated state are fed back through PID controller. The objectives of PID control consist of stabilization and performance improvement. Genetic Algorithm is applied to find the optimal PID parameters that minimize the integral of absolute error and satisfy the specified time-domain criteria. We apply the observer-based PID controller to the linearized model of MBR. Numerical results demonstrate the design of PID controller and the improvement of the closed-loop response and quality of the treated water. © 2021 IEEE

关键词： Bioreactors

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Convolutional Neural Networks for Predicting Energy Efficiency of Air Conditioning Systems 17

Convolutional Neural Networks for Predicting Energy Efficien...

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17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020

作者： Kittisarakul, Supitchaya Banjerdpongchai, David Chulalonkorn University Intelligent Control Automation of Process Systems Research Unit Bangkok10330 Thailand

ISBN: (数字)9781728164861

ISBN: (纸本)9781728164861

This paper presents a prediction of the energy efficiency of air conditioning system using convolutional neural network (CNN). Coefficient of Performance (COP) is used as a measure of energy efficiency. In previous work, the study employed autoregressive with exogenous inputs (ARX) as a model for COP prediction. In this paper, the obtained data are preprocessed, and COP is calculated and used in the training process. We employ CNN to build two sub-models to separately predict temperature difference and power consumption using the past 30-minute data of room temperature, ambient temperature, and power consumption. The models are tested with historical data and compute the root mean square error (RMSE) as a prediction indicator. We compare the proposed CNN with ARX and Artificial Neural Network (ANN). The results show that CNN yields RMSE significantly less than that of ARX and ANN. However, the prediction from CNN seems to have lagging behavior in some seasons which might be due to the fluctuation of seasonal weathers. © 2020 IEEE.

关键词： Energy efficiency

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