scholarly journals On the Importance of Characterizing Virtual PMUs for Hardware-in-the-Loop and Digital Twin Applications

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6133
Author(s):  
Alessandro Mingotti ◽  
Federica Costa ◽  
Diego Cavaliere ◽  
Lorenzo Peretto ◽  
Roberto Tinarelli
Keyword(s):  
Real Time ◽  
Measurement Unit ◽  
Hardware In The Loop ◽  
The Real ◽  
Digital Twin ◽  
The Status ◽  
Simulated Network ◽  
Uncertainty Contribution

In recent years, the introduction of real-time simulators (RTS) has changed the way of researching the power network. In particular, researchers and system operators (SOs) are now capable of simulating the complete network and of making it interact with the real world thanks to the hardware-in-the-loop (HIL) and digital twin (DT) concepts. Such tools create infinite scenarios in which the network can be tested and virtually monitored to, for example, predict and avoid faults or energy shortages. Furthermore, the real-time monitoring of the network allows estimating the status of the electrical assets and consequently undertake their predictive maintenance. The success of the HIL and DT application relies on the fact that the simulated network elements (cables, generation, accessories, converters, etc.) are correctly modeled and characterized. This is particularly true if the RTS acquisition capabilities are used to enable the HIL and the DT. To this purpose, this work aims at emphasizing the role of a preliminary characterization of the virtual elements inside the RTS system, experimentally verifying how the overall performance is significantly affected by them. To this purpose, a virtual phasor measurement unit (PMU) is tested and characterized to understand its uncertainty contribution. To achieve that, firstly, the characterization of a virtual PMU calibrator is described. Afterward, the virtual PMU calibration is performed, and the results clearly highlight its key role in the overall uncertainty. It is then possible to conclude that the characterization of the virtual elements, or models, inside RTS systems (omitted most of the time) is fundamental to avoid wrong results. The same concepts can be extended to all those fields that exploit HIL and DT capabilities.

scholarly journals Power Hardware-in-the-Loop: Response of Power Components in Real-Time Grid Simulation Environment

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 593
Author(s):  
Moiz Muhammad ◽  
Holger Behrends ◽  
Stefan Geißendörfer ◽  
Karsten von Maydell ◽  
Carsten Agert
Keyword(s):  
Real Time ◽  
Power Distribution ◽  
Control Strategies ◽  
Power Grid ◽  
Energy System ◽  
Hardware In The Loop ◽  
Distribution Grid ◽  
Compensation Strategy ◽  
Software Environment ◽  
The Real

With increasing changes in the contemporary energy system, it becomes essential to test the autonomous control strategies for distributed energy resources in a controlled environment to investigate power grid stability. Power hardware-in-the-loop (PHIL) concept is an efficient approach for such evaluations in which a virtually simulated power grid is interfaced to a real hardware device. This strongly coupled software-hardware system introduces obstacles that need attention for smooth operation of the laboratory setup to validate robust control algorithms for decentralized grids. This paper presents a novel methodology and its implementation to develop a test-bench for a real-time PHIL simulation of a typical power distribution grid to study the dynamic behavior of the real power components in connection with the simulated grid. The application of hybrid simulation in a single software environment is realized to model the power grid which obviates the need to simulate the complete grid with a lower discretized sample-time. As an outcome, an environment is established interconnecting the virtual model to the real-world devices. The inaccuracies linked to the power components are examined at length and consequently a suitable compensation strategy is devised to improve the performance of the hardware under test (HUT). Finally, the compensation strategy is also validated through a simulation scenario.

scholarly journals Construction of Intelligent Integrated Model Framework for the Workshop Manufacturing System Via Digital Twin

2021 ◽  
Author(s):  
Zhongyu Zhang ◽  
Zhenjie Zhu ◽  
Jinsheng Zhang ◽  
Jingkun Wang
Keyword(s):  
Real Time ◽  
Production System ◽  
Manufacturing System ◽  
System Optimization ◽  
Production Cycle ◽  
Model Framework ◽  
The Real ◽  
Digital Twin ◽  
Twin Model ◽  
Information Layer

Abstract With the drastic development of the globally advanced manufacturing industry, transition of the original production pattern from traditional industries to advanced intelligence is completed with the least delay possible, which are still facing new challenges. Because the timeliness, stability and reliability of them is significantly restricted due to lack of the real-time communication. Therefore, an intelligent workshop manufacturing system model framework based on digital twin is proposed in this paper, driving the deep inform integration among the physical entity, data collection, and information decision-making. The conceptual and obscure of the traditional digital twin is refined, optimized, and upgraded on the basis of the four-dimension collaborative model thinking. A refined nine-layer intelligent digital twin model framework is established. Firstly, the physical evaluation is refined into entity layer, auxiliary layer and interface layer, scientifically managing the physical resources as well as the operation and maintenance of the instrument, and coordinating the overall system. Secondly, dividing the data evaluation into the data layer and the processing layer can greatly improve the flexible response-ability and ensure the synchronization of the real-time data. Finally, the system evaluation is subdivided into information layer, algorithm layer, scheduling layer, and functional layer, developing flexible manufacturing plan more reasonably, shortening production cycle, and reducing logistics cost. Simultaneously, combining SLP and artificial bee colony are applied to investigate the production system optimization of the textile workshop. The results indicate that the production efficiency of the optimized production system is increased by 34.46%.

scholarly journals Using Digital Twining in Fast-food Production Chain Simulation

2021 ◽  
Vol 343 ◽  
pp. 03005
Author(s):  
Florina Chiscop ◽  
Bogdan Necula ◽  
Carmen Cristiana Cazacu ◽  
Cristian Eugen Stoica
Keyword(s):  
Real Time ◽  
Fast Food ◽  
Virtual Model ◽  
Time Data ◽  
Production Chain ◽  
The Real ◽  
Time Dynamic ◽  
Digital Twin ◽  
Food Company ◽  
The Moment

The topic of this paper represents our research in the process of creating a virtual model (digital twin) for a fast-food company production chain starting with the moment when a customer launches an order, following with the processing of that order, until the customer receives it. The model will describe elements that are included in this process such as equipment, human resources and the necessary space that is needed to host this layout. The virtual model created in a simulation platform will be a replicate of a real fast-food company, thus helping us observe the real time dynamic of this production system. Using WITNESS HORIZON 23 we will construct the model of the layout based on real time data received from the fast-food company. This digital twin will be used to manage the production chain material flow, evaluating the performance of the system architecture in various scenarios. In order to obtain a diagnosis of the system’s performance we will simulate the workflow running through preliminary architecture in compliance with the real time behaviour to identify the bottlenecks and blockages in the flow trajectory. In the end we will propose two different optimised architectures for the fast-food company production chain.

scholarly journals Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

The Design of Product Packaging Transmission Line Control System Based on Siemens PLC

2014 ◽  
Vol 709 ◽  
pp. 327-330
Author(s):  
Zong Yang Zhong ◽  
Hui Lai Sun
Keyword(s):  
Control System ◽  
Transmission Line ◽  
Real Time ◽  
Control Program ◽  
Main Function ◽  
Time Data ◽  
Product Packaging ◽  
The Real ◽  
Real Time Data ◽  
The Status

This paper mainly introduces a kind of product packaging transmission line control system based on Siemens S7-200 PLC. It elaborated the main function principle of the system and the implementation of the control program; SIMATIC WinCC flexible 2008 of SIEMENS was used to monitor the status of the system, and display the real-time data and alarm report. At the same time the system can also be controlled by the screen.

scholarly journals Hardware-in-the-Loop Operations With an Emulator Rig for SOFC Hybrid Systems

2017 ◽  
Author(s):  
Mario L. Ferrari ◽  
Alessandro Sorce ◽  
Aristide F. Massardo
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Hybrid Systems ◽  
Control Strategies ◽  
Experimental Tests ◽  
Outlet Temperature ◽  
Hardware In The Loop ◽  
Set Point ◽  
The Real ◽  
System Components

This paper shows the Hardware-In-the-Loop (HIL) technique developed for the complete emulation of Solid Oxide Fuel Cell (SOFC) based hybrid systems. This approach is based on the coupling of an emulator test rig with a real-time software for components which are not included in the plant. The experimental facility is composed of a T100 microturbine (100 kW electrical power size) modified for the connection to an SOFC emulator device. This component is composed of both anodic and cathodic vessels including also the anodic recirculation system which is carried out with a single stage ejector, driven by an air flow in the primary duct. However, no real stack material was installed in the plant. For this reason, a real-time dynamic software was developed in the Matlab-Simulink environment including all the SOFC system components (the fuel cell stack with the calculation of the electrochemical aspects considering also the real losses, the reformer, and a cathodic recirculation based on a blower, etc.). This tool was coupled with the real system utilizing a User Datagram Protocol (UDP) data exchange approach (the model receives flow data from the plant at the inlet duct of the cathodic vessel, while it is able to operate on the turbine changing its set-point of electrical load or turbine outlet temperature). So, the software is operated to control plant properties to generate the effect of a real SOFC in the rig. In stand-alone mode the turbine load is changed with the objective of matching the measured Turbine Outlet Temperature (TOT) value with the calculated one by the model. In grid-connected mode the software/hardware matching is obtained through a direct manipulation of the TOT set-point. This approach was essential to analyze the matching issues between the SOFC and the micro gas turbine devoting several tests on critical operations, such as start-up, shutdown and load changes. Special attention was focused on tests carried out to solve the control system issues for the entire real hybrid plant emulated with this HIL approach. Hence, the innovative control strategies were developed and successfully tested considering both the Proportional Integral Derivative and advanced approaches. Thanks to the experimental tests carried out with this HIL system, a comparison between different control strategies was performed including a statistic analysis on the results The positive performance obtainable with a Model Predictive Control based technique was shown and discussed. So, the HIL system presented in this paper was essential to perform the experimental tests successfully (for real hybrid system development) without the risks of destroying the stack in case of failures. Mainly surge (especially during transient operations, such as load changes) and other critical conditions (e.g. carbon deposition, high pressure difference between the fuel cell sides, high thermal gradients in the stack, excessive thermal stress in the SOFC system components, etc.) have to be carefully avoided in complete plants.

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