Study on Smart Object-Based Control Model for Cyber-Physical Systems

2014 ◽  
Vol 536-537 ◽  
pp. 1195-1199
Author(s):  
Yan Min Yin ◽  
Chun Lei Gao
Keyword(s):  
Power System ◽  
Smart Grids ◽  
Large Scale ◽  
Low Voltage ◽  
Cyber Physical Systems ◽  
Control Model ◽  
Control Mechanisms ◽  
Technical Problems ◽  
Physical Systems ◽  
Optimization Control

The cyber-physical systems (CPS) are large-scale intelligent complex systems by the deep integration of technology and the physical systems. The cyber-physical power systems (CPPS) are the key technology to implement the objective of smart grids, by seamlessly integrating the computing, communication and sensing technologies with the power system. The existing research of CPPS is limited to the survey. This paper firstly presents the concept of smart power object applied to CPPS, which often is a power device object adopts RFID technology to identify itself, feeds back its real-time states, and has certain coordination ability. Then, on this basis, the key technical problems of global optimization and local control based on CPPS is further studied, such as control model, construction strategy, coordination control mechanisms, and communication mechanisms. Finally, it is applied to regional voltage optimization control to implement smart optimization control in low voltage power system.

scholarly journals Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 487 ◽  
Author(s):  
Mahmoud Elsisi ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
Mohamed M. F. Darwish
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Industry 4.0 ◽  
Learning Algorithm ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Smart Meters ◽  
Industrial Environment ◽  
Physical Systems ◽  
Smart Machines

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

An integrated low-voltage rated HTS DC power system with multifunctions to suit smart grids

2015 ◽  
Vol 510 ◽  
pp. 48-53 ◽  
Author(s):  
Jian Xun Jin ◽  
Xiao Yuan Chen ◽  
Ronghai Qu ◽  
Hai Yang Fang ◽  
Ying Xin
Keyword(s):  
Power System ◽  
Smart Grids ◽  
Low Voltage ◽  
Dc Power

Durchgängiges Anforderungsmanagement und Engineering für Smart Grid Lösungen

2020 ◽  
Vol 68 (9) ◽  
pp. 711-719
Author(s):  
Mathias Uslar
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Cyber Physical Systems ◽  
Industrie 4.0 ◽  
Internet Der Dinge ◽  
Physical Systems

ZusammenfassungIn diesem Beitrag wird die Notwendigkeit einer sinnvollen Definition und Klarstellung der Disziplin Energieinformatik aufgezeigt. Der Beitrag diskutiert verschiedene bestehende Definitionen und stellt sie in den Kontext des Anforderungsmanagements und der Lösungsfindung. Er motiviert die Notwendigkeit eines strukturierten disziplinären Ansatzes in der Energieinformatik auf der Grundlage bestehender Probleme und skizziert den aktuellen Stand des Stands der Wissenschaft und Technik, der hauptsächlich den systemtechnischen Anwendungsbereich für Smart Grids umfasst. Synergien mit anderen aktuellen Schwerpunktthemen wie Internet der Dinge (IoT), Industrie 4.0 (Digitalisierung der Produktion) und Cyber-Physical Systems (CPS) werden aus Anforderungssicht motiviert. Auf der Grundlage der aufgeworfenen Fragen und Herausforderungen werden neue sinnvolle Forschungsthemen für ein durchgängiges Anforderungsmanagement im Kontext Smart Grid diskutiert.

Data quality challenges in large-scale cyber-physical systems: A systematic review

2021 ◽  
pp. 101951
Author(s):  
Ahmed Abdulhasan Alwan ◽  
Mihaela Anca Ciupala ◽  
Allan J. Brimicombe ◽  
Seyed Ali Ghorashi ◽  
Andres Baravalle ◽  
...  
Keyword(s):  
Systematic Review ◽  
Data Quality ◽  
Large Scale ◽  
Cyber Physical Systems ◽  
Physical Systems

Adoption of Machine Learning With Adaptive Approach for Securing CPS

2020 ◽  
pp. 388-415
Author(s):  
Rama Mercy Sam Sigamani
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Smart Grids ◽  
False Negative ◽  
False Negative Rate ◽  
Cyber Physical Systems ◽  
Detection Accuracy ◽  
Physical Systems ◽  
Positive Rate ◽  
Iot Devices

The cyber physical system safety and security is the major concern on the incorporated components with interface standards, communication protocols, physical operational characteristics, and real-time sensing. The seamless integration of computational and distributed physical components with intelligent mechanisms increases the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. In IoT-enabled cyber physical systems, cyber security is an essential challenge due to IoT devices in industrial control systems. Computational intelligence algorithms have been proposed to detect and mitigate the cyber-attacks in cyber physical systems, smart grids, power systems. The various machine learning approaches towards securing CPS is observed based on the performance metrics like detection accuracy, average classification rate, false negative rate, false positive rate, processing time per packet. A unique feature of CPS is considered through structural adaptation which facilitates a self-healing CPS.

scholarly journals Methods for Large-Scale Time-Triggered Network Scheduling

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 738 ◽  
Author(s):  
Francisco Pozo ◽  
Guillermo Rodriguez-Navas ◽  
Hans Hansson
Keyword(s):  
Wireless Networks ◽  
Large Scale ◽  
Synthesis Method ◽  
Cyber Physical Systems ◽  
Network Scheduling ◽  
Synthesis Time ◽  
Physical Systems ◽  
Large Size ◽  
Np Complete ◽  
Communication Schedule

Future cyber–physical systems may extend over broad geographical areas, like cities or regions, thus, requiring the deployment of large real-time networks. A strategy to guarantee predictable communication over such networks is to synthesize an offline time-triggered communication schedule. However, this synthesis problem is computationally hard (NP-complete), and existing approaches do not scale satisfactorily to the required network sizes. This article presents a segmented offline synthesis method which substantially reduces this limitation, being able to generate time-triggered schedules for large hybrid (wired and wireless) networks. We also present a series of algorithms and optimizations that increase the performance and compactness of the obtained schedules while solving some of the problems inherent to segmented approaches. We evaluate our approach on a set of realistic large-size multi-hop networks, significantly larger than those considered in the existing literature. The results show that our segmentation reduces the synthesis time by up to two orders of magnitude.

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