Recent scientific and technological advancements driven by the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI), distributed computing and data communication technologies have opened up a vast range of opportunities in many scientific fields – spanning from fast, reli-able and efficient data communication to large-scale cloud/edge computing and intelligent big data analytics. Technological inno-vations and developments in these areas have also enabled many opportunities in the space industry. The successful Mars landing of NASA’s Perseverance rover on February 18, 2021 represents another giant leap for mankind in space exploration. Emerging research and developments of connectivity and computing tech-nologies in IoT for space/non-terrestrial environments is expected to yield significant benefits in the near future. This survey paper presents a broad overview of the area and provides a look-ahead of the opportunities made possible by IoT and space-based technologies. We first survey the current developments of IoT and space industry, and identify key challenges and opportunities in these areas. We then review the state-of-the-art and discuss future opportunities for IoT developments, deployment and integration to support future endeavours in space exploration.

gital twins, along with the internet of things (IoT), data mining, and machine learning technologies, offer great potential in the transformation of today’s manufacturing paradigm toward intelligent manufacturing. Production control in petrochemical industry involves complex circumstances and a high demand for timeliness; therefore, agile and smart controls are important components of intelligent manufacturing in the petrochemical industry. This paper proposes a framework and approaches for constructing a digital twin based on the petrochemical industrial IoT, machine learning and a practice loop for information exchange between the physical factory and a virtual digital twin model to realize production control optimization. Unlike traditional production control approaches, this novel approach integrates machine learning and real-time industrial big data to train and optimize digital twin models. It can support petrochemical and other process manufacturing industries to dy-namically adapt to the changing environment, respond in a timely manner to changes in the market due to production optimization, and improve economic benefits. Accounting for environmental characteristics, this paper provides concrete solutions for machine learning difficulties in the petrochemical industry, e.g., high data dimensions, time lags and alignment between time series data, and high demand for immediacy. The approaches were evaluated by applying them in the production unit of a petrochemical factory, and a model was trained via industrial IoT data and used to realize intelligent production control based on real-time data. A case study shows the effectiveness of this approach in the petrochemical industry.

This paper presents a case study for the use of an IoT-driven digital twin for energy optimization in an automated Surface Mount Technology (SMT) PCB assembly line containing legacy machines. The line was instrumented with multiple sensors for measuring machine-wise activity and energy consumption. A software platform for data aggregation and a discrete-event digital twin of the line were built entirely using open-source tools. Based on the insights gained from data collected over several days, we propose a buffering-based solution for improving the energy efficiency of the line, and evaluate its impact using simulations of the digital twin. The results show that a 2.7x reduction in the energy consumption is possible via buffer insertion without significantly affecting line throughput.

数字孪生(digital twin，DT)是推动电力装备领域数字化、智能化发展的关键技术之一，相关研究处于初

期起步阶段，如何实现电力装备数字孪生成为亟需解决的问题。该文首先阐述了数字孪生的内涵及应用；从数字孪生框架与实现方法、其在装备全寿命周期的应用、主流厂商及其平台方面总结了装备领域数字孪生的研究与应用进展；其次分析了电力装备数字孪生实现所需的关键技术；最后以变压器为例给出了基于 Microsoft Azure 和ANSYS Twin Builder 的电力装备多物理场数字孪生实现方法，并指出实现电力装备数字孪生在数据采集、模型构建与求解、平台使用方面的挑战。该文建议下一步开发高性能传感装置并构建合理的传感网络提升数据采集的深度与广度；开展电力装备全尺度多物理场模型的构建与实时求解算法研究；开发面向电力装备性能分析的国产化数字孪生平台。

Abstract. With the growing of intermittent renewable energy sources, like wind and solar, are required energy backup solutions to establish an advantageous compromise between the energy production and consumption. Typically, these renewable energy sources are not installed at the end-users level, which can cre-ate the problem of uncontrolled distributed energy sources. In this research work we propose a solution based on the standard OpenADR to handle this problem, creating a platform based on internet-of-things capable to turn-on or off electrical devices based on a central decision process that meets the requirements of energy producers and consumers. Producers can provide energy according to the con-sumer’s requirements and take part of energy production and costs fluctuations. Based on an OpenADR standard for energy data exchange and a central cloud server, a list of services are provided to handle this transactions, with georefer-enced information to minimize energy losses in the distribution process.

更多

Keywords: OpenADR, Decision Process, Smart Grid, Distributed Energy Sources

收起

With the emergence of distributed energy resources (DERs), with their associated communication and control complexities, there is a need for an efficient platform that can digest all the incoming data and ensure the reliable operation of the power system. The digital twin (DT) is a new concept that can unleash tremendous opportunities and can be used at the different control and security levels of power systems. This paper provides a methodology for the modelling of the implementation of energy cyber-physical systems (ECPSs) that can be used for multiple applications. Two DT types are introduced to cover the high-bandwidth and the low-bandwidth applications that need centric oversight decision making. The concept of the digital twin is validated and tested using Amazon Web Services (AWS) as a cloud host that can incorporate physical and data models as well as being able to receive live measurements from the different actual power and control entities. The experimental results demonstrate the feasibility of the real-time implementation of the DT for the ECPS based on internet of things (IoT) and cloud computing technologies. The normalized mean-square error for the low-bandwidth DT case was 3.7%. In the case of a high-bandwidth DT, the proposed method showed superior performance in reconstructing the voltage estimates, with 98.2% accuracy from only the controllers’ states.

2

Key Takeaways

➢ Use Industrial applications to learn about:

更多

▪ IIoT architecture

▪ Building and Using Digital Twin

➢ MathWorks key building blocks for developing IIoT applications:

▪ Data Analysis and Physical Modeling

▪ Operational Deployment and Integration

➢ MathWorks teams can help you get your project started

▪ Training

▪ Consulting

收起

Driven by new regulation, new market structures and new energy sources the smart grid is the response to profound changes in the way that electricity is generated, distributed, managed and consumed. What makes the challenge particularly acute, is the essential role that energy production plays in modern democracies. Keeping the lights on is a political, economic and social imperative.

更多

Enabled by current and future developments in technology, smart grids will better enable electricity companies to meet rising demand, increase the reliability and quality of power supplies, improve energy efficiency, and integrate low-carbon energy sources into power networks.

This paper looks at the environment in which the smart grid is being developed, based on industry standards and how a newly intelligent, interconnected and interoperable grid can be seen as a leading example of the Internet of Things

in action.

收起