Application and Prospect of Human-Machine Interaction in Power System

Yunfeng, Zou; Mingming, Pang

doi:10.1088/1742-6596/1345/3/032094

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Robotics, according to Lyu and Liu (2021: 2) 'is at the intersection of computer science and engineering, involving design, construction, oper-ation, and use of robots, increasingly adopted in the energy sector to reduce set-up time and cost and improve quality and productivity'. Intelligent patrol robots are used to patrol outdoor power substations remotely, detecting defects in equipment and machinery in real time (Yunfeng and Mingming, 2019).…”

Section: Figure 1: 4ir Technologies In the Energy Sectormentioning

confidence: 99%

“…Machine-human integration (MHI), referred to by Yunfeng and Mingming (2019) as machinehuman interaction, has developed from simple switches that used analogue systems to using computer systems to collect and analyse data at power terminals to dispatch this power within the grid, and to the use of voice to promote knowledge learning and transmission of data. Today, fifth generation MHI technology facilitates the friendly interaction between users and various systems with information tasks being exchanged between humans and various systems, such as computers and mechanical systems (Yunfeng and Mingming, 2019). Here, multi-sensor fusion technology is used as the interface for more intelligent interaction between staff and robots, initiated by robots themselves.…”

Section: Figure 1: 4ir Technologies In the Energy Sectormentioning

confidence: 99%

“…Voyant et al (2017) speak of machine learning (or AI) techniques that may be used to better forecast solar radiation, which allows solar plants to optimise productivity. Yunfeng and Mingming (2019) and Liao et al (2019) explain how AI (or virtual reality) is being used to reproduce various operational environments in the power system for power grid simulation teaching and training, safety and rescue training, and virtual maintenance testing of power system equipment, as well as addressing training-related challenges such as lack of personnel and the need for expensive testing equipment. Machines are being developed to carry out what Makala and Bakovic (2020, 2) refer to as 'deep learning' to 'help discern patterns and anomalies across very large datasets -both on the power demand and power supply sides -that otherwise would be nearly impossible to achieve.…”

Section: Introductionmentioning

confidence: 99%

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A comparative analysis of the application of Fourth Industrial Revolution technologies in the energy sector: A case study of South Africa, Germany and China

Bhagwan

Evans

2022

J. energy South. Afr.

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Fourth Industrial Revolution (4IR) technologies have elevated the capabilities and possibilities of improvement and efficiency in the energy sector. This paper interrogates how energy companies in South Africa, Germany and China apply 4IR technologies. A total of 26 energy companies in those countries were surveyed. An analysis was carried out using the Cronbach Alpha, Kruskal-Wallis and Mann-Whitney tests. Survey results indicate that 85% of companies acknowledge good levels of participation in the 4IR, and were clear about which 4IR technologies are important, although few companies develop these themselves. Technologies enabling access to big, real-time data (BRTD) and BRTD analysis software, are valued the most in measured importance, efficiency, reliability and ability to be integrated across the energy system. The transfer of data using the Internet of things ranked highly as a 4IR technology, whereas artificial intelligence, robotics and machine-human integration (also referred to as machine-human interaction) are considered less important, efficient, and reliable. China rates 4IR technologies as more important than South Africa and Germany do. For South Africa to be competitive in the global energy sector it needs to engage with and embrace 4IR technologies to a greater extent.

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Section: Figure 1: 4ir Technologies In the Energy Sectormentioning

confidence: 99%

Section: Figure 1: 4ir Technologies In the Energy Sectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparative analysis of the application of Fourth Industrial Revolution technologies in the energy sector: A case study of South Africa, Germany and China

Bhagwan

Evans

2022

J. energy South. Afr.

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“…Machine-actionability means that machines can be programmed so that they find, access, and process data—ultimately without further human interaction. The implications of this third challenge lead to a new perspective on an energy system with human and machine agents at the center 15 , 29 , 30 . The new perspective on the energy system is visualized in Fig.…”

Section: Introductionmentioning

confidence: 99%

Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

Schwanitz

Wierling

Biresselioğlu

et al. 2022

Sci Rep

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With the continued digitization of the energy sector, the problem of sunken scholarly data investments and forgone opportunities of harvesting existing data is exacerbating. It compounds the problem that the reproduction of knowledge is incomplete, impeding the transparency of science-based targets for the choices made in the energy transition. The FAIR data guiding principles are widely acknowledged as a way forward, but their operationalization is yet to be agreed upon within different research domains. We comprehensively test FAIR data practices in the low carbon energy research domain. 80 databases representative for data needed to support the low carbon energy transition are screened. Automated and manual tests are used to document the state-of-the art and provide insights on bottlenecks from the human and machine perspectives. We propose action items for overcoming the problem with FAIR energy data and suggest how to prioritize activities.

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Collaborative Decision-Making for Human-Technology Interaction - A Case Study Using an Automated Water Bottling Plant

Coetzer

Kuriakose

Vermaak

2020

J. Phys.: Conf. Ser.

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The Fourth Industrial Revolution places different rapidly advancing technologies at the centre of developing autonomous systems. Previous and current research focus on the developing and testing state-of-the-art technologies related to an autonomous environment. However, most industrial environments in the South Africa, and many other developing countries, are still making use of mixed environments. In mixed environments, automated systems and humans need to collaborate for the completion of a process. Currently, there exists very little research on how a collaborative decision-making process can be developed such that the worker’s acceptance and adaptation to the process is taken into cognizance. This research identifies the lack of collaborative decision-making processes as a research gap. This paper focuses on the research done in this field with an extensive literature review, followed by a review of potential models for human technology interaction. The paper then uses the case study of an automated water bottling plant to advance the study in collaborative decision making. The paper concludes by discussing the advantages of collaborative decision.

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Application and Prospect of Human-Machine Interaction in Power System

Cited by 4 publications

References 23 publications

A comparative analysis of the application of Fourth Industrial Revolution technologies in the energy sector: A case study of South Africa, Germany and China

A comparative analysis of the application of Fourth Industrial Revolution technologies in the energy sector: A case study of South Africa, Germany and China

Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

Collaborative Decision-Making for Human-Technology Interaction - A Case Study Using an Automated Water Bottling Plant

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