EN541 Article Critique: AI for Smart Robot in Power Systems

This report presents a comprehensive critique of an article discussing the framework for a smart operating robot in a power system, emphasizing the application of Artificial Intelligence (AI). The original article highlights issues within the Tianjin power process in China, where manual operation ticket generation leads to inefficiencies and potential errors. The critique evaluates the article's strengths and weaknesses, focusing on the proposed AI-based solution for automating power system analysis and operation ticket compilation. It examines the system's ability to enhance decision-making, predict incidents, and reduce operator workload, while also considering the challenges associated with automating tasks requiring experienced reasoning. The critique concludes by assessing the article's overall contribution to the field of AI-driven power system management.

Running head: ARTICLE CRITIQUE
RESEARCH METHODS FOR ENGINEERS: ARTICLE
CRITIQUE
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Authors Note

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Introduction
The purpose of the article is to highlight the concept of the smart processing system
and the significant use of artificial technology application within powers control centre. The
article portrays issues found within the Tianjin power process in China. Another major
problem highlighted in the paper, is the power operations tickets are generated in a manual
way by the operators. Therefore, based on these issues the paper provides a framework of the
smart processing system specifically an operated robot that consists of an intelligent power
system along with smart ticket operating system.
Summary paragraph
One of the major demands of the power system evaluating tool is the urge for an
effective analysis process. It has been seen in the article that, with a growth of technology,
institutions and organisation are increasingly implementing new technology that urges in
terms of various locations with a better system of evaluation tool and automatic system for
ticketing operations (Wang et al., 2017). The fact illustrated here is that during the time when
the power process faces interruption the evaluation process used may not be helpful for the
functioning operators therefore; to solve this issue AI techniques has proposed in the paper to
establish a better system of evaluation. The reason for the slowdown of the system that is the
traditional method of analysis using an algorithm such as the Newton Raphson method due to
which calculation process takes a lot of time. Therefore, the paper reveals the use of AI
technology for developing the DSA process with an advanced process of AI (Culler & Long,
2016).
Moreover, the article presents an understanding of the use and handling of the ticket
compilation system for refusing the occurrence of errors and delay within the system. Apart
from this company the article also highlights the automatic ticket generating that has been
adopted by several organisations (Wang et al., 2018). In order to measure and understand the
potential after consequences of the operations, the study has illustrated the incident
predication system and warning system for helping the operators to take preventive action in
case of accidental events.
Critical analysis of the Article
Power control system is the major facilities within the power systems. The main
purpose of the centre is ensuring the supervision and evaluation the position of the power

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process in accordance to the suitable actions for ensuring flexibility of the power operations.
It is evident from the article that the modern system of power is a complex issue that requires
a huge amount of information for operating it (Lee et al., 2016). It is rather a struggle for the
employees to implement effective planning for the complicated issues based on the huge
information. It has been found from the article that the control centre needs to be managed 24
hours. In this regards, the risk may occur if the operators are shut down at night.
The paper has suggested the use of an operating robot to assist the operators to handle
the operations effectively. The smart operating ticket operations is a in depth learning that is
based on the process to form an automatic regenerate the operations that are reliable.
However, the strength of the smart operating system is that it helps the operator to detect the
error within the process thereby helping in the recognition of the object, and topological
evaluations of the organisation (Zander et al., 2015). On the contrary, the weakness shows
that the relevant information of the system is very rare and the existing products cannot be
replaced with smart software to generate operational tickets. Therefore, the software that is to
be used is mainly based on limited rules that can be only used in case of simpler tasks in the
power system.
In this case, it has been found that most of the problem needs to be interacted from
different perspectives due to the presence of the complex power system. For solving these
issues AI needs to be used for the better performing operations. The AI-based power
generation not only helps in the fast evaluation system, it also helps to provide the employees
to encounter with different events taking place within the power process, with better decision
assistance process (Gao et al., 2017). The system is designed with incident detection and
waning process for predicting the status of the power process for warning the employees to
take prior steps to manage the system.
It has been discussed in the article that operational tickets generated by the smart
process can help to reduce the errors within the operational and through this operators can
also check the automatic compiling system thereby reducing workload. In this context, the
article also points out relevant difficulties that might be faced by automatic generation if
operational ticket (Wang et al., 2018). The difficulty is that the operational generation of
tickets requires high experienced thinking and reasoning within the working procedure.
Conclusion

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Therefore, from the above article, it is evident that the paper illustrates the demands
and the significant application of the AI power system within the power control centre in
China. Based, on the theory of in-depth learning system, the article successfully introduced
the development of smart operating AI system within power generations. Based on this
system the paper lays down the simulation of each of the operations in details to gather the
understanding of the system.
Reference list
Culler, D., & Long, J. (2016). A prototype smart materials warehouse application
implemented using custom mobile robots and open source vision technology
developed using emgucv. Procedia Manufacturing, 5, 1092-1106.

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Gao, Z., Chin, C. S., Chiew, J. H. K., Jia, J., & Zhang, C. (2017). Design and Implementation
of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability
for Electric Vehicles. Energies, 10(10), 1503.
Lee, Y. T., Hsiao, W. H., Huang, C. M., & Seng-cho, T. C. (2016). An integrated cloud-based
smart home management system with community hierarchy. IEEE Transactions on
Consumer Electronics, 62(1), 1-9.
Wang, Q., Yang, X., Huang, Z., Ma, S., Li, Q., Wenzhong Gao, D., & Wang, F. (2018). A
Novel Design Framework for Smart Operating Robot in Power System. IEEE/CAA
Journal of Automatica Sinica, 5 (2), 531
Wang, X. V., Wang, L., Mohammed, A., & Givehchi, M. (2017). Ubiquitous manufacturing
system based on Cloud: A robotics application. Robotics and Computer-Integrated
Manufacturing, 45, 116-125.
Zander, J., Mosterman, P. J., Padir, T., Wan, Y., & Fu, S. (2015). Cyber-physical systems can
make emergency response smart. Procedia Engineering, 107, 312-318.