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Studies from Western Norway University of Applied Sciences Yield New Information about Blockchain Technology (Privacy Reinforcement Learning for Faults Detection In the Smart Grid)

  • Jul 30, 2021 12:17 pm GMT
  • 146 views
Source: 
Telecommunications Daily

2021 JUL 29 (NewsRx) -- By a News Reporter-Staff News Editor at Telecommunications Daily -- A new study on Technology - Blockchain Technology is now available. According to news originating from Bergen, Norway, by NewsRx correspondents, research stated, “Recent anticipated advancements in ad hoc Wireless Mesh Networks (WMN) have made them strong natural candidates for Smart Grid’s Neighborhood Area Network (NAN) and the ongoing work on Advanced Metering Infrastructure (AMI). Fault detection in these types of energy systems has recently shown lots of interest in the data science community, where anomalous behavior from energy platforms is identified.”

Our news journalists obtained a quote from the research from the Western Norway University of Applied Sciences, “This paper develops a new framework based on privacy reinforcement learning to accurately identify anomalous patterns in a distributed and heterogeneous energy environment. The local outlier factor is first performed to derive the local simple anomalous patterns in each site of the distributed energy platform. A reinforcement privacy learning is then established using blockchain technology to merge the local anomalous patterns into global complex anomalous patterns. Besides, different optimization strategies are suggested to improve the whole outlier detection process. To demonstrate the applicability of the proposed framework, intensive experiments have been carried out on well-known CASAS (Center of Advanced Studies in Adaptive Systems) platform. Our results show that our proposed framework outperforms the baseline fault detection solutions. Recent anticipated advancements in ad hoc Wireless Mesh Networks (WMN) have made them strong natural candidates for Smart Grid’s Neighborhood Area Network (NAN) and the ongoing work on Advanced Metering Infrastructure (AMI). Fault detection in these types of energy systems has recently shown lots of interest in the data science community, where anomalous behavior from energy platforms is identified. This paper develops a new framework based on privacy reinforcement learning to accurately identify anomalous patterns in a distributed and heterogeneous energy environment. The local outlier factor is first performed to derive the local simple anomalous patterns in each site of the distributed energy platform. A reinforcement privacy learning is then established using blockchain technology to merge the local anomalous patterns into global complex anomalous patterns. Besides, different optimization strategies are suggested to improve the whole outlier detection process. To demonstrate the applicability of the proposed framework, intensive experiments have been carried out on well-known CASAS (Center of Advanced Studies in Adaptive Systems) platform.”

According to the news editors, the research concluded: “Our results show that our proposed framework outperforms the baseline fault detection solutions.”

This research has been peer-reviewed.

For more information on this research see: Privacy Reinforcement Learning for Faults Detection In the Smart Grid. Ad Hoc Networks, 2021;119:102541. Ad Hoc Networks can be contacted at: Elsevier, Radarweg 29, 1043 Nx Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Ad Hoc Networks - http://www.journals.elsevier.com/ad-hoc-networks/)

The news correspondents report that additional information may be obtained from Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Dept Comp Sci Elect Engn & Math Sci, Bergen, Norway. Additional authors for this research include Asma Belhadi, Youcef Djenouri, Gautam Srivastava and Alireza Jolfaei.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.adhoc.2021.102541. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

 

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