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Multisource Heterogeneous Sensor Processing Meets Distribution Networks: Brief Review and Potential Directions.

Junliang Wang1, Ying Zhang1

  • 1School of Microelectronics (School of Integrated Circuits), Nanjing University of Science and Technology, Nanjing 210094, China.

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The increasing deployment of sensors in distribution networks (DNs) generates vast multisource heterogeneous (MSH) data. This review explores MSH data analysis for improved DN reliability and intelligent grid construction.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • Growing sensor deployment in distribution networks (DNs) driven by power automation and IoT.
  • Exponential increase in multisource heterogeneous (MSH) data from sensors in multilayer grid architectures.
  • Challenges in handling large-scale MSH data for traditional reliability assessment and dispatch planning.

Purpose of the Study:

  • To review technological developments in MSH data analysis for DNs.
  • To address the challenge of identifying critical factors and dynamically assessing DN reliability using MSH data.
  • To propose future directions for intelligent, reliable, and stable next-generation DN construction.

Main Methods:

  • Analysis of existing technologies and algorithms for MSH data in DNs.
  • Integration of conventional approaches with artificial intelligence for computational adaptability.
  • Focus on key approaches for MSH data processing and assessment.

Main Results:

  • Identified dual implications of large-scale MSH data: enhanced reliability assessment foundation and computational demands on traditional methods.
  • Highlighted the pressing challenge of dynamic assessment and prediction of DN reliability under extreme conditions.
  • Proposed practical future directions for MSH data analysis in DNs.

Conclusions:

  • MSH data analysis is crucial for advancing DN reliability and dispatch planning.
  • Integrating conventional methods with AI offers a promising approach for MSH data processing.
  • Future research should focus on addressing the unique characteristics of DN data for intelligent grid construction.