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Increased digitization, rising complexity and expansion of power systems, has resulted in significant increase in the quantum of data ranging from KiloBytes (KB) to PetaBytes (PB). The branch of statistics has helped us to process the big data to analyse draw inferences and take further action for effective increase in efficiency of power systems. The advent of computer softwares as MS-Excel, COBOL, DBase, SQL has enabled process more big data fast. However, with the increase in automation and deployment of several types and quantity of sensors, power systems becoming closed-loop and need for processing the data and taking corrective action very fast as adjusting power system parameters, the modern techniques as Big Data (BD) Analysis, Machine Learning Algorithms (MLA) support energy systems to enhance the technical, operational, economic, and environmental benefits.

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Big Data and Machine Learning for Hybrid Power System—Power Quality

  • 章节

  • 发表日期：

    2025年

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  • Namrata Manohar,
  • Mousmi Ajay Chaurasia,
  • Stefan Mozar,
  • Chia-Feng Juang

摘要

Increased digitization, rising complexity and expansion of power systems, has resulted in significant increase in the quantum of data ranging from KiloBytes (KB) to PetaBytes (PB). The branch of statistics has helped us to process the big data to analyse draw inferences and take further action for effective increase in efficiency of power systems. The advent of computer softwares as MS-Excel, COBOL, DBase, SQL has enabled process more big data fast. However, with the increase in automation and deployment of several types and quantity of sensors, power systems becoming closed-loop and need for processing the data and taking corrective action very fast as adjusting power system parameters, the modern techniques as Big Data (BD) Analysis, Machine Learning Algorithms (MLA) support energy systems to enhance the technical, operational, economic, and environmental benefits.