Design and Implementation of a Data-Driven Approach to Visualizing Power Quality

Fei Xiao, Tianguang Lu, Qian Ai, Xiaolong Wang, Xinyu Chen, Sidun Fang, Qiuwei Wu

    Research output: Contribution to journalJournal articleResearchpeer-review

    139 Downloads (Pure)

    Abstract

    Numerous underlying causes of power-quality (PQ) disturbances have enhanced the application of situational awareness to power systems. This application provides an optimal overall response for contingencies. With measurement data acquired by a multi-source PQ monitoring system, we propose an interactive visualization tool for PQ disturbance data based on a geographic information system (GIS). This tool demonstrates the spatio–temporal distribution of the PQ disturbance events and the cross-correlation between PQ records and environmental factors, leveraging Getis statistics and random matrix theory. A methodology based on entity matching is also introduced to analyze the underlying causes of PQ disturbance events. Based on real-world data obtained from an actual power system, offline and online PQ data visualization scenarios are provided to verify the effectiveness and robustness of the proposed framework.
    Original languageEnglish
    JournalIEEE Transactions on Smart Grid
    Volume11
    Issue number5
    Pages (from-to)4366 - 4379
    ISSN1949-3053
    DOIs
    Publication statusPublished - 2020

    Keywords

    • Situation awareness
    • Power quality
    • Geographic information system
    • Getis statistics
    • Random matrix theory
    • Entity matching

    Fingerprint

    Dive into the research topics of 'Design and Implementation of a Data-Driven Approach to Visualizing Power Quality'. Together they form a unique fingerprint.

    Cite this