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Smart Grid Outlier Detection Based on the Minorization-Maximization Algorithm.

Lina Qiao1,2, Wengen Gao1,2, Yunfei Li1,2

  • 1College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a Minorization-Maximization (MM) algorithm to detect and locate outliers in power systems. The MM algorithm effectively identifies and pinpoints power system anomalies, enhancing operational stability and data accuracy.

Keywords:
localizationoutlier detectionpower systemthe Minorization–Maximization algorithm

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Area of Science:

  • Electrical Engineering
  • Data Science
  • Control Systems

Background:

  • Outliers in power systems, caused by aging equipment or sensor faults, compromise data quality and system safety.
  • Accurate data is crucial for effective power system monitoring, analysis, and control.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for detecting and localizing outliers in power systems.
  • To improve the reliability and accuracy of power system data analysis.

Main Methods:

  • A Minorization-Maximization (MM) algorithm was developed for outlier detection and localization.
  • Estimation of unknown parameters for Gaussian Mixture Models (GMM) was integrated.
  • Simulations were conducted on the IEEE 14-bus system to test the algorithm's performance.

Main Results:

  • The MM algorithm demonstrated superior performance in detecting outliers compared to traditional methods.
  • Accurate localization of outliers was achieved with a probability exceeding 95%.
  • The algorithm effectively handles outliers, improving data integrity.

Conclusions:

  • The proposed MM algorithm offers an effective solution for managing outliers in power systems.
  • Implementation of this algorithm enhances the monitoring, analysis, and control capabilities of power systems.
  • This contributes to ensuring the stable and reliable operation of the power grid.