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Energy and Power Signals01:17

Energy and Power Signals

272
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
272
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

78
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
78
Power in a Three-Phase Circuit01:15

Power in a Three-Phase Circuit

297
Three-phase systems have two configurations: the wye and delta. A star configuration can be three or four wires; in a delta configuration, the components are connected in a closed loop. Instantaneous power refers to the power value at a precise moment, and in a balanced three-phase system, it is constant. This is because the sum of the instantaneous powers in the three phases remains steady over time, despite individual fluctuations, due to the symmetry and phase relationship. The total...
297
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

180
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
180
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

129
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
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Related Experiment Video

Updated: Jun 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Anomaly Detection for Power Quality Analysis Using Smart Metering Systems.

Gabriele Patrizi1, Cristian Garzon Alfonso1, Leandro Calandroni1

  • 1Department of Information Engineering, University of Florence, Via di Santa Marta, 3, 50139 Florence, Italy.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
Summary

This study introduces machine learning for rapid power quality anomaly detection using One Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Angle-Based Outlier Detection (ABOD). These methods efficiently identify signal anomalies on-line, improving system availability and reducing maintenance costs.

Keywords:
anomaly detectionfault detectionmachine learningmetering systemspower quality

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • Power quality analysis is critical for complex systems and large plants.
  • Rapid anomaly detection in voltage and current signals is essential for system availability and cost-effective maintenance.
  • Existing methods may lack the speed and efficiency required for on-line detection.

Purpose of the Study:

  • To implement and evaluate machine learning-based anomaly detection algorithms for on-line power quality monitoring.
  • To assess the effectiveness of One Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Angle-Based Outlier Detection (ABOD) for rapid anomaly identification.
  • To demonstrate the feasibility of deploying these algorithms directly on sensor nodes for low-complexity, efficient signal processing.

Main Methods:

  • Utilized machine learning algorithms: One Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Angle-Based Outlier Detection (ABOD).
  • Applied algorithms for on-line clustering and anomaly detection of voltage and current signals.
  • Established an experimental platform for method evaluation, using consistent hyperparameters and Principal Component Analysis (PCA) for data processing.

Main Results:

  • The proposed anomaly detection algorithms demonstrated rapid and efficient identification of signal anomalies.
  • Models achieved high performance metrics, including 100% recall and up to a 92% F1 score.
  • The computational simplicity allows for direct implementation on sensor nodes.

Conclusions:

  • Machine learning algorithms, specifically OCSVM, IF, and ABOD, are effective tools for on-line power quality anomaly detection.
  • The proposed solution offers a low-complexity, efficient method for enhancing system availability and reducing maintenance.
  • Further classification algorithms can be employed for in-depth investigation once anomalies are detected by the initial algorithms.