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Related Concept Videos

Primary Distribution01:28

Primary Distribution

148
Primary distribution systems deliver electrical power from substations to consumers through various voltage classes, with 15-kV class voltages being predominant among U.S. utilities. Older 2.5- and 5-kV classes are being replaced by 15-kV primaries, while higher 25- to 34.5-kV classes are used in high-density urban areas and rural regions with long feeders. Three-phase, four-wire multigrounded systems are widely employed for balanced power delivery, using the neutral wire as a grounding point.
148
Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Classification of Systems-I01:26

Classification of Systems-I

290
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Power System Distribution01:25

Power System Distribution

310
Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
310
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

152
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...
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Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures.

Stefano Frizzo Stefenon1,2, Gurmail Singh3, Kin-Choong Yow3

  • 1Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy.

Sensors (Basel, Switzerland)
|July 9, 2022
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Summary

This study introduces Semi-ProtoPNet, a deep learning model for automated visual inspection of power distribution grids. It achieves 97.22% accuracy in classifying defective structures, enhancing grid reliability.

Keywords:
computer visionconvolutional neural networksdeep learninginsulator classificationpower grid inspection

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Power distribution grids are susceptible to environmental contamination, leading to electrical arcs and potential shutdowns.
  • Automated visual inspection using deep learning can improve the reliability of electrical power systems.

Purpose of the Study:

  • To propose the Semi-ProtoPNet deep learning model for classifying defective structures in power distribution networks.
  • To enhance the reliability of power grids through automated defect detection.

Main Methods:

  • Development of the Semi-ProtoPNet deep learning model, which omits convex optimization in its final dense layer.
  • Utilizing a negative reasoning process to reject incorrect image classifications, enabling analysis with limited, diverse datasets.

Main Results:

  • Semi-ProtoPNet achieved a classification accuracy of 97.22% for defective structures.
  • The model outperformed several established deep learning architectures, including VGG, ResNet, DenseNet, and ProtoPNet variants.

Conclusions:

  • The Semi-ProtoPNet model offers a highly accurate and efficient solution for automated defect detection in power distribution grids.
  • Its unique approach to negative reasoning enhances its robustness in handling diverse image backgrounds, a common challenge in this field.