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Related Concept Videos

Fault Types01:18

Fault Types

400
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
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Differential Leveling01:12

Differential Leveling

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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Errors in Global Positioning System01:26

Errors in Global Positioning System

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Power Factor Correction01:20

Power Factor Correction

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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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Radial System Protection01:23

Radial System Protection

420
Radial systems employ time-delay overcurrent relays to reduce load interruptions. When a fault occurs, the nearest breaker opens first, while upstream breakers remain closed due to longer delay settings. This approach ensures minimal disruption to the rest of the system.
In a radial system with a fault downstream of the third breaker, ideally, only the third breaker will open, isolating the fault and interrupting the load connected beyond it. The second breaker has a longer delay setting,...
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Related Experiment Video

Updated: Jan 18, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Enhanced YOLOv11 Framework for Accurate Multi-Fault Detection in UAV Photovoltaic Inspection.

Shufeng Meng1, Yang Yue2, Tianxu Xu1

  • 1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

This study introduces an improved YOLOv11 framework for detecting photovoltaic (PV) anomalies like stains, defects, and snow. The enhanced model accurately classifies all three fault types simultaneously, boosting energy yield and enabling intelligent PV inspection.

Keywords:
Grad-CAMHSV color modelYOLOv11mix structure blockoutlook attentionphotovoltaic fault detection

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Engineering
  • Materials Science

Background:

  • Photovoltaic (PV) energy yield is significantly reduced by common anomalies such as stains, defects, and snow accumulation.
  • Existing detection methods often compromise accuracy for speed and cannot simultaneously identify all three major PV fault types.
  • There is a critical need for advanced, accurate, and simultaneous detection systems for comprehensive PV anomaly assessment.

Purpose of the Study:

  • To develop an enhanced object detection framework capable of accurately and simultaneously classifying three prevalent photovoltaic anomalies: stains, defects, and snow.
  • To improve the accuracy and efficiency of photovoltaic (PV) fault detection for enhanced energy yield and system reliability.
  • To introduce novel deep learning components for superior feature extraction and contextual understanding in PV anomaly identification.

Main Methods:

  • An enhanced YOLOv11 framework was developed, incorporating the hue-saturation-value (HSV) color model for robust color feature extraction.
  • An outlook attention module was integrated into the backbone for precise micro-defect boundary delineation.
  • A mix structure block was employed in the detection head for improved recognition of small objects by encoding global context and fine-grained details.
  • The bounded sigmoid linear unit (B-SiLU) activation function was utilized to optimize gradient flow and feature discrimination.

Main Results:

  • The enhanced YOLOv11 framework demonstrated significant improvements in overall mean average precision (mAP), increasing by 1.8%.
  • Specific accuracy gains were observed for defect (2.2%), stain (3.3%), and snow (0.8%) detection.
  • Gradient-weighted class activation mapping (Grad-CAM) visualizations confirmed the model's focused attention on relevant fault regions.

Conclusions:

  • The proposed enhanced YOLOv11 framework provides a reliable and accurate solution for intelligent PV inspection and early fault detection.
  • The integration of advanced deep learning techniques, including HSV color modeling and attention mechanisms, significantly boosts the performance of PV anomaly classification.
  • This research contributes to the advancement of automated PV system monitoring, leading to improved operational efficiency and energy production.