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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...
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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.
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Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
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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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Efficient Multi-Sound Source Localization Algorithm for Transformer Faults Based on Polyphase Filters.

Hualiang Zhou1,2, Zhantao Su1,2, Yuxuan Huang3

  • 1NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
Summary
This summary is machine-generated.

Early detection of power transformer faults is crucial. This study introduces a new sound localization algorithm using polyphase filters and sum-difference monopulse techniques for accurate multi-fault identification, improving transformer maintenance.

Keywords:
multi-source localizationmulti-source separationpolyphase filtersum-difference monopulsetransformer

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Area of Science:

  • Electrical Engineering and Power Systems
  • Acoustic Signal Processing
  • Fault Diagnosis

Background:

  • Power transformer faults, responsible for over 30% of issues, necessitate early detection.
  • Abnormal sound analysis and microphone array-based sound source localization aid in troubleshooting.
  • Existing localization methods struggle with accuracy, simplicity, and multiple sound sources.

Purpose of the Study:

  • To develop a robust multi-sound source localization algorithm for power transformer faults.
  • To enhance the accuracy and reduce the complexity of fault localization in power systems.
  • To improve the efficiency of operation and maintenance for power transformers.

Main Methods:

  • Utilized polyphase filters to divide transformer acoustic signals into subbands for multi-source separation and reduced sampling rates.
  • Applied noise suppression and created sum-difference beams from time-domain subband signals.
  • Employed Fast Fourier Transform (FFT) for frequency-domain transformation and sum-difference monopulse angle measurement for high-precision localization.

Main Results:

  • The proposed algorithm achieved higher localization accuracy compared to existing methods.
  • Demonstrated reduced computational complexity, even with real-world microphone array amplitude-phase errors.
  • Successfully enabled high-precision localization of specific transformer faults in the frequency domain.

Conclusions:

  • The novel algorithm effectively addresses limitations of current sound source localization techniques for transformer fault diagnosis.
  • Its accuracy, simplicity, and efficiency make it suitable for practical applications in power system maintenance.
  • Facilitates early targeting of fault sources, significantly enhancing transformer operational safety and maintenance efficiency.