Acoustic Multi-Parameter Early Warning Method for Transformer DC Bias State

Zhou, Yuhao; Wang, Bowen

doi:10.3390/s22082906

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…Among them, acoustic diagnosis technology has been extensively studied by experts and scholars due to its advantages of non-contact testing, no electromagnetic interference, and no impact on the power system. In fact, when mechanical defects [6][7][8], DC bias defects [9,10], or discharge defects [11,12] occur in power equipment, they make a sound that is obviously different from normal operation. Based on this, experts and scholars have conducted a lot of research on the diagnosis of abnormal acoustic signals, which focus on the preprocessing of sound signals [13][14][15], acoustic feature extraction [16,17], classifier design [18][19][20], etc.…”

Section: Introductionmentioning

confidence: 99%

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Topology Optimization Design Method for Acoustic Imaging Array of Power Equipment

Xiong,

Zha,

Pei

et al. 2024

Sensors

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Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array topology. The acoustic frequency domain characteristics of typical power equipment are elaborately sorted out. After that, the cut-off frequencies of acoustic imaging instruments are determined, to meet the needs of the full bandwidth test requirements. Through a simulation calculation, the circular array is demonstrated to be the optimal shape. And the design parameters affect the imaging performance of the array to varying degrees, indicating that it is difficult to obtain the optimal array topology by an exhaustive method. Aimed at the complex working conditions of power equipment, a topology optimization design method of an acoustic imaging array for power equipment is proposed, and the global optimal solution of microphone array topology is obtained. Compared with the original array, the imaging performance of the improved LF and HF array is promoted by 54% and 49%, respectively. Combined with the simulation analysis and laboratory test, it is verified that the improved array can not only accurately locate the single sound source but also accurately identify the main sound source from the interference of the contiguous sound source.

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Section: Introductionmentioning

confidence: 99%

“…According to Nyquist's law, the array aperture D should not be less than twice the maximum wavelength λ max corresponding to the lower cut-off frequency f min . It can be expressed in Equation (9).…”

mentioning

confidence: 99%

Topology Optimization Design Method for Acoustic Imaging Array of Power Equipment

Xiong,

Zha,

Pei

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Li et al established an engineering model of the magnetic flux density and electromagnetic force density on transformer core discontinuities, and then analyzed the important sources of transformer vibration and noise [15]. By comparing the acoustical parameters of the core model with the 500 kV transformer, Zhou et al selected three typical parameters for DC bias state acoustic warning, and clear parameter warning thresholds were given [16]. He et al proposed a method to evaluate the effectiveness of transformer online monitoring data under DC bias driven by monitoring data under different working conditions [17].…”

Section: Introductionmentioning

confidence: 99%

Measurement of Magnetic and Magnetostrictive Characteristics of Transformer Core Based on Triaxial Strain Gauge and B-H Vector Sensor

Wang,

Fan,

et al. 2023

Sensors

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As is well known, the magnetostrictive phenomenon of electrical steel sheet is the main source of electricity in equipment such as transformers. The magnetostrictive characteristic of the actual transformer core is more complicated than that of single-sheet steel. The magnetostriction phenomenon of the transformer core cannot be fully understood by studying only a single piece of electrical steel, so it is necessary to study the local magnetic characteristics of the transformer directly. In this paper, two-limb, one-phase transformer core with a multi-step-lap construction was assembled, a laminated magnetostrictive testing system based on triaxial strain gauges was built, and the local magnetic characteristics were studied using a self-developed B-H vector sensor. The magnetostrictive and magnetic properties in different local regions were measured and analyzed under several magnetization patterns, and the influence of DC bias on the magnetostrictive property of the corner, yoke, and limb of the core was investigated. The influence of the position of the clamp on the magnetostriction of the transformer core was also studied. The magnetostrictive strain of the single sheet and laminated core was compared and discussed. The results showed that the strain caused by the interaction between laminations in this area can be effectively reduced when clamping in the middle of the yoke.

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Contrast Estimation in Vibroacoustic Signals for Diagnosing Early Faults of Short-Circuited Turns in Transformers under Different Load Conditions

et al. 2022

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The transformer is one of the most important electrical machines in electrical systems. Its proper operation is fundamental for the distribution and transmission of electrical energy. During its service life, it is under continuous electrical and mechanical stresses that can produce diverse types of damage. Among them, short-circuited turns (SCTs) in the windings are one of the main causes of the transformer fault; therefore, their detection in an early stage can help to increase the transformer life and reduce the maintenance costs. In this regard, this paper proposes a signal processing-based methodology to detect early SCTs (i.e., damage of low severity) through the analysis of vibroacoustic signals in steady state under different load conditions, i.e., no load, linear load, nonlinear load, and both linear and nonlinear loads, where the transformer is adapted to emulate different conditions, i.e., healthy (0 SCTs) and with damage of low severity (1 and 2 SCTs). In the signal processing stage, the contrast index is analyzed as a fault indicator, where the Unser and Tamura definitions are tested. For the automatic classification of the obtained indices, an artificial neural network is used. It showed better results than the ones provided by a support vector machine. Results demonstrate that the contrast estimation is suitable as a fault indicator for all the load conditions since 89.78% of accuracy is obtained if the Unser definition is used.

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Acoustic Multi-Parameter Early Warning Method for Transformer DC Bias State

Cited by 3 publications

References 28 publications

Topology Optimization Design Method for Acoustic Imaging Array of Power Equipment

Topology Optimization Design Method for Acoustic Imaging Array of Power Equipment

Measurement of Magnetic and Magnetostrictive Characteristics of Transformer Core Based on Triaxial Strain Gauge and B-H Vector Sensor

Contrast Estimation in Vibroacoustic Signals for Diagnosing Early Faults of Short-Circuited Turns in Transformers under Different Load Conditions

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