Vibration Monitoring of Converter Transformer by Simplified permutation Entropy

Zhang, Xin; Huang, Ronghui; Zhou, Dongxu; Wang, Fenghua

doi:10.1109/cac.2018.8623221

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…The calculation equation is as follows: C e = X p X R . (13) 4 Kurtosis factor K 4 . Kurtosis factor is an expression of the degree of waveform smoothing and is used to describe the distribution of variables.…”

Section: Time Domain Indexes (Tdi)mentioning

confidence: 99%

“…Meanwhile, some scroungers will monitor producers for a long time always be ready to compete for food in order to improve the success of their own predation. 4 When an individual in a sparrow population finds a predator, it will send an alarm signal by chirping. Sparrows at the periphery of the population will move quickly to a safe area to gain a better position, while sparrows in the center will wander randomly and tend to approach other individuals.…”

Section: Sparrow Search Algorithm (Ssa) 421 Principle Of the Algorithmmentioning

confidence: 99%

“…When a predator appears, the sparrows that give alarm signals usually account for 10-20% of the entire population, and their initial position is generated randomly. According to rule 4 , the behavior of individuals in the population at this time can be expressed as…”

Section: Mathematical Model Of the Algorithmmentioning

confidence: 99%

“…In addition, research has consistently shown that there is an AC-DC complex electromagnetic field inside the converter transformer during its operation state. Operation state identification of converter transformers can be adversely affected by these problems, making it technically challenging [3,4]. Therefore, there is an urgent need to study an effective method for operation state identification of converter transformers.…”

Section: Introductionmentioning

confidence: 99%

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Operation State Identification Method for Converter Transformers Based on Vibration Detection Technology and Deep Belief Network Optimization Algorithm

Zhang

Xiao

et al. 2021

Actuators

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The converter transformer is a special power transformer that connects the converter bridge to the AC system in the HVDC transmission system. Due to the special structure of the converter transformer, it is necessary to test its operation state during its manufacture and processing to ensure the safety of its future connection to the grid. Numerous studies have shown that vibration signals in transformers can reflect their operating state. Therefore, in order to achieve an effective identification of the operation state of the converter transformer, this paper proposes a method for identifying the operation state of the converter transformer based on vibration detection technology and a deep belief network optimization algorithm. This paper firstly describes the background, principle and application of vibration detection technology, using vibration measurement systems with piezoelectric acceleration sensors, piezoelectric actuators and data acquisition instruments to collect vibration signals at different measurement points on the converter transformer in states of no-load and on-load. By analyzing the time-frequency characteristics of the vibration signals, fast Fourier transform (FFT), wavelet packet decomposition (WPD) and time domain indexes (TDI) are combined into a fused feature extraction method to extract the eigenvalues of the vibration signals, so that the fused eigenvectors of the signals can be constructed. Considering the excellent performance of deep learning in classification, the deep belief network is used to classify the signals’ eigenvectors. To effectively improve the network classification efficiency, the sparrow search algorithm was introduced to build a mathematical model based on the behavioral characteristics of sparrow populations and combine the model with a deep belief network, so as to achieve adaptive parameter optimization of the network and accurate classification of the signals’ eigenvectors. The proposed method is applied to a 500 kV converter transformer for experimental verification. The experimental results show that the fused feature extraction method was able to fully extract the features of the vibration signal, and the deep belief network optimization algorithm had higher classification accuracy and better operational efficiency, and was able to effectively achieve accurate identification of the operation state of the converter transformer. In addition, the method achieved a precision response to the detection results of the vibration sensors, contributing to future improvements in converter transformer manufacturing technology.

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Section: Time Domain Indexes (Tdi)mentioning

confidence: 99%

Section: Sparrow Search Algorithm (Ssa) 421 Principle Of the Algorithmmentioning

confidence: 99%

Section: Mathematical Model Of the Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Operation State Identification Method for Converter Transformers Based on Vibration Detection Technology and Deep Belief Network Optimization Algorithm

Zhang

Xiao

et al. 2021

Actuators

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show abstract

Improved empirical wavelet transform (EWT) and its application in non-stationary vibration signal of transformer

Qiu

Jin

et al. 2022

Sci Rep

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The resonant frequency of the transformer contains information related to its structure. It is easier to identify the resonance frequency in the vibration signal during the hammer test and power on than in the operation of the transformer, because the vibration caused by the load current does not need to be considered during the hammer test and power on. Therefore, an analysis method with simple calculation, fast calculation speed and easy real-time monitoring is needed to deal with these two non-stationary vibrations. Vibration monitoring can understand the health status of transformer in real time, improve the reliability of power supply and give early warning in the early stage of faults. A new frequency domain segmentation method is proposed in this paper. This method can effectively process the vibration signal of transformer and identify its resonant frequency. Eleven different load states are set on the transformer. The method proposed in this paper can extract the resonant frequency of the transformer from the hammering test signal. Compared with the original empirical wavelet transform method, this method can divide the frequency domain more effectively, has higher time–frequency resolution, and the running time of the modified method is shortened from 80 to 2 s. The universality of this method is proved by experiments on three different types of transformers.

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AC Side Current Harmonics Measurement and Analysis of UHVDC Converter Transformer

Zhao

Liu³

et al. 2021

Electric Power Components and Systems

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Vibration Monitoring of Converter Transformer by Simplified permutation Entropy

Cited by 3 publications

References 7 publications

Operation State Identification Method for Converter Transformers Based on Vibration Detection Technology and Deep Belief Network Optimization Algorithm

Operation State Identification Method for Converter Transformers Based on Vibration Detection Technology and Deep Belief Network Optimization Algorithm

Improved empirical wavelet transform (EWT) and its application in non-stationary vibration signal of transformer

AC Side Current Harmonics Measurement and Analysis of UHVDC Converter Transformer

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