Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

Huerta-Rosales, Jose R.; Granados-Lieberman, David; Amezquita-Sanchez, Juan P.; Camarena-Martinez, David; Valtierra-Rodríguez, Martín

doi:10.3390/math8040575

Cited by 12 publications

(11 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also applicable to transformers and is capable of describing the fault location [87]. In addition, the analysis method based on the vibration signal is also applicable for condition monitoring of transformer winding [108]. However, the collected vibration signals are always complicated and even chaotic due to various external factors (such as environmental vibrations, magnetostriction and oil pumps operations), making it difficult to fully and effectively utilize the information contained therein.…”

Section: Fault Diagnosis Of Transformermentioning

confidence: 99%

A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

Ding

Yao²,

et al. 2022

Sustainability

View full text Add to dashboard Cite

Nowadays, cold ironing technology has been demonstrated to be an effective solution to deal with the environmental and social problems brought by port ship emissions and relevant effects. The working states of cold ironing equipment, especially the key components such as circuit breakers, transformers and frequency converters, have a significant effect on the safety and reliability of the whole system. However, due to the harsh working environment of cold ironing equipment, they are prone to a high risk of failure. In this respect, fault diagnosis methods can play a significant role in detecting potential failure in time and guarantee the safe and reliable operation of the cold ironing system. In recent years, research on the fault diagnosis of a cold ironing system has been rapidly growing, and this paper aims to present a comprehensive review of this literature, with an emphasis on the fault diagnosis technology applied to the key components in a cold ironing system. This review classifies the literature according to the type of key component, and, for each special type of component, the fault diagnosis methods are further categorized and analyzed. This paper provides useful references for professionals and researchers working on the fault diagnosis of a cold ironing system and points out valuable research directions in the future.

show abstract

Section: Fault Diagnosis Of Transformermentioning

confidence: 99%

A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

Ding

Yao²,

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…Continuous wavelet transform (CWT) is a typical representative of wavelet transform, which introduces scaling factor and translation factor to achieve multi-scale analysis of signals. CWT has the characteristics of multi-resolution analysis, and the local characteristics of signals can be solved through reasonable selection of wavelet basis functions [52][53][54].…”

Section: Time-frequency Characteristics Of Vibration Signalsmentioning

confidence: 99%

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

Zhang

Xiao

et al. 2021

Actuators

View full text Add to dashboard Cite

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.

show abstract

“…Meanwhile, Bagheri et al [ 14 ] analyze the vibration spectra to detect short-circuit initiation. Even though the FT has presented good results, its performance decreases when the signal presents non-stationary events as monitored in the transformers [ 15 ]. In this regard, advanced signal processing has been introduced, specifically time-frequency domain techniques.…”

Section: Introductionmentioning

confidence: 99%

Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA

Huerta-Rosales

Granados-Lieberman

García-Pérez

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.

show abstract

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

Cited by 12 publications

References 44 publications

A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

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

Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA

Contact Info

Product

Resources

About