Vibration measurement based condition monitoring

Vibrations of the transformers are complex multi-physics phenomena that require a deep understanding of electromagnetic and mechanical principles. Their analysis can be used to assess the condition of the transformer in terms of mechanical fixation quality, buckling or ageing of the components. The article presents the 20 years of efforts of researchers in Xi\u27an Jiaotong University and The University of Queensland on transformer vibration characteristics and its application in the winding mechanical condition monitoring

Analysis on transformer vibration signal recognition based on convolutional neural network

In order to study the relationship between the transformer vibration and the operation state, the wavelet analysis method and the convolutional neural network method were used to analyze the transformer vibration signal. This paper proposes a transformer based on convolution neural network-based surface vibration signal feature extraction method. The result show that the convolution of neural network in different station transformer surface vibration signal classification has a lot of advantage, as the integration of feature extraction and classification recognition process together can effectively classify vibration signal recognition processing. This method is feasible for classification and identification by providing an accuracy value of 92.74 %. The future perspective of this research will focus on a generalized network model and parameters through experimentation for further investigation of accuracy and efficiency of this method

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

Exploring Statistical Index Criteria for Transformer Frequency Response Interpretation

Large power transformers are considered as the most expensive assets in power system network after hydro generators. Therefore, monitoring of such equipment needs to take special attention. Frequency Response Analysis (FRA) is one of the efficient methods to examine the mechanical condition of the transformer without opening the transformer tank. FRA is a comparative method, where the measured response is compared to the reference fingerprints. Therefore, interpretation of the FRA results needs to be done by an expert in the field. To overcome this problem, so that untrained personnel would be able to use FRA for transformer condition monitoring the interpretation of the frequency response should be based on standard or on some criteria. In this study, various statistical indices for frequency response results interpretation will be introduced and evaluated. Frequency responses of single-phase 0.4-1kVA transformers and three-phase transformers up to 40kVA are interpreted by statistical indices. Outcome of each indicator is discussed and the most reliable ones for FR interpretation are suggested. The simulation of inter-disk short circuit was performed by the rheostat connected in parallel with the winding of transformer. The voltage taps of the transformers were used in order to have the access to different percentage of the transformer winding. With the help of different voltage taps and different resistances in parallel, the two levels of critical values were found and advised to use

Transformer Fault Condition Prognosis Using Vibration Signals Over Cloud Environment

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8302497On-line monitoring and diagnosis of transformers have been investigated and discussed significantly in the last few decades. Vibration method is considered as one of the non-destructive and economical methods to explore transformer operating condition and evaluate transformer mechanical integrity and performance. However, transformer vibration and its evaluation criteria in transformer faulty condition are quite challenging and are not yet agreed upon. At the same time, with the advent of IoT facilities and services, it is expected that classical diagnosis techniques will be replaced with more powerful data-driven prognosis methods that can be used efficiently and effectively in smart monitoring. In this paper, we first discuss in detail an analytical approach to the transformer vibration modeling. Nevertheless, precise interpretation of transformer vibration signal through analytical models becomes unrealistic as higher harmonics are mixed with fundamental harmonics in vibration spectra. Therefore, as the next step, we aim to support the Industry 4.0 concept by utilizing the state-of-the-art machine learning and signal processing techniques to develop prognosis models of transformer operating condition based on vibration signals. Transformer turn-to-turn insulation deterioration and short circuit analysis as one the most important concerns in transformer operation is practically emulated and examined. Along with transformer short-circuit study, transformer over and under excitations are also studied and evaluated. Our constructed predictive models are able to detect transformer short-circuit fault in early stages using vibration signals before transformer catastrophic failure. Real-time information is transferred to the cloud system and results become accessible over any portable device