Condition Monitoring of Distribution Transformer's Mechanical Parts Using Sweep Frequency Response Analysis (SFRA)

Ghani, Sulaiman Ab; Thayoob, Yasmin Hanum Md; Ghazali, Young Zaidey Yang; Khiar, Mohd Shahril Ahmad; Chairul, Imran Sutan

doi:10.1016/j.proeng.2013.12.208

Cited by 17 publications

(5 citation statements)

References 5 publications

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“…The standard SFRA method [19] involves comparing the frequency responses of consecutive periodically conducted tests. If the two compared frequency responses do not differ, then the longitudinal R, L and C parameters did not change signi cantly, indicating that no deformations occurred in the meantime.…”

Section: Sfra and Indicatorsmentioning

confidence: 99%

Machine Learning for Power Transformer Sfra Based Fault Detection

Bjelić

Brkovic

Žarković

et al. 2022

Preprint

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This paper presents machine learning methods for health assessment of power transformer based on sweep frequency response analysis. The paper presents an overview of monitoring and diagnostics based on statistical Sweep Frequency Response Analysis (SFRA) based indicators that are used to evaluate the state of the power transformer. Experimental data obtained from power transformers with internal short-circuit faults is used as a database for applying machine learning. Machine learning is implemented to achieve more precise asset management and condition-based maintenance. Unsupervised machine learning was applied through the k-means cluster method for classifying and dividing the examined power transformer state into groups with similar state and probability of failure. Artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) as part of supervised machine learning are created in order to detect fault severity in tested power transformers of different lifetime. The presented machine learning methods can be used to improve health assessment of power transformers.

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Section: Sfra and Indicatorsmentioning

confidence: 99%

Machine Learning for Power Transformer Sfra Based Fault Detection

Bjelić

Brkovic

Žarković

et al. 2022

Preprint

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“…Perubahan tanggapan frekuensi yang diukur dengan teknik Sweep Frequency Response Analysis (SFRA) ini mengidentifikasi perubahan fisik di dalam transformator. Demikian juga berturut.turut dilakukan pada belitan transformator sisi sekunder [9].…”

Section: Pendahuluanunclassified

Evaluasi Kondisi Belitan Generator Transformers (GT) Dengan Sweep Frequency Response Analysis

2022

rele.n.a.elektrikal.dan.n.a.j.teknik.n.a

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Pengaruh gaya elektromagnetig yang besar dan terus-menerus, berkibat belitan transformator daya cenderung mengalami perubahan. Mencegah terjadinya gerakan belitan transformator daya berkembang yang mengarah pada kerusakan utama sangat penting, oleh karena itu pendeteksian dan perawatan transformator perlu dilakukan secara rutin agar transformator dapat beroperasi dengan handal. Metode Sweep Frequency Response Analysis (SFRA) dikembangkan untuk mendiagnosis kondisi mekanik belitan transformator Metode pendeteksian kondisi belitan transformator diperoleh hasil yang akurat. Hasil yang diperoleh semua kondisi belitan dalam keadaan baik tanpa perlu adanya perbaikan.

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“…Insulation resistance (IR), transformer turn ratio (TTR), and frequency response analysis (FRA) are frequently used to assess the health of transformers [10]. Short circuits can occur across any two lines or when the neutral point of the transformer is grounded between any lines [11]. Figure 1(a) shows an impact of continuous overcurrent causing transformer to become short circuit.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of electrical test methods on detecting transformer faults

Ghani

Khiar

Chairul

et al. 2022

IJEECS

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Condition monitoring of distribution and power transformers is of utmost importance to utilities due to cost effectiveness concerns. The common faults that occur in transformers are short circuits and winding deformation. To date, there are many established test methods used to detect these faults. In this study, three test methods (insulation resistance (IR), transformer turn ratio (TTR), and frequency response analysis (FRA)) were compared to assess their effectiveness in detecting short circuit and winding deformation in a three-phase transformer. Based on the results, the three test methods were found to be capable of detecting short circuits. However, only TTR and FRA can detect winding deformation, and FRA can further indicate which phase is faulty. Therefore, it is concluded that FRA is more effective in detecting short circuit and winding deformation of a three-phase transformer.

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Condition Monitoring of Distribution Transformer's Mechanical Parts Using Sweep Frequency Response Analysis (SFRA)

Cited by 17 publications

References 5 publications

Machine Learning for Power Transformer Sfra Based Fault Detection

Machine Learning for Power Transformer Sfra Based Fault Detection

Evaluasi Kondisi Belitan Generator Transformers (GT) Dengan Sweep Frequency Response Analysis

Comparative study of electrical test methods on detecting transformer faults

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