Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor
Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and classification of these...
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| Format: | Article |
| Language: | English |
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Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)
2020-04-01
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| Series: | Journal of Applied Sciences and Environmental Management |
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| Online Access: | https://www.ajol.info/index.php/jasem/article/view/195114 |
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| author | A.I. Abdullateef O.S. Fagbolagun M.F. Sanusi M.F. Akorede M.A. Afolayan |
| author_facet | A.I. Abdullateef O.S. Fagbolagun M.F. Sanusi M.F. Akorede M.A. Afolayan |
| author_sort | A.I. Abdullateef |
| collection | DOAJ |
| description |
Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis.
Keywords: induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transform
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| first_indexed | 2024-04-24T14:51:55Z |
| format | Article |
| id | doaj.art-eab62c58436f4c708fe8162f587c642d |
| institution | Directory Open Access Journal |
| issn | 2659-1502 2659-1499 |
| language | English |
| last_indexed | 2024-04-24T14:51:55Z |
| publishDate | 2020-04-01 |
| publisher | Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP) |
| record_format | Article |
| series | Journal of Applied Sciences and Environmental Management |
| spelling | doaj.art-eab62c58436f4c708fe8162f587c642d2024-04-02T19:49:36ZengJoint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)Journal of Applied Sciences and Environmental Management2659-15022659-14992020-04-0124310.4314/jasem.v24i3.3Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction MotorA.I. AbdullateefO.S. FagbolagunM.F. SanusiM.F. AkoredeM.A. Afolayan Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis. Keywords: induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transform https://www.ajol.info/index.php/jasem/article/view/195114induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transform |
| spellingShingle | A.I. Abdullateef O.S. Fagbolagun M.F. Sanusi M.F. Akorede M.A. Afolayan Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor Journal of Applied Sciences and Environmental Management induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transform |
| title | Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor |
| title_full | Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor |
| title_fullStr | Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor |
| title_full_unstemmed | Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor |
| title_short | Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor |
| title_sort | detection and classification of stator short circuit faults in three phase induction motor |
| topic | induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transform |
| url | https://www.ajol.info/index.php/jasem/article/view/195114 |
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