A proposed wavelet analysis based fault diagnosis scheme of power transformers using fault signatures and CT saturation Article Swipe
Amira M. Dahman
,
Adel A. Abou El‐Ela
,
Mohamed I. Zaki
,
Ragab A. El‐Sehiemy
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2025.105820
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2025.105820
Diagnosis of concealed internal faults within power transformer is a key for high grid reliability to ensure continuity of power supply to customers. One of the urgent situations of power transformer is the faults under CT saturation and the operation under inrush currents that lead to huge failure of fault identification of the power transformer. In this paper, a fault identification scheme is designed using details and approximate coefficients obtained by discreet wavelet transform applied to a differential current signal under different situations. Also, this paper considers the impact of transformer internal faults such as turn to earth and turn to turn faults, external faults, and inrush currents. The signature of processing differential current is employed for identifying these fault conditions since such fault has a distinct differential current signature. The simulation tests are performed on a 115/22 kV power transformer using ATP-EMTP real-time simulator. Different wavelet families are assessed to show that the optimum mother wavelet, db1, has high fault detection and classification performance. The proposed scheme is verified for transformer energization conditions, and the influence of CT saturation is also considered in this study. Moreover, one of the most important proposed scheme features is simplicity with high lights aspects toward all fault conditions and fault types at different fault location and different fault resistances. Intensive simulation results are obtained to prove the improved selectivity and sensitivity of the proposed scheme for identifying internal transformer faults. Furthermore, sensitivity analysis is not only conducted in terms of transformer loading and fault resistance variation, but transformer scalability study is also verified. Finally, to evaluate the performance of the proposed scheme, an assessment study is adopted to show the accuracy and reliability of differential protection scheme.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.rineng.2025.105820
- OA Status
- gold
- Cited By
- 2
- References
- 49
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4411442677Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.rineng.2025.105820Digital Object Identifier
- Title
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A proposed wavelet analysis based fault diagnosis scheme of power transformers using fault signatures and CT saturationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-19Full publication date if available
- Authors
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Amira M. Dahman, Adel A. Abou El‐Ela, Mohamed I. Zaki, Ragab A. El‐SehiemyList of authors in order
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https://doi.org/10.1016/j.rineng.2025.105820Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.rineng.2025.105820Direct OA link when available
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Wavelet, Fault (geology), Computer science, Electronic engineering, Pattern recognition (psychology), Engineering, Geology, Seismology, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.wavelet | 72, 146 |
| abstract_inverted_index.ATP-EMTP | 142 |
| abstract_inverted_index.Finally, | 260 |
| abstract_inverted_index.accuracy | 277 |
| abstract_inverted_index.analysis | 239 |
| abstract_inverted_index.assessed | 149 |
| abstract_inverted_index.currents | 42 |
| abstract_inverted_index.designed | 63 |
| abstract_inverted_index.discreet | 71 |
| abstract_inverted_index.distinct | 126 |
| abstract_inverted_index.employed | 115 |
| abstract_inverted_index.evaluate | 262 |
| abstract_inverted_index.external | 103 |
| abstract_inverted_index.families | 147 |
| abstract_inverted_index.features | 194 |
| abstract_inverted_index.improved | 224 |
| abstract_inverted_index.internal | 3, 91, 234 |
| abstract_inverted_index.location | 211 |
| abstract_inverted_index.obtained | 69, 220 |
| abstract_inverted_index.proposed | 166, 192, 230, 267 |
| abstract_inverted_index.verified | 169 |
| abstract_inverted_index.wavelet, | 156 |
| abstract_inverted_index.Diagnosis | 0 |
| abstract_inverted_index.Different | 145 |
| abstract_inverted_index.Intensive | 216 |
| abstract_inverted_index.Moreover, | 186 |
| abstract_inverted_index.concealed | 2 |
| abstract_inverted_index.conducted | 243 |
| abstract_inverted_index.considers | 86 |
| abstract_inverted_index.currents. | 107 |
| abstract_inverted_index.detection | 161 |
| abstract_inverted_index.different | 81, 209, 213 |
| abstract_inverted_index.important | 191 |
| abstract_inverted_index.influence | 176 |
| abstract_inverted_index.operation | 39 |
| abstract_inverted_index.performed | 134 |
| abstract_inverted_index.real-time | 143 |
| abstract_inverted_index.signature | 109 |
| abstract_inverted_index.transform | 73 |
| abstract_inverted_index.verified. | 259 |
| abstract_inverted_index.assessment | 270 |
| abstract_inverted_index.conditions | 120, 204 |
| abstract_inverted_index.considered | 182 |
| abstract_inverted_index.continuity | 17 |
| abstract_inverted_index.customers. | 22 |
| abstract_inverted_index.processing | 111 |
| abstract_inverted_index.protection | 282 |
| abstract_inverted_index.resistance | 251 |
| abstract_inverted_index.saturation | 36, 179 |
| abstract_inverted_index.signature. | 129 |
| abstract_inverted_index.simplicity | 196 |
| abstract_inverted_index.simulation | 131, 217 |
| abstract_inverted_index.simulator. | 144 |
| abstract_inverted_index.situations | 27 |
| abstract_inverted_index.variation, | 252 |
| abstract_inverted_index.approximate | 67 |
| abstract_inverted_index.conditions, | 173 |
| abstract_inverted_index.identifying | 117, 233 |
| abstract_inverted_index.performance | 264 |
| abstract_inverted_index.reliability | 14, 279 |
| abstract_inverted_index.scalability | 255 |
| abstract_inverted_index.selectivity | 225 |
| abstract_inverted_index.sensitivity | 227, 238 |
| abstract_inverted_index.situations. | 82 |
| abstract_inverted_index.transformer | 7, 30, 90, 140, 171, 235, 247, 254 |
| abstract_inverted_index.Furthermore, | 237 |
| abstract_inverted_index.coefficients | 68 |
| abstract_inverted_index.differential | 77, 112, 127, 281 |
| abstract_inverted_index.energization | 172 |
| abstract_inverted_index.performance. | 164 |
| abstract_inverted_index.resistances. | 215 |
| abstract_inverted_index.transformer. | 54 |
| abstract_inverted_index.classification | 163 |
| abstract_inverted_index.identification | 50, 60 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.89476026 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |