Long Short-Term Memory Networks Based Automatic Feature Extraction for Photovoltaic Array Fault Diagnosis
Photovoltaic (PV) array fault diagnosis is important because it helps reduce energy and revenue losses to PV system operators. It also reduces fire hazards and electric shocks caused by PV array faults. As a result, many machine-learning-based fault diagnosis techniques have been proposed in recent...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8660399/ |
| _version_ | 1818416007502364672 |
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| author | Albert Yaw Appiah Xinghua Zhang Ben Beklisi Kwame Ayawli Frimpong Kyeremeh |
| author_facet | Albert Yaw Appiah Xinghua Zhang Ben Beklisi Kwame Ayawli Frimpong Kyeremeh |
| author_sort | Albert Yaw Appiah |
| collection | DOAJ |
| description | Photovoltaic (PV) array fault diagnosis is important because it helps reduce energy and revenue losses to PV system operators. It also reduces fire hazards and electric shocks caused by PV array faults. As a result, many machine-learning-based fault diagnosis techniques have been proposed in recent times. Although the fault diagnosis accuracies associated with these techniques have been impressive, most machine learning algorithms rely on manual feature extraction, which is time consuming, expensive, and diagnostic expertise exacting. To address the problem of manual feature extraction, this paper proposes a new PV array fault diagnosis technique capable of automatically extracting features from raw data for PV array fault classification. The proposed technique utilizes long short-term memory networks, which is a deep learning algorithm, for feature extraction. The extracted features feed into a softmax regression classifier for fault diagnosis. The proposed technique exhibits high fault diagnosis accuracies on both noisy and noiseless data. In addition, the results of the proposed technique compare favorably with those of other techniques. It can, therefore, be inferred from the results that the proposed fault diagnosis technique offers an effective approach to automatically extract useful features from raw data and thus remove the need for the manual feature extraction. |
| first_indexed | 2024-12-14T11:44:02Z |
| format | Article |
| id | doaj.art-bdeb62ece0424ea6a6391a3e81fb92f4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:44:02Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bdeb62ece0424ea6a6391a3e81fb92f42022-12-21T23:02:42ZengIEEEIEEE Access2169-35362019-01-017300893010110.1109/ACCESS.2019.29029498660399Long Short-Term Memory Networks Based Automatic Feature Extraction for Photovoltaic Array Fault DiagnosisAlbert Yaw Appiah0https://orcid.org/0000-0002-2294-7516Xinghua Zhang1Ben Beklisi Kwame Ayawli2Frimpong Kyeremeh3College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, ChinaCollege of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, ChinaCollege of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, ChinaCollege of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, ChinaPhotovoltaic (PV) array fault diagnosis is important because it helps reduce energy and revenue losses to PV system operators. It also reduces fire hazards and electric shocks caused by PV array faults. As a result, many machine-learning-based fault diagnosis techniques have been proposed in recent times. Although the fault diagnosis accuracies associated with these techniques have been impressive, most machine learning algorithms rely on manual feature extraction, which is time consuming, expensive, and diagnostic expertise exacting. To address the problem of manual feature extraction, this paper proposes a new PV array fault diagnosis technique capable of automatically extracting features from raw data for PV array fault classification. The proposed technique utilizes long short-term memory networks, which is a deep learning algorithm, for feature extraction. The extracted features feed into a softmax regression classifier for fault diagnosis. The proposed technique exhibits high fault diagnosis accuracies on both noisy and noiseless data. In addition, the results of the proposed technique compare favorably with those of other techniques. It can, therefore, be inferred from the results that the proposed fault diagnosis technique offers an effective approach to automatically extract useful features from raw data and thus remove the need for the manual feature extraction.https://ieeexplore.ieee.org/document/8660399/Automatic feature extractionfault diagnosislong short-term memoryphotovoltaic arraysoftmax regression |
| spellingShingle | Albert Yaw Appiah Xinghua Zhang Ben Beklisi Kwame Ayawli Frimpong Kyeremeh Long Short-Term Memory Networks Based Automatic Feature Extraction for Photovoltaic Array Fault Diagnosis IEEE Access Automatic feature extraction fault diagnosis long short-term memory photovoltaic array softmax regression |
| title | Long Short-Term Memory Networks Based Automatic Feature Extraction for Photovoltaic Array Fault Diagnosis |
| title_full | Long Short-Term Memory Networks Based Automatic Feature Extraction for Photovoltaic Array Fault Diagnosis |
| title_fullStr | Long Short-Term Memory Networks Based Automatic Feature Extraction for Photovoltaic Array Fault Diagnosis |
| title_full_unstemmed | Long Short-Term Memory Networks Based Automatic Feature Extraction for Photovoltaic Array Fault Diagnosis |
| title_short | Long Short-Term Memory Networks Based Automatic Feature Extraction for Photovoltaic Array Fault Diagnosis |
| title_sort | long short term memory networks based automatic feature extraction for photovoltaic array fault diagnosis |
| topic | Automatic feature extraction fault diagnosis long short-term memory photovoltaic array softmax regression |
| url | https://ieeexplore.ieee.org/document/8660399/ |
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