Operation State Recognition of Renewable Energy Unit Based on SSAE and Improved KNN Algorithm
Quickly recognizing the real-time operating states will be helpful to identify the instantaneous and permanent power loss of the renewable energy station, so as to realize the continuous operation under the influence of the instantaneous disturbances caused by faults. This paper proposes a state rec...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10185564/ |
| _version_ | 1797772611972759552 |
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| author | Linjun Shi Tao Dai Wenjie Lao Feng Wu Keman Lin Kwang Y. Lee |
| author_facet | Linjun Shi Tao Dai Wenjie Lao Feng Wu Keman Lin Kwang Y. Lee |
| author_sort | Linjun Shi |
| collection | DOAJ |
| description | Quickly recognizing the real-time operating states will be helpful to identify the instantaneous and permanent power loss of the renewable energy station, so as to realize the continuous operation under the influence of the instantaneous disturbances caused by faults. This paper proposes a state recognition method for renewable energy units based on sparse stacked auto-encoder (SSAE) feature extraction and improved k-nearest neighbor (KNN) algorithm. The characteristics of this method is that the electrical parameters of the unit port are collected directly without relying on the unit’s supervisory control and data acquisition (SCADA) system, whose acquisition speed is too slow to meet the recognition accuracy requirement, and that the unit operation states can be recognized quickly and accurately. Firstly, operation states of renewable energy unit are divided, and the framework for the unit’s state recognition is proposed. Moreover, improved strategies for state recognition of renewable energy unit are proposed. Finally, the power system analysis software package (PSASP) is used to obtain the electrical parameters of renewable energy units and the improved KNN algorithm is used to recognize operation states after extracting features based on SSAE. By comparing the method proposed with the traditional KNN algorithm, the effect of the proposed method for states recognition is shown to be the best, with an accuracy of 98.16% and computing time of 50ms. The results show the validity of the proposed method. |
| first_indexed | 2024-03-12T21:54:26Z |
| format | Article |
| id | doaj.art-bb04fc42b15b477a9025a85eae225275 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T21:54:26Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bb04fc42b15b477a9025a85eae2252752023-07-25T23:00:15ZengIEEEIEEE Access2169-35362023-01-0111741917420010.1109/ACCESS.2023.329653310185564Operation State Recognition of Renewable Energy Unit Based on SSAE and Improved KNN AlgorithmLinjun Shi0Tao Dai1https://orcid.org/0009-0007-8661-0799Wenjie Lao2https://orcid.org/0000-0002-2552-798XFeng Wu3https://orcid.org/0000-0003-3011-9902Keman Lin4https://orcid.org/0000-0002-6749-7205Kwang Y. Lee5https://orcid.org/0000-0002-9965-9117College of Energy and Electrical Engineering, Hohai University, Nanjing, ChinaCollege of Energy and Electrical Engineering, Hohai University, Nanjing, ChinaCollege of Energy and Electrical Engineering, Hohai University, Nanjing, ChinaCollege of Energy and Electrical Engineering, Hohai University, Nanjing, ChinaCollege of Energy and Electrical Engineering, Hohai University, Nanjing, ChinaDepartment of Electrical and Computer Engineering, Baylor University, Waco, TX, USAQuickly recognizing the real-time operating states will be helpful to identify the instantaneous and permanent power loss of the renewable energy station, so as to realize the continuous operation under the influence of the instantaneous disturbances caused by faults. This paper proposes a state recognition method for renewable energy units based on sparse stacked auto-encoder (SSAE) feature extraction and improved k-nearest neighbor (KNN) algorithm. The characteristics of this method is that the electrical parameters of the unit port are collected directly without relying on the unit’s supervisory control and data acquisition (SCADA) system, whose acquisition speed is too slow to meet the recognition accuracy requirement, and that the unit operation states can be recognized quickly and accurately. Firstly, operation states of renewable energy unit are divided, and the framework for the unit’s state recognition is proposed. Moreover, improved strategies for state recognition of renewable energy unit are proposed. Finally, the power system analysis software package (PSASP) is used to obtain the electrical parameters of renewable energy units and the improved KNN algorithm is used to recognize operation states after extracting features based on SSAE. By comparing the method proposed with the traditional KNN algorithm, the effect of the proposed method for states recognition is shown to be the best, with an accuracy of 98.16% and computing time of 50ms. The results show the validity of the proposed method.https://ieeexplore.ieee.org/document/10185564/Feature extractionimproved k-nearest neighbor algorithmrenewable energysparse stack auto-encoderstate recognition |
| spellingShingle | Linjun Shi Tao Dai Wenjie Lao Feng Wu Keman Lin Kwang Y. Lee Operation State Recognition of Renewable Energy Unit Based on SSAE and Improved KNN Algorithm IEEE Access Feature extraction improved k-nearest neighbor algorithm renewable energy sparse stack auto-encoder state recognition |
| title | Operation State Recognition of Renewable Energy Unit Based on SSAE and Improved KNN Algorithm |
| title_full | Operation State Recognition of Renewable Energy Unit Based on SSAE and Improved KNN Algorithm |
| title_fullStr | Operation State Recognition of Renewable Energy Unit Based on SSAE and Improved KNN Algorithm |
| title_full_unstemmed | Operation State Recognition of Renewable Energy Unit Based on SSAE and Improved KNN Algorithm |
| title_short | Operation State Recognition of Renewable Energy Unit Based on SSAE and Improved KNN Algorithm |
| title_sort | operation state recognition of renewable energy unit based on ssae and improved knn algorithm |
| topic | Feature extraction improved k-nearest neighbor algorithm renewable energy sparse stack auto-encoder state recognition |
| url | https://ieeexplore.ieee.org/document/10185564/ |
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