Frequency prediction after disturbance of grid-connected wind power systems based on WOA and Attention-LSTM
Under the guidance of the goal of the “carbon peaking and carbon neutrality”, due to the high proportion of renewable energy and the high proportion of power electronic equipment, the power system will bring strong randomness, low inertia and other characteristics, causing a large number of frequenc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723002214 |
| _version_ | 1797821587724959744 |
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| author | Xin Li Na Xiao Bo Peng Zishuo Ai Yi Wang |
| author_facet | Xin Li Na Xiao Bo Peng Zishuo Ai Yi Wang |
| author_sort | Xin Li |
| collection | DOAJ |
| description | Under the guidance of the goal of the “carbon peaking and carbon neutrality”, due to the high proportion of renewable energy and the high proportion of power electronic equipment, the power system will bring strong randomness, low inertia and other characteristics, causing a large number of frequency stability problems. In order to solve the problems of traditional power system frequency prediction methods, such as difficulty in modeling and poor prediction accuracy, and to determine whether the frequency stability problem will occur after the wind power grid-connected system is disturbed, the Lasso algorithm is first used to reduce the dimension of the input data, and then the attention mechanism based long and short memory (attention LSTM) neural network is used to predict the output frequency curve, The network parameters are optimized by the global search algorithm Whale Optimization Algorithm (WOA). Finally, the accuracy of the algorithm is verified by taking the improved wind power grid-connected 39 bus as an example. The results show that this method has a good guiding significance for evaluating the frequency stability of the wind power grid-connected system after interference, and can effectively predict the frequency curve of the system after interference, and evaluate the frequency stability of the system after topology change. |
| first_indexed | 2024-03-13T09:54:56Z |
| format | Article |
| id | doaj.art-c4d0b3359a774193b462024f9472a36a |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-13T09:54:56Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-c4d0b3359a774193b462024f9472a36a2023-05-24T04:20:33ZengElsevierEnergy Reports2352-48472023-04-019208216Frequency prediction after disturbance of grid-connected wind power systems based on WOA and Attention-LSTMXin Li0Na Xiao1Bo Peng2Zishuo Ai3Yi Wang4Information and Communication Branch of State Grid Jibei Electric Power Co., Ltd., Beijing, 10054, ChinaInformation and Communication Branch of State Grid Jibei Electric Power Co., Ltd., Beijing, 10054, ChinaInformation and Communication Branch of State Grid Jibei Electric Power Co., Ltd., Beijing, 10054, ChinaNorth China Electric Power University, Baoding, 071003, China; Corresponding author.North China Electric Power University, Baoding, 071003, ChinaUnder the guidance of the goal of the “carbon peaking and carbon neutrality”, due to the high proportion of renewable energy and the high proportion of power electronic equipment, the power system will bring strong randomness, low inertia and other characteristics, causing a large number of frequency stability problems. In order to solve the problems of traditional power system frequency prediction methods, such as difficulty in modeling and poor prediction accuracy, and to determine whether the frequency stability problem will occur after the wind power grid-connected system is disturbed, the Lasso algorithm is first used to reduce the dimension of the input data, and then the attention mechanism based long and short memory (attention LSTM) neural network is used to predict the output frequency curve, The network parameters are optimized by the global search algorithm Whale Optimization Algorithm (WOA). Finally, the accuracy of the algorithm is verified by taking the improved wind power grid-connected 39 bus as an example. The results show that this method has a good guiding significance for evaluating the frequency stability of the wind power grid-connected system after interference, and can effectively predict the frequency curve of the system after interference, and evaluate the frequency stability of the system after topology change.http://www.sciencedirect.com/science/article/pii/S2352484723002214Frequency predictionLasso algorithmWhale optimization algorithmAttention mechanismsLong short-term memory neural network |
| spellingShingle | Xin Li Na Xiao Bo Peng Zishuo Ai Yi Wang Frequency prediction after disturbance of grid-connected wind power systems based on WOA and Attention-LSTM Energy Reports Frequency prediction Lasso algorithm Whale optimization algorithm Attention mechanisms Long short-term memory neural network |
| title | Frequency prediction after disturbance of grid-connected wind power systems based on WOA and Attention-LSTM |
| title_full | Frequency prediction after disturbance of grid-connected wind power systems based on WOA and Attention-LSTM |
| title_fullStr | Frequency prediction after disturbance of grid-connected wind power systems based on WOA and Attention-LSTM |
| title_full_unstemmed | Frequency prediction after disturbance of grid-connected wind power systems based on WOA and Attention-LSTM |
| title_short | Frequency prediction after disturbance of grid-connected wind power systems based on WOA and Attention-LSTM |
| title_sort | frequency prediction after disturbance of grid connected wind power systems based on woa and attention lstm |
| topic | Frequency prediction Lasso algorithm Whale optimization algorithm Attention mechanisms Long short-term memory neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723002214 |
| work_keys_str_mv | AT xinli frequencypredictionafterdisturbanceofgridconnectedwindpowersystemsbasedonwoaandattentionlstm AT naxiao frequencypredictionafterdisturbanceofgridconnectedwindpowersystemsbasedonwoaandattentionlstm AT bopeng frequencypredictionafterdisturbanceofgridconnectedwindpowersystemsbasedonwoaandattentionlstm AT zishuoai frequencypredictionafterdisturbanceofgridconnectedwindpowersystemsbasedonwoaandattentionlstm AT yiwang frequencypredictionafterdisturbanceofgridconnectedwindpowersystemsbasedonwoaandattentionlstm |