A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control
Fast and accurate prediction and control of power system dynamic frequency after disturbance is essential to enhance power system stability. Machine learning methods have great potential in harnessing data for online application with accurate predictions. This paper proposes a two-stage novel transf...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9435326/ |
| _version_ | 1818617884960620544 |
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| author | Jian Xie Wei Sun |
| author_facet | Jian Xie Wei Sun |
| author_sort | Jian Xie |
| collection | DOAJ |
| description | Fast and accurate prediction and control of power system dynamic frequency after disturbance is essential to enhance power system stability. Machine learning methods have great potential in harnessing data for online application with accurate predictions. This paper proposes a two-stage novel transfer and deep learning-based method to predict power system dynamic frequency after disturbance and provide optimal event-based load shedding strategy to maintain system frequency. The proposed deep learning model combines convolutional neural network (CNN) and long short-term memory (LSTM) network to harness both spatial and temporal measurements in the input data, through a four-dimensional (4-D) tensor input construction process including, 1) capture system network topology information and critical measurements from different time intervals; 2) compute a multi-dimensional electric distance matrix and reduce to a 2-D plane which can describe the system nodal distribution; 3) construct 3-D tensors based on state variables at different sample times; and 4) integrate into 4-D tensor inputs. Moreover, a transfer learning process is employed to overcome the challenge of insufficient data and operating condition changes in real power systems for new prediction tasks. Simulation results in IEEE 118-bus system verify that the CNN-LSTM method not only greatly improves the timeliness of online frequency control, but also presents good accuracy and effectiveness. Test cases in the New England 39-bus system and the South Carolina 500-bus system validate that the transfer learning process can provide accurate results even with insufficient training data. |
| first_indexed | 2024-12-16T17:12:48Z |
| format | Article |
| id | doaj.art-013c5413adf84ffc89d98f41f5165684 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:12:48Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-013c5413adf84ffc89d98f41f51656842022-12-21T22:23:22ZengIEEEIEEE Access2169-35362021-01-019757127572110.1109/ACCESS.2021.30820019435326A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and ControlJian Xie0https://orcid.org/0000-0002-3447-2642Wei Sun1Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USADepartment of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USAFast and accurate prediction and control of power system dynamic frequency after disturbance is essential to enhance power system stability. Machine learning methods have great potential in harnessing data for online application with accurate predictions. This paper proposes a two-stage novel transfer and deep learning-based method to predict power system dynamic frequency after disturbance and provide optimal event-based load shedding strategy to maintain system frequency. The proposed deep learning model combines convolutional neural network (CNN) and long short-term memory (LSTM) network to harness both spatial and temporal measurements in the input data, through a four-dimensional (4-D) tensor input construction process including, 1) capture system network topology information and critical measurements from different time intervals; 2) compute a multi-dimensional electric distance matrix and reduce to a 2-D plane which can describe the system nodal distribution; 3) construct 3-D tensors based on state variables at different sample times; and 4) integrate into 4-D tensor inputs. Moreover, a transfer learning process is employed to overcome the challenge of insufficient data and operating condition changes in real power systems for new prediction tasks. Simulation results in IEEE 118-bus system verify that the CNN-LSTM method not only greatly improves the timeliness of online frequency control, but also presents good accuracy and effectiveness. Test cases in the New England 39-bus system and the South Carolina 500-bus system validate that the transfer learning process can provide accurate results even with insufficient training data.https://ieeexplore.ieee.org/document/9435326/CNNdeep learningdynamic frequencyLSTMspatial-temporal featuretransfer learning |
| spellingShingle | Jian Xie Wei Sun A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control IEEE Access CNN deep learning dynamic frequency LSTM spatial-temporal feature transfer learning |
| title | A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control |
| title_full | A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control |
| title_fullStr | A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control |
| title_full_unstemmed | A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control |
| title_short | A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control |
| title_sort | transfer and deep learning based method for online frequency stability assessment and control |
| topic | CNN deep learning dynamic frequency LSTM spatial-temporal feature transfer learning |
| url | https://ieeexplore.ieee.org/document/9435326/ |
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