Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool
This paper proposes a recurrent neural network (RNN)-based maximum frequency deviation forecasting model for power systems with high photovoltaic power (PV) penetration. The proposed RNN model extracts the nonlinear features and invariant structures exhibited in regional PV power output data and tim...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9212358/ |
| _version_ | 1818917926238945280 |
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| author | Sungyoon Song Yoongun Jung Changhee Han Seungmin Jung Minhan Yoon Gilsoo Jang |
| author_facet | Sungyoon Song Yoongun Jung Changhee Han Seungmin Jung Minhan Yoon Gilsoo Jang |
| author_sort | Sungyoon Song |
| collection | DOAJ |
| description | This paper proposes a recurrent neural network (RNN)-based maximum frequency deviation forecasting model for power systems with high photovoltaic power (PV) penetration. The proposed RNN model extracts the nonlinear features and invariant structures exhibited in regional PV power output data and time-variable frequency data in case of contingency. To capture the regularity and random characteristics of PV power output, a probability power flow-dynamic tool (PPDT) for uncertain power system modeling has been developed. This tool considers all possible combinations of PV power generation patterns, even those with low probability, such as those caused by passing clouds. The results are verified by a comparison of various artificial intelligence methods using case studies from the South Korean power system. An online dispatch algorithm that considers the frequency constraints for a designated contingency can be implemented by using the proposed model. |
| first_indexed | 2024-12-20T00:41:49Z |
| format | Article |
| id | doaj.art-78c137a3f1e24905a4d261b41a8b2083 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T00:41:49Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-78c137a3f1e24905a4d261b41a8b20832022-12-21T19:59:32ZengIEEEIEEE Access2169-35362020-01-01818205418206410.1109/ACCESS.2020.30287079212358Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic ToolSungyoon Song0https://orcid.org/0000-0002-6501-1304Yoongun Jung1Changhee Han2https://orcid.org/0000-0002-7454-5148Seungmin Jung3https://orcid.org/0000-0002-9806-9545Minhan Yoon4https://orcid.org/0000-0002-4309-0072Gilsoo Jang5https://orcid.org/0000-0001-7590-8345Energy ICT Convergence Research Department, Korea Institute of Energy Research, Daejeon, South KoreaDepartment of Electrical Engineering, Korea University, Seoul, South KoreaDepartment of Electrical Engineering, Korea University, Seoul, South KoreaDepartment of Electrical Engineering, Hanbat National University, Daejeon, South KoreaDepartment of Electrical Engineering, Kangwon University, Seoul, South KoreaDepartment of Electrical Engineering, Korea University, Seoul, South KoreaThis paper proposes a recurrent neural network (RNN)-based maximum frequency deviation forecasting model for power systems with high photovoltaic power (PV) penetration. The proposed RNN model extracts the nonlinear features and invariant structures exhibited in regional PV power output data and time-variable frequency data in case of contingency. To capture the regularity and random characteristics of PV power output, a probability power flow-dynamic tool (PPDT) for uncertain power system modeling has been developed. This tool considers all possible combinations of PV power generation patterns, even those with low probability, such as those caused by passing clouds. The results are verified by a comparison of various artificial intelligence methods using case studies from the South Korean power system. An online dispatch algorithm that considers the frequency constraints for a designated contingency can be implemented by using the proposed model.https://ieeexplore.ieee.org/document/9212358/RNNfrequency stabilityprobability power flowrandomness |
| spellingShingle | Sungyoon Song Yoongun Jung Changhee Han Seungmin Jung Minhan Yoon Gilsoo Jang Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool IEEE Access RNN frequency stability probability power flow randomness |
| title | Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool |
| title_full | Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool |
| title_fullStr | Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool |
| title_full_unstemmed | Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool |
| title_short | Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool |
| title_sort | recurrent neural network based maximum frequency deviation prediction using probability power flow dynamic tool |
| topic | RNN frequency stability probability power flow randomness |
| url | https://ieeexplore.ieee.org/document/9212358/ |
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