Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System
The number of wind farms is increasing every year because many countries are turning their attention to renewable energy sources. Wind turbines are considered one of the best alternatives to produce clean energy. Most of the wind farms installed supervisory control and data acquisition (SCADA) syste...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/1/125 |
| _version_ | 1797543280465936384 |
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| author | Imre Delgado Muhammad Fahim |
| author_facet | Imre Delgado Muhammad Fahim |
| author_sort | Imre Delgado |
| collection | DOAJ |
| description | The number of wind farms is increasing every year because many countries are turning their attention to renewable energy sources. Wind turbines are considered one of the best alternatives to produce clean energy. Most of the wind farms installed supervisory control and data acquisition (SCADA) system in their turbines to monitor wind turbines and logged the information as time-series data. It demands a powerful information extraction process for analysis and prediction. In this research, we present a data analysis framework to visualize the collected data from the SCADA system and recurrent neural network-based variant long short-term memory (LSTM) based prediction. The data analysis is presented in cartesian, polar, and cylindrical coordinates to understand the wind and energy generation relationship. The four features: wind speed, direction, generated active power, and theoretical power are predicted and compared with state-of-the-art methods. The obtained results confirm the applicability of our model in real-life scenarios that can assist the management team to manage the generated energy of wind turbines. |
| first_indexed | 2024-03-10T13:42:20Z |
| format | Article |
| id | doaj.art-1e1198f5d6554e04a32c6daa8174f239 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T13:42:20Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-1e1198f5d6554e04a32c6daa8174f2392023-11-21T02:53:29ZengMDPI AGEnergies1996-10732020-12-0114112510.3390/en14010125Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA SystemImre Delgado0Muhammad Fahim1Institute of Information Systems, Innopolis University, 420500 Tatarstan, RussiaInstitute of Information Systems, Innopolis University, 420500 Tatarstan, RussiaThe number of wind farms is increasing every year because many countries are turning their attention to renewable energy sources. Wind turbines are considered one of the best alternatives to produce clean energy. Most of the wind farms installed supervisory control and data acquisition (SCADA) system in their turbines to monitor wind turbines and logged the information as time-series data. It demands a powerful information extraction process for analysis and prediction. In this research, we present a data analysis framework to visualize the collected data from the SCADA system and recurrent neural network-based variant long short-term memory (LSTM) based prediction. The data analysis is presented in cartesian, polar, and cylindrical coordinates to understand the wind and energy generation relationship. The four features: wind speed, direction, generated active power, and theoretical power are predicted and compared with state-of-the-art methods. The obtained results confirm the applicability of our model in real-life scenarios that can assist the management team to manage the generated energy of wind turbines.https://www.mdpi.com/1996-1073/14/1/125recurrent neural networktime series forecastingsmart gridsSCADA data |
| spellingShingle | Imre Delgado Muhammad Fahim Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System Energies recurrent neural network time series forecasting smart grids SCADA data |
| title | Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System |
| title_full | Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System |
| title_fullStr | Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System |
| title_full_unstemmed | Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System |
| title_short | Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System |
| title_sort | wind turbine data analysis and lstm based prediction in scada system |
| topic | recurrent neural network time series forecasting smart grids SCADA data |
| url | https://www.mdpi.com/1996-1073/14/1/125 |
| work_keys_str_mv | AT imredelgado windturbinedataanalysisandlstmbasedpredictioninscadasystem AT muhammadfahim windturbinedataanalysisandlstmbasedpredictioninscadasystem |