Improved Combined Inertial Control of Wind Turbine Based on CAE and DNN for Temporary Frequency Support
With the continuous and large-scale development of renewable energy, there is a prominent decrease in the level of inertia in new power systems. This decrease leads to the weakening of the system’s capability to provide inertia support and frequency regulation during disturbance events. The wind tur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/12/6984 |
| _version_ | 1797596288762511360 |
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| author | Ziyang Ji Jie Zhang Yi Liu Tao Zhou |
| author_facet | Ziyang Ji Jie Zhang Yi Liu Tao Zhou |
| author_sort | Ziyang Ji |
| collection | DOAJ |
| description | With the continuous and large-scale development of renewable energy, there is a prominent decrease in the level of inertia in new power systems. This decrease leads to the weakening of the system’s capability to provide inertia support and frequency regulation during disturbance events. The wind turbines (WT), as the main representatives of renewable energy generation, should be more efficiently involved in the power system frequency regulation dynamics. However, optimal frequency regulation is difficult to achieve through the combined inertial control strategy of wind turbines because it greatly depends on control parameters and fluctuates in different scenarios. To cope with disturbance efficiently and quickly in different scenarios and obtain the optimal frequency regulation results, this paper presents an improved combined inertial intelligent control strategy of WT based on contractive autoencoder (CAE) and deep neural network (DNN). This method obtains the optimal parameters for combined inertial control using the particle swarm optimization (PSO) algorithm, then effectively extracts features from actual data using CAE followed by building a network model to predict the optimal combined inertial control parameters online. To verify and test the proposed method, it is applied in the IEEE 9-bus test system. The simulation results show that the method can obtain optimal control parameters with a faster computational time, good prediction accuracy, and generalization capability. |
| first_indexed | 2024-03-11T02:48:29Z |
| format | Article |
| id | doaj.art-250537e6a426446c91277d1fd447761d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T02:48:29Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-250537e6a426446c91277d1fd447761d2023-11-18T09:07:16ZengMDPI AGApplied Sciences2076-34172023-06-011312698410.3390/app13126984Improved Combined Inertial Control of Wind Turbine Based on CAE and DNN for Temporary Frequency SupportZiyang Ji0Jie Zhang1Yi Liu2Tao Zhou3School of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaJiangsu Electric Power Industrial Management Co., Ltd., Nanjing 210008, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaWith the continuous and large-scale development of renewable energy, there is a prominent decrease in the level of inertia in new power systems. This decrease leads to the weakening of the system’s capability to provide inertia support and frequency regulation during disturbance events. The wind turbines (WT), as the main representatives of renewable energy generation, should be more efficiently involved in the power system frequency regulation dynamics. However, optimal frequency regulation is difficult to achieve through the combined inertial control strategy of wind turbines because it greatly depends on control parameters and fluctuates in different scenarios. To cope with disturbance efficiently and quickly in different scenarios and obtain the optimal frequency regulation results, this paper presents an improved combined inertial intelligent control strategy of WT based on contractive autoencoder (CAE) and deep neural network (DNN). This method obtains the optimal parameters for combined inertial control using the particle swarm optimization (PSO) algorithm, then effectively extracts features from actual data using CAE followed by building a network model to predict the optimal combined inertial control parameters online. To verify and test the proposed method, it is applied in the IEEE 9-bus test system. The simulation results show that the method can obtain optimal control parameters with a faster computational time, good prediction accuracy, and generalization capability.https://www.mdpi.com/2076-3417/13/12/6984primary frequency regulationcombined inertial controlparticle swarm optimizationcontractive autoencoderdeep neural network |
| spellingShingle | Ziyang Ji Jie Zhang Yi Liu Tao Zhou Improved Combined Inertial Control of Wind Turbine Based on CAE and DNN for Temporary Frequency Support Applied Sciences primary frequency regulation combined inertial control particle swarm optimization contractive autoencoder deep neural network |
| title | Improved Combined Inertial Control of Wind Turbine Based on CAE and DNN for Temporary Frequency Support |
| title_full | Improved Combined Inertial Control of Wind Turbine Based on CAE and DNN for Temporary Frequency Support |
| title_fullStr | Improved Combined Inertial Control of Wind Turbine Based on CAE and DNN for Temporary Frequency Support |
| title_full_unstemmed | Improved Combined Inertial Control of Wind Turbine Based on CAE and DNN for Temporary Frequency Support |
| title_short | Improved Combined Inertial Control of Wind Turbine Based on CAE and DNN for Temporary Frequency Support |
| title_sort | improved combined inertial control of wind turbine based on cae and dnn for temporary frequency support |
| topic | primary frequency regulation combined inertial control particle swarm optimization contractive autoencoder deep neural network |
| url | https://www.mdpi.com/2076-3417/13/12/6984 |
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