Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller
The everyday benefits of environmentally friendly power sources urges to build their use to the bigger degree of whichwind energy is the most accessible asset. This paper presents the plan of multimode hang control methodology based variable speed wind power a...
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2022-01-01
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| Series: | EAI Endorsed Transactions on Energy Web |
| Subjects: | |
| Online Access: | https://eudl.eu/pdf/10.4108/eai.29-6-2021.170251 |
| _version_ | 1818891627699109888 |
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| author | K. Naresh P. Reddy P. Sujatha |
| author_facet | K. Naresh P. Reddy P. Sujatha |
| author_sort | K. Naresh |
| collection | DOAJ |
| description | The everyday benefits of environmentally friendly power sources urges to build their use to the bigger degree of whichwind energy is the most accessible asset. This paper presents the plan of multimode hang control methodology based variable speed wind power age framework. The multimode hang control procedure improves the framework to work regarding the network framework and furthermore in the independent method of activity. The multimode control methodology utilizes the DC connect voltage regulator to control the DC interface capacitor voltage for working the framework side converter and current regulator to control current and force of the rotor side converter. The control methodology is investigated with the customary regulator like PI regulator, astute regulators like Fuzzy regulator, fake neural organization (ANN) and model prescient regulator (MPC) which predicts the future factors. A correlation has been performed with the previously mentioned various sorts of regulators based breeze power age framework regarding various boundaries. This paper likewise includes examination of various experiments with the previously mentioned regulators. The examination of various experiments with various regulators has been performed utilizing MATLAB 2013a and every one of the outcomes are checked. |
| first_indexed | 2024-12-19T17:43:49Z |
| format | Article |
| id | doaj.art-d49ffca7ee75444aac19a4bb2cafdd2c |
| institution | Directory Open Access Journal |
| issn | 2032-944X |
| language | English |
| last_indexed | 2024-12-19T17:43:49Z |
| publishDate | 2022-01-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Energy Web |
| spelling | doaj.art-d49ffca7ee75444aac19a4bb2cafdd2c2022-12-21T20:12:08ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Energy Web2032-944X2022-01-0193710.4108/eai.29-6-2021.170251Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive ControllerK. Naresh0P. Reddy1P. Sujatha2Research Scholar, EEE Department, JNTUA Ananthapuramu, Ananthapuramu, Andhra Pradesh- India- 515002Professor, EEE Department, SVEC- Thirupati, Andhra Pradesh- India- 517102Professor, EEE Department, JNTUA Ananthapuramu, Ananthapuramu, Andhra Pradesh-India- 515002The everyday benefits of environmentally friendly power sources urges to build their use to the bigger degree of whichwind energy is the most accessible asset. This paper presents the plan of multimode hang control methodology based variable speed wind power age framework. The multimode hang control procedure improves the framework to work regarding the network framework and furthermore in the independent method of activity. The multimode control methodology utilizes the DC connect voltage regulator to control the DC interface capacitor voltage for working the framework side converter and current regulator to control current and force of the rotor side converter. The control methodology is investigated with the customary regulator like PI regulator, astute regulators like Fuzzy regulator, fake neural organization (ANN) and model prescient regulator (MPC) which predicts the future factors. A correlation has been performed with the previously mentioned various sorts of regulators based breeze power age framework regarding various boundaries. This paper likewise includes examination of various experiments with the previously mentioned regulators. The examination of various experiments with various regulators has been performed utilizing MATLAB 2013a and every one of the outcomes are checked.https://eudl.eu/pdf/10.4108/eai.29-6-2021.170251multimode control strategypi controllerfuzzy controller artificial neural networkmodel predictive controller |
| spellingShingle | K. Naresh P. Reddy P. Sujatha Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller EAI Endorsed Transactions on Energy Web multimode control strategy pi controller fuzzy controller artificial neural network model predictive controller |
| title | Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller |
| title_full | Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller |
| title_fullStr | Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller |
| title_full_unstemmed | Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller |
| title_short | Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller |
| title_sort | design and comparison of performance of dfig based wind turbine with pid controller fuzzy controller artificial neural network and model predictive controller |
| topic | multimode control strategy pi controller fuzzy controller artificial neural network model predictive controller |
| url | https://eudl.eu/pdf/10.4108/eai.29-6-2021.170251 |
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