logging in or signing up Neural Network Based Sensorless Maximum Wind Energ Kliment Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: Embed: Flash iPad Dynamic Copy Does not support media & animations Automatically changes to Flash or non-Flash embed WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 926 Category: Entertainment License: All Rights Reserved Like it (1) Dislike it (0) Added: November 05, 2007 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... By: allammy (31 month(s) ago) hi plz allow me to download Saving..... Post Reply Close Saving..... Edit Comment Close By: allammy (31 month(s) ago) thank you its agreat works Saving..... Post Reply Close Saving..... Edit Comment Close By: meetujain (33 month(s) ago) hi plz allow me to download Saving..... Post Reply Close Saving..... Edit Comment Close By: nishu69 (38 month(s) ago) can u pls send me the detail report on this to nischith10@gmail.com Saving..... Post Reply Close Saving..... Edit Comment Close Premium member Presentation Transcript Neural Network Based Sensorless Maximum Wind Energy Control with Compensated Power Coefficient: Neural Network Based Sensorless Maximum Wind Energy Control with Compensated Power Coefficient Hui Li Dept. of ECE FAMU-FSU College of Engineering Tallahassee, FL32310 Current Mechanical Sensorless Peak Power Tracking Control: Current Mechanical Sensorless Peak Power Tracking Control Proposed work : Proposed work Variable Wind Speed System: Variable Wind Speed System (a) PMSG wind Generator (b) SCIG wind Generator Analysis of Wind Turbine Maximum Power: Pm output mechanical power of the wind turbine, air density, tip speed ratio, Cp the power coefficient, rm adius of the rotor, wind turbine rotor swept area, Vw wind velocity, r rotor speed of the turbine Analysis of Wind Turbine Maximum Power Wind velocity estimation by ANN (I): Wind velocity estimation by ANN (I) Wind velocity estimation by ANN (II): Wind velocity estimation by ANN (II)Peak Control strategy with compensation of power coefficient drift: Peak Control strategy with compensation of power coefficient drift Derivation of Pseudo-power curve: Derivation of Pseudo-power curve (a) (b)Peak Control strategy with compensation of power coefficient drift: Peak Control strategy with compensation of power coefficient drift Simulation Study of PMSG Wind Generator: Simulation Study of PMSG Wind Generator SCIG Wind Generator: SCIG Wind Generator Experimental Setup: Experimental Setup Experimental Results: Experimental Results Conclusion: Conclusion A maximum mechanical power of the wind turbine can be well tracked at both dynamic and steady states; A neural network based wind velocity estimator is developed to provide fast and accurate velocity information to avoid using anemometers; A neural network based scheme is presented to compensate the potential drift of wind turbine power coefficient curve without extra sensors A maximum mechanical The simulation study and experimental results of PMSG wind generator and SCIG wind generator proves the validity of the method. You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
Neural Network Based Sensorless Maximum Wind Energ Kliment Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: Embed: Flash iPad Dynamic Copy Does not support media & animations Automatically changes to Flash or non-Flash embed WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 926 Category: Entertainment License: All Rights Reserved Like it (1) Dislike it (0) Added: November 05, 2007 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... By: allammy (31 month(s) ago) hi plz allow me to download Saving..... Post Reply Close Saving..... Edit Comment Close By: allammy (31 month(s) ago) thank you its agreat works Saving..... Post Reply Close Saving..... Edit Comment Close By: meetujain (33 month(s) ago) hi plz allow me to download Saving..... Post Reply Close Saving..... Edit Comment Close By: nishu69 (38 month(s) ago) can u pls send me the detail report on this to nischith10@gmail.com Saving..... Post Reply Close Saving..... Edit Comment Close Premium member Presentation Transcript Neural Network Based Sensorless Maximum Wind Energy Control with Compensated Power Coefficient: Neural Network Based Sensorless Maximum Wind Energy Control with Compensated Power Coefficient Hui Li Dept. of ECE FAMU-FSU College of Engineering Tallahassee, FL32310 Current Mechanical Sensorless Peak Power Tracking Control: Current Mechanical Sensorless Peak Power Tracking Control Proposed work : Proposed work Variable Wind Speed System: Variable Wind Speed System (a) PMSG wind Generator (b) SCIG wind Generator Analysis of Wind Turbine Maximum Power: Pm output mechanical power of the wind turbine, air density, tip speed ratio, Cp the power coefficient, rm adius of the rotor, wind turbine rotor swept area, Vw wind velocity, r rotor speed of the turbine Analysis of Wind Turbine Maximum Power Wind velocity estimation by ANN (I): Wind velocity estimation by ANN (I) Wind velocity estimation by ANN (II): Wind velocity estimation by ANN (II)Peak Control strategy with compensation of power coefficient drift: Peak Control strategy with compensation of power coefficient drift Derivation of Pseudo-power curve: Derivation of Pseudo-power curve (a) (b)Peak Control strategy with compensation of power coefficient drift: Peak Control strategy with compensation of power coefficient drift Simulation Study of PMSG Wind Generator: Simulation Study of PMSG Wind Generator SCIG Wind Generator: SCIG Wind Generator Experimental Setup: Experimental Setup Experimental Results: Experimental Results Conclusion: Conclusion A maximum mechanical power of the wind turbine can be well tracked at both dynamic and steady states; A neural network based wind velocity estimator is developed to provide fast and accurate velocity information to avoid using anemometers; A neural network based scheme is presented to compensate the potential drift of wind turbine power coefficient curve without extra sensors A maximum mechanical The simulation study and experimental results of PMSG wind generator and SCIG wind generator proves the validity of the method.