NDLI: Two Models Switched Predictive Pitch Control for Wind Turbine Based on Improved Incremental SVR

Content Provider	IEEE Xplore Digital Library
Author	Lin Yonggang Li Wei Cui Baoling Liu Hongwei
Copyright Year	2006
Description	Author affiliation: State Key Lab. of Fluid Power Transmission & Control, Zhejiang Univ., Hang Zhou (Lin Yonggang; Li Wei; Cui Baoling; Liu Hongwei)
Abstract	Model predictive control arithmetic was used for wind turbine pitch control, whose nonlinear model was identified by support vector regression (SVR). But the model of wind turbine could be changed in fieldwork, so incremental learning algorithm was adopted for SVR online identification. In order to shorten the calculation time of SVR online identification, the improved sequential minimal optimization (SMO) algorithm was used to substitute for the original quadratic programming (QP). And the algorithm was further improved by the elimination of invalid break points and the model's being stored and reused. Because the differential loop was used in the electro-hydraulic proportional pitch-controlled system and the direction of load was changeless, the models of feathering and backpaddling are different. Therefore the two models were switched in the predictive control process. At last two models switched predictive pitch control algorithm based on improved incremental SVR was presented and tested in the pitch-controlled wind turbine semi-physical simulation test-bed. The results showed the power was kept more steady around the rated by the algorithm than traditional PID control one, when wind speed was above the rated
Starting Page	7498
Ending Page	7502
File Size	172914
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1713423
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Wind turbines Power system modeling Predictive control Quadratic programming Testing Arithmetic Prediction algorithms Three-term control Wind speed Semi-physical Model predictive control Pitch-controlled SVR SMO
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Individual variable pitch control of wind turbines

Model Predictive Control of DFIG-Based Wind Turbine

Nonlinear Model Predictive control (NMPC) of fixed pitch variable speed wind turbine

Very short term pitch angle optimization in wind turbines: A machine learning approach

Pitch control of variable speed constant frequency wind turbines based on neural network controller

Research on fixed-pitch wind turbine running in deep stall region

LQG controller design for pitch regulated variable speed wind turbine

Hyperbolic Tangent Basis Function Neural Networks Training by Hybrid Evolutionary Programming for Accurate Short-Term Wind Speed Prediction

Pitch-controlled variable-speed wind turbine generation

Two Models Switched Predictive Pitch Control for Wind Turbine Based on Improved Incremental SVR

Similar Documents

Individual variable pitch control of wind turbines

Model Predictive Control of DFIG-Based Wind Turbine

Nonlinear Model Predictive control (NMPC) of fixed pitch variable speed wind turbine

Very short term pitch angle optimization in wind turbines: A machine learning approach

Pitch control of variable speed constant frequency wind turbines based on neural network controller

Research on fixed-pitch wind turbine running in deep stall region

LQG controller design for pitch regulated variable speed wind turbine

Hyperbolic Tangent Basis Function Neural Networks Training by Hybrid Evolutionary Programming for Accurate Short-Term Wind Speed Prediction

Pitch-controlled variable-speed wind turbine generation

Two Models Switched Predictive Pitch Control for Wind Turbine Based on Improved Incremental SVR