Decentralised wireless networked model predictive control design for wind turbines | IEEE Conference Publication | IEEE Xplore

Decentralised wireless networked model predictive control design for wind turbines


Abstract:

An innovative Decentralised Wireless Networked Model Predictive Control (DWNMPC) is presented to regulate wind turbines speed and compensate the effect of communication c...Show More

Abstract:

An innovative Decentralised Wireless Networked Model Predictive Control (DWNMPC) is presented to regulate wind turbines speed and compensate the effect of communication constraints such as dropouts. A decentralised control system and an estimation algorithm have been developed as follows. The decentralised structure decomposes the wind farm into n turbines each with its local controller. A coordinated strategy where controllers share the turbine's status among other controllers is implemented to adjust the power generated by each turbine. A decentralised Kalman Filter (KF), based on the state-space model, is available for each subsystem to estimate the states locally. Then, the local control performance is optimised using the state estimation while considering input constraints. Experiments using the TrueTime network simulator and a 5 MW variable-speed pitch regulated wind turbine for below rated wind speed model are provided and the results demonstrate the effectiveness of the proposed DWNMPC approach in compensating for high percentages of dropouts while providing good performance and robustness.
Date of Conference: 08-10 October 2020
Date Added to IEEE Xplore: 23 November 2020
ISBN Information:
Print on Demand(PoD) ISSN: 2372-1618
Conference Location: Sinaia, Romania

References

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