Abstract
This paper investigates the capability of the battery energy storage system (BESS) to enhance the stability of wind power systems using an improved intelligent control strategy based on neural identification and simultaneous control. The proposed neural identifer comprises of only a few adaptive parameters, which reduce the computational burden and make it easier to implement. It faithfully estimates the local linear model of a dynamic system and adjusts its parameters online for varying operating states of the system. The proposed neuro-adaptive controller needs local measurements only and is model-free. The performance of the proposed controller is compared with the lead-lag controller whose parameters are tuned using teacher learning-based optimization. The performance of the controllers is compared on the basis of various power system stability indices such as oscillation damping, wind-power smoothing and improving the voltage profile of the wind farm under varying operating conditions.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
