Novel Passivity Analysis for Wind Turbine Systems via Decentralized Event-Triggered Approach | Zendy

R. Vadivel | Zendy; T. K. Santhosh | Zendy; R. Suresh | Zendy; Nallappan Gunasekaran | Zendy; Mohammed H. Alsharif | Zendy; Mun-Kyeom Kim | Zendy

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Novel Passivity Analysis for Wind Turbine Systems via Decentralized Event-Triggered Approach

Author(s) -

R. Vadivel,

T. K. Santhosh,

R. Suresh,

Nallappan Gunasekaran,

Mohammed H. Alsharif,

Mun-Kyeom Kim

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3591485

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper focuses on an innovative passivity analysis of wind turbine systems employing a decentralized event-triggered control approach. To establish new stability conditions, a novel Lyapunov-Krasovskii functional (LKF) is introduced, incorporating information from both the upper and lower bounds of the interval time-varying delay. We provide a new approach to identify the time instants of transmission from the sensors to the controller using just locally available information in event-triggered communication schemes. By using the relaxed integral inequality and the delay decomposition methodology, novel delay-dependent criteria guarantee that the considered system is passive. The passification problem is then handled by designing a decentralized event-triggered controller based on the acquired passivity conditions. The established stability conditions are expressed as linear matrix inequalities (LMIs), which may be numerically verified using the MATLAB LMI toolkit. Finally, a numerical example clearly demonstrates the superiority of our theoretical results using the simulation model.

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