Observer-based robust fault-tolerant control for wind energy system

Ashwini, Patil; Archana, Thosar

doi:10.1007/s13198-023-01863-x

Cited by 1 publication

(1 citation statement)

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“…The controller demonstrates robustness and good regulation performance in both healthy and faulty modes. Reference [84] proposes a robust, fault-tolerant control system for wind energy applications. It introduces an observer-based approach to handle sensor failures and irregular conditions, specifically focusing on the generator speed sensor.…”

Section: Conditional Monitoring: a Literature Review (Wind Turbine)mentioning

confidence: 99%

A survey on data acquisition methods in conditional monitoring of wind turbines

Heibati,

Alipour-Sarabi,

Bathaee

2023

Scientia Iranica

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The generation of electrical power through wind turbines has significantly increased nowadays. However, these systems are prone to faults that can disrupt the network and incur substantial costs for the generation units. Therefore, effective maintenance scheduling becomes crucial. A major challenge faced by wind turbines is their maintenance requirements, as any interruption in their operation and power generation can result in significant economic losses. Consequently, meticulous planning is indispensable to minimize such consequences. This paper that is the first part of the study conducts a survey of data acquisition methods in condition monitoring of wind turbines. In the second part, signal processing techniques for condition monitoring of wind turbines are presented. Furthermore, the paper examines a range of studies that have implemented practical condition monitoring methods in wind turbines, delving into the associated challenges and proposing potential solutions. Various methods such as vibration analysis, acoustic analysis, electrical parameter analysis, AI-based techniques, and fault-tolerant control have been employed for wind turbine maintenance. However, limitations exist in terms of data availability and computational burden. Future challenges include developing algorithms that require less data, reducing computational requirements, updating models with new conditions, enabling early detection and proactive maintenance, and reducing maintenance costs.

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Section: Conditional Monitoring: a Literature Review (Wind Turbine)mentioning

confidence: 99%