Abstract
A flexible model which is based on a Triangular intuitionistic flexibility ranking and aggregating (TIFRA) operator is proposed for failure detection and reliability management in a Wind Turbine system. The model which is employed when there are limited research data and valid source of information, uses expert-based knowledge/opinion for failure detection and reliability management. The results from the study concludes that, the most important area affected by failure with respect to the failure criteria used, includes; oil level sensor tilt sensors for tower installation and accelerometers for tower sway (A2), Pressure sensor for blade monitoring (A3), and the Pitch actuator (A4). The main advantage of the proposed method is that it provides advanced information about faults that identifies the intensity of the system behavior also; the method provides a more complete view of the reliability management and root cause of failure in the Wind Turbine (WT) system using the flexibility parameter in the model.
Highlights
Wind Turbine technology which is an alternative source of power formation, is a sophisticated and complex system with high automation processes [1]
The Triangular intuitionistic flexibility ranking and aggregating (TIFRA) which consist of an Induced triangular intuitionistic hybrid fuzzy weighted geometric (I-TIHFWG) operator, and a Flexibility ranking score function is used for failure detection and reliability management of the wind turbine (WT) system
2.2 Triangular intuitionistic fuzzy number (TIFN) aggregation operators Based on the flexibility ranking function of TIFNs, we present the TIHFWG operator and the induced TIHFWG (I-TIHFWG) operator
Summary
Wind Turbine technology which is an alternative source of power formation, is a sophisticated and complex system with high automation processes [1]. The increasing technological advancement of today’s wind power source and its high rate of used in modern Towns and Cities have increased the concerns of its component maintenance repairing. Healthy reliability management study and failure detection which is a requirement for a reliable continuous operations and maintenance of machines systems [2,3] and for wind turbines on the remote access [4]. Can be described as one of the fundamentals in today’s Modern industrial wind turbine (WT) systems and a pre-requisite to ensuring a sustainable failure prediction maintenance [5]. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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