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In this section, a comprehensive GPF-based algorithm is developed to synthesize the outcomes of the three previously described methods—GNM, FPM, and FHM—into a unified framework for switchgear health assessment and life prediction. The algorithm transforms the normalized outputs of these methods into a consolidated HI, which is subsequently mapped to the RUL of each switchgear unit. Fig 5 depicts the proposed GPF-based HI & RUL algorithm.
Abstract Ensuring the dependable operation of wind turbines is essential for enhancing energy efficiency and minimizing maintenance costs, particularly in small-scale wind turbines where blade failures can severely affect safety and performance. This paper proposes a hybrid condition monitoring and prognostic framework for small-scale wind turbine (SSWT) blades that combines signal-based features, structural dynamic characteristics, and Health Index (HI) fusion.
1. Introduction The primary challenge associated with microgrid (MG) operation is ensuring an effective protection scheme that safeguards the MG in both grid-connected and islanded modes by swiftly isolating only the affected portions of the system (Tejeswini et al., 2019) [1].
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