Assessment of the Reliability of Wind Farms

The article examines the built wind farms with an installed capacity of 10 MW and greater, located on the territory of the country. After monitoring for a period of five years, a sample was made of the most frequently damaged elements and systems. These failures are characterized by the failure indicator, expressed in percentages. The three most significant systems causing the cessation of normal operation of wind turbines are identified. The statistical method -linear regression is used for their analysis and research. The hypothesis of repeatability of failure manifestations was tested. Through the Student's criterion, it is proven that the manifestation of these three indicators will not differ significantly from the average values for each of them. The null hypothesis is considered plausible.

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Published
2024-09-19
DOI
https://doi.org/10.1109/bulef63204.2024.10794916
Primary Topic
Engineering Diagnostics and Reliability
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article
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article

Assessment of the Reliability of Wind Farms

Evgeniya Vasileva, Ivelina Takeva-Beberova
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Engineering Diagnostics and Reliability
article

Assessment of the Reliability of Wind Farms

Evgeniya Vasileva, Ivelina Takeva-Beberova
article en

Abstract

The article examines the built wind farms with an installed capacity of 10 MW and greater, located on the territory of the country. After monitoring for a period of five years, a sample was made of the most frequently damaged elements and systems. These failures are characterized by the failure indicator, expressed in percentages. The three most significant systems causing the cessation of normal operation of wind turbines are identified. The statistical method -linear regression is used for their analysis and research. The hypothesis of repeatability of failure manifestations was tested. Through the Student's criterion, it is proven that the manifestation of these three indicators will not differ significantly from the average values for each of them. The null hypothesis is considered plausible.

Technical University of Sofia (BG)
Affordable and clean energy
Openalex Percentile: Top 74%
Engineering Diagnostics and Reliability
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