The Influence of Climate Parameters on Maintenance of Wind Farms—A Galician Case Study
| Autor: | Diego Vergara, Ángel M. Costa, Feliciano Fraguela, José A. Orosa |
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| Rok vydání: | 2020 |
| Předmět: |
Moist air
Energy-Generating Resources Operations research Maintenance Process (engineering) Computer science Climate 020209 energy wind farm Sample (statistics) Wind 02 engineering and technology lcsh:Chemical technology sensors 01 natural sciences Biochemistry Article maintenance Analytical Chemistry law.invention Wind farm 010104 statistics & probability Artificial Intelligence law Anemometer 0202 electrical engineering electronic engineering information engineering Production (economics) lcsh:TP1-1185 0101 mathematics Electrical and Electronic Engineering Extreme value theory Data mining Weather Instrumentation Corrective maintenance Sensors data mining Atomic and Molecular Physics and Optics Variable (computer science) Spain weather Electrical network moist air |
| Zdroj: | Sensors Volume 21 Issue 1 Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 40, p 40 (2021) RUC. Repositorio da Universidade da Coruña instname RUC: Repositorio da Universidade da Coruña Universidade da Coruña (UDC) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21010040 |
| Popis: | There are different monitoring procedures in wind farms with two main objectives: (i) to improve energy production by the capability of the national electrical network and (ii) to reduce the stooped hours due to preventive and or corrective maintenance activities. In this sense, different sensors are employed to sample in real-time the working conditions of equipment, the electrical production and the weather conditions. Despite this, just the anemometer measurement can be related to the more important errors of interruption of power regulation and anemometer errors. Both errors are related to gusty winds and contribute to more than 33% of the cost of a wind farm. The present paper reports some mathematical relations between weather and maintenance but there are no extreme values of each variable that let us predict a near failure and its corresponding loss of working hours. To achieve this, statistical analysis identifies the relation between weather variables and errors and different models are obtained. What is more, due to the difficulty and economic implications involving the implementation of complex algorithms and techniques of artificial intelligence, it is still a challenge to optimize this process. Finally, the obtained results show a particular case study that can be extrapolated to other wind farms after different case studies to adjust the model to different weather regions, and serve as a useful tool for weather maintenance. |
| Databáze: | OpenAIRE |
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