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A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant.
- Source :
- Energies (19961073); Apr2024, Vol. 17 Issue 7, p1627, 24p
- Publication Year :
- 2024
-
Abstract
- This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19961073
- Volume :
- 17
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- Energies (19961073)
- Publication Type :
- Academic Journal
- Accession number :
- 176593282
- Full Text :
- https://doi.org/10.3390/en17071627