Early fault prediction of a wind turbine using a novel ANN training algorithm based on ant colony optimization

Yıl 2019, Cilt: 3 Sayı: 4, 139 - 147, 31.12.2019

Yunus Eroğlu , Serap Ulusam Seçkiner

https://doi.org/10.30521/jes.613315

Cited By: 10

Öz

The technological
developments in wind energy field have reduced the investment and the operation
costs. For this reason, wind farms have become more popular around the world.
Increasing the share of wind energy in the market has led to the need for
secure, inexpensive, and effective monitoring and control approaches. In the
present work, various monitoring and control tools, which are cheap and easy to
implement in wind farms using existing system data are proposed. The primary
purpose of this study is to offer a new methodology, i.e. an artificial neural
network (ANN) design with a novel training algorithm called Antrain ANN, in
order to explore the early fault detection in a wind turbine. Our case problem
is the fault detection for a wind turbine. For this issue, we used real data
consisting of 873 samples with 12 inputs and one output. The models used in the
work try to forecast fault occurrence before 10 minutes it happens.  The proposed Antrain ANN algorithm is
compared with Quick Propagation, Conjugate Gradient Descent, Quasi-Newton,
Limited Memory Quasi-Newton, Online Backpropagation, and Batch Back Propagation
algorithms, respectively. The results have shown that the proposed novel
approach has better results in the correct classification rates than other
algorithms except the Quasi-Newton and Limited Memory Quasi-Newton ones.

Anahtar Kelimeler

Ant colony algorithm, Artificial neural networks, Fault detection, Wind energy, Wind turbine

Kaynakça

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