Increasing efficiency in wind energy electricity generating by signal processing from wind measuring equipment on wind turbine for the determination of wind direction

Koetket, Ponrawee; Chaichana, Thanapong; Auttawaitkul, Yingrak; Chiracharit, Werapon; Khunkhet, Somthawin; Waewsak, Jompob

doi:10.1109/iwait.2018.8369735

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…Cherif et al [19], a hybrid excited flux switching generator was used to construct a turbine system. Koetket et al [20], the wind speed is measured by measuring devices on wind turbines and rotating the nacelle in the direction of the wind. The turbine is trained to generate the most power possible based on the wind's direction and speed, according to the authors of [21], who presented two main aspects such as power electronics and smart systems to achieve maximum power using reinforcement learning.…”

Section: Background Studymentioning

confidence: 99%

An intelligent wind turbine with yaw mechanism using machine learning to reduce high-cost sensors quantity

Bhandari Abhi,

Kumar Saha,

Tahmidul Kabir

et al. 2023

IJEECS

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In this paper, with the assistance of some tools and a machine learning model, a smart wind turbine was formed that eliminates some expensive sensors and reduces sensor complexity. Squirrel cage induction generator (SCIG) and six rotor blades make up the proposed design, and depending on the wind's direction, the turbine itself can rotate the rotor hub to produce energy more effectively. Additionally, two stepper motors are coupled to the yaw mechanism with the aid of the rotor hub, and the entire controlling procedure will depend on the direction of the wind. The rotor hub must continuously revolve in the same direction as the wind to maximize wind energy utilization. Additionally, to correctly predict wind degrees, a machine learning model was deployed. Random forest regression was used to train and predict the wind direction. The model is deployed in Raspberry Pi, where the incoming sensor values are being stored. Using the generated data, machine learning model was trained and it can be concluded that the model can potentially replace some of the expensive sensors to reduce cost. The model can be used for similar weather conditions only based on machine learning model and fewer sensors.

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Section: Background Studymentioning

confidence: 99%