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primary topic: Forecasting of Wind Turbines Generated Power with Missing Input Variables

Forecasting of Wind Turbines Generated Power with Missing Input Variables
type: AcademicArticle
type: Article
abstract: The power generated by electric wind turbines undergoes rapid changes due to continuous fluctuation of wind speed, direction, atmospheric pressure, etc. Providing the power industry with the capability to estimate these performance characteristics helps in the pre-planning of maintenance, which helps in power management by assessing the generated power for the day. However, forecasting the generated power with any missing input parameters is quite challenging. Therefore, this paper proposes a forecasting model with three types of neural networks to handle one missing input parameter to predict the wind turbine's generated power. Firstly, a Feed Forward Neural Network (FFNN) is developed to forecast generated power from all four available input parameters. Later the FFNN, along with a Long Short-Term Memory (LSTM) and Nonlinear Autoregressive (NAR) neural networks, are modeled to handle the missing input parameter. The main FFNN then uses the predicted parameter to forecast the generated power. The results from the simulation study have indicated that the proposed strategy achieved the best performance in predicting the missing input and the system's generated power. © 2022 IEEE.
date: 2022
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Institute of Electrical and Electronics Engineers Inc.
type: Organization

2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022
type: Collection

P.A.M. Devan
type: Person
given name: P.A.M.
family name: Devan

R. Abishek
type: Person
given name: R.
family name: Abishek

M. Sunder
type: Person
given name: M.
family name: Sunder

M. Maiti
type: Person
given name: M.
family name: Maiti

K. Bingi
type: Person
given name: K.
family name: Bingi

M. Assaad
type: Person
given name: M.
family name: Assaad

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primary topic: Forecasting of Wind Turbines Generated Power with Missing Input Variables