NDLI: Power production prediction of wind turbines using a fusion of MLP and ANFIS networks

Content Provider	IET Digital Library
Author	Morshedizadeh, Majid Kordestani, Mojtaba Carriveau, Rupp Ting, David S. K. Saif, Mehrdad
Abstract	Access to accurate power production prediction of a wind turbine in future hours enables operators to detect possible underperformance and anomalies in advance. This may enable more proactive and strategic operations optimisation. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines. In this study, an algorithm is proposed to impute values of data that are missing, out-of-range, or outliers. It is shown that an appropriate combination of a decision tree and mean value for imputation can improve the data analysis and prediction performance by the creation of a smoother dataset. In addition, principal component analysis is employed to extract parameters with power production influence based on all available signals in the SCADA data. Then, a new data fusion technique is applied, combining dynamic multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS) networks to predict future performance of wind turbines. This prediction is made on a scale of one-hour intervals. This novel combination of feature extraction, imputation, and MLP/ANFIS fusion performs well with favourably low prediction error levels. Thus, such an approach may be a valuable tool for turbine power production prediction.
Starting Page	1025
Ending Page	1033
Page Count	9
ISSN	17521416
Volume Number	12
e-ISSN	17521424
Issue Number	Issue 9, Jul (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/12/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2017.0736
Journal	IET Renewable Power Generation
Publisher Date	2018-04-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ANFIS Network Data Analysis Data Fusion Technique Data Handling Technique Decision Tree Feature Extraction Fuzzy Neural Nets Fuzzy Reasoning Imputation Inference Mechanisms Knowledge Engineering Technique Mean Value MLP/ANFIS Fusion Multilayer Perceptrons Neural Computing Technique Neuro-fuzzy Inference System Network Power 2.3 MW Power Engineering Computing Power Production Influence Power Production Prediction Principal Component Analysis Proactive Operation Optimisation SCADA Data SCADA System Sensor Fusion Strategic Operation Optimisation Supervisory Control And Data Acquisition Time 20.0 Month Turbine Power Production Prediction Wind Power Plant Wind Turbine
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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