The wind power producers in India are obliged to give a forecast of their power in advance as per Indian Electricity Grid Code 2010 (IEGC). This work explores Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to forecast the average hourly wind speed. To obtain ANFIS that best suited the wind speed forecasting system, several ANFIS models were trained, tested, and compared. The results for the short term wind speed forecast using the available data have been studied. A typical Supervisory Control and Data Acquisition (SCADA) system is modeled to transfer data to a forecasting system in the host computer loaded with ANFIS and also to communicate the output to the State Load Dispatch Centers (SLDC). With the constraints of data and computation capability, the proposed wind speed forecast work is expected to deliver better wind power forecast results, when the required inputs are given. The presented work deals with the methodology which can be used by the Wind power producers with appropriate facilities and tools on a larger scale.
Skip Nav Destination
Article navigation
November 2013
Research Article|
December 16 2013
Wind speed forecasting for grid code compliancea)
S. R. Nandha Kishore;
S. R. Nandha Kishore
b)
Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham
, Coimbatore 641112, India
Search for other works by this author on:
V. Vanitha
V. Vanitha
Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham
, Coimbatore 641112, India
Search for other works by this author on:
a)
Paper submitted as part of the International Conference on Wind Energy: Materials, Engineering, and Policies (Guest Editors: Torben Mikkelson and R. T. Durai Prabhakaran). The Conference was held in Hyderabad, India, November 22–23, 2012.
J. Renewable Sustainable Energy 5, 063125 (2013)
Article history
Received:
January 31 2013
Accepted:
November 21 2013
Citation
S. R. Nandha Kishore, V. Vanitha; Wind speed forecasting for grid code compliance. J. Renewable Sustainable Energy 1 November 2013; 5 (6): 063125. https://doi.org/10.1063/1.4850256
Download citation file:
Sign in
Don't already have an account? Register
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Pay-Per-View Access
$40.00
Citing articles via
A review of tidal energy—Resource, feedbacks, and environmental interactions
Simon P. Neill, Kevin A. Haas, et al.
Overview of preparation for the American WAKE ExperimeNt (AWAKEN)
Patrick Moriarty, Nicola Bodini, et al.
Machine learning for modern power distribution systems: Progress and perspectives
Marija Marković, Matthew Bossart, et al.
Related Content
Assessment of SCADA project implementation of electric distribution utility for reliability and safety in electric service
AIP Conf. Proc. (October 2024)
A switched boost landsman converter with ANFIS based MPPT for grid connected wind energy system
AIP Conf. Proc. (May 2023)
Optimization of wind-turbine control using the hybrid ANFIS-PID method based on ant colony optimization
AIP Conf. Proc. (May 2023)
Wind turbine condition monitoring & classifying system using virtual SCADA and IOT: A machine learning approach
AIP Conference Proceedings (April 2022)
Geothermal power plant performance estimation using ANFIS-PCA and ANFIS-GA
AIP Conf. Proc. (September 2024)