ARIMA based statistical approach to predict wind power ramps

Arun Kumar Nayak, Kailash Chand Sharma, Rohit Bhakar, Jyotirmay Mathur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Wind power penetration in power systems is significantly increasing over the years. Wind generation is highly random and a significant change in wind power within a short timeframe forms a wind ramp event. These events can create severe generation-demand imbalance and cause damage to the wind turbines due to extreme stresses. Therefore, prediction of wind ramp events is essential for system operators to operate the power systems in a secure and reliable fashion. The existing approaches broadly predict based on classification of ramp events, which does not offer efficient results. This paper proposes Autoregressive Integrated Moving Average (ARIMA) based approach for wind ramp predication. Proposed approach is implemented on wind farm located at Bishop and Clerks, Massachusetts, USA to show annual and seasonal distribution results for up and down ramps. Proposed approach is validated through comparative analysis of ramp events obtained using forecasted and actual ramps. The approach is especially effective for short time horizon, offering low error percentage.

Original languageEnglish
Title of host publicationIEEE Power and Energy Society General Meeting, 2015
PublisherIEEE
ISBN (Print)9781467380409
DOIs
Publication statusPublished - 30 Sep 2015
EventIEEE Power and Energy Society General Meeting, PESGM 2015 - Denver, USA United States
Duration: 26 Jul 201530 Jul 2015

Conference

ConferenceIEEE Power and Energy Society General Meeting, PESGM 2015
CountryUSA United States
CityDenver
Period26/07/1530/07/15

Keywords

  • ARIMA model
  • Ramp Events
  • Wind Power

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  • Cite this

    Nayak, A. K., Sharma, K. C., Bhakar, R., & Mathur, J. (2015). ARIMA based statistical approach to predict wind power ramps. In IEEE Power and Energy Society General Meeting, 2015 [7286237] IEEE. https://doi.org/10.1109/PESGM.2015.7286237