Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

1 State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, People’s Republic of China 2 Electric Power Research Institute of Shandong Electric Power Corporation of SGCC, Jinan, People’s Republic of China

Fund Project:

National Natural Science Foundation of China (No. 51307185), Natural Science Foundation Project of CQ CSTC (No. cstc2012jjA90004) and the Fundamental Research Funds for the Central Universities (No. CDJPY12150002).

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials

    An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimation (PDE) methods, which usually assume that the wind speed are subordinate to a certain known distribution (e.g. Weibull distribution and Normal distribution) and estimate the parameters of models with the historical data. This paper presents a kernel density estimation (KDE) method which is a nonparametric way to estimate the probability density function (PDF) of wind speed. The method is a kind of data-driven approach without making any assumption on the form of the underlying wind speed distribution, and capable of uncovering the statistical information hidden in the historical data. The proposed method is compared with three parametric models using wind data from six sites. The results indicate that the KDE outperforms the PDE in terms of accuracy and flexibility in describing the long-term wind speed distributions for all sites. A sensitivity analysis with respect to kernel functions is presented and Gauss kernel function is proved to be the best one. Case studies on a standard IEEE reliability test system (IEEE-RTS) have verified the applicability and effectiveness of the proposed model in evaluating the reliability performance of wind farms.

    Cited by
Get Citation
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
  • Received:
  • Revised:
  • Adopted:
  • Online: March 02,2017
  • Published: