A robust prognostic indicator for renewable energy technologies: A novel error correction grey prediction model

Daming Zhou, Ahmed Al-Durra, Ke Zhang, Alexandre Ravey, Fei Gao

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

This paper proposes a novel error correction grey prediction model for degradation prediction of renewable energy storages. The proposed approach uses an error correction factor ψ to eliminate the inherent error of the original grey model (GM), and at the same time retain the original simplicity and fast prototyping. In addition, due to the uncertainty and complexity of failure mechanisms, a trigonometric residual modification is considered in order to well-describe the influence of operating conditions or cyclic fluctuation on the renewable energy storages. Two experimental case studies, including lithium-ion battery and fuel cell aging tests, are performed to validate the performance of the proposed method. In particular, the accuracy of the proposed method is investigated for different prediction horizon lengths, in order to further demonstrate its effectiveness and robustness. It is worth mentioning that the proposed method can ensure the accuracy of the remaining useful life estimation in the case of long-term forecasting, and thus, the maintenance management and corrective action of renewable energy storages can be scheduled earlier, leading to more effective cost minimization and risk mitigation.

Original languageBritish English
Article number8624619
Pages (from-to)9312-9325
Number of pages14
JournalIEEE Transactions on Industrial Electronics
Volume66
Issue number12
DOIs
StatePublished - Dec 2019

Keywords

  • Fuel cell
  • grey prediction model
  • lithium-ion battery (LIB)
  • remaining useful life (RUL) estimation
  • renewable energy storages

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