Performance limits for the amplitude estimation of power system harmonics & interharmonics

Altintasi C., ORGUNER U., Salor O.

IET GENERATION TRANSMISSION & DISTRIBUTION, cilt.14, sa.19, ss.4108-4121, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 19
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1049/iet-gtd.2020.0080
  • Dergi Adı: IET GENERATION TRANSMISSION & DISTRIBUTION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.4108-4121
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

In this study, the problem of amplitude estimation of (inter)harmonics is investigated. Specifically performance limits are provided for the accuracy of unbiased amplitude estimation algorithms and for the analysis window size required by an algorithm for achieving and maintaining a desired accuracy. Cramer-Rao lower bound (CRLB) is used for the bounds on the accuracy. Unlike the existing literature concentrating on the asymptotic behaviour of the bounds, the behaviour of CRLBs for small window sizes and close frequency components is investigated. In particular, the (worst) amplitude CRLBs are shown to be inversely proportional to the squared frequency differences for small analysis window sizes and close frequency components. For the bounds on analysis window size, the concept of convergence-time is defined and illustrated. The convergence-times of unbiased amplitude estimation algorithms are then lower bounded by establishing its relationship with CRLBs. The results and ideas are illustrated using both synthetic signals and field data collected from a power grid. Numerical results show that the proposed performance limits are tight and have strong prediction capabilities for amplitude estimation algorithms having prior knowledge about the existing frequencies in the signal.