Power transformers are some of the most expensive components of electrical power plant. The failure of a transfoimer is a matter of significant concern for electrical utilities, not only for the consequent severe economic losses but also because the utility response to a customer during the outage condition is one of the major factors in determining the overall customer attitude towards the utility. Therefore, it is essential to predict the thermal behaviour of a transformer during load cycling and in particular in the presence of overload conditions. The authors propose a novel technique to predict the winding hottest spot temperature of a power transformer in the presence of overload conditions, as an alternative methodology to the radial basis function network (RBFN) based technique presented in a previous paper. The method proposed is based on a modified local memorybased algorithm which, working on the load current, the top oil temperature rise over ambient temperature and taking into account other meteorological parameters, permits the recognition of the hot spot temperature pattern. In particular some corrective actions for the classical local methods will be evidenced to customise it for real-time applications. Data obtained from experimental tests allow the local learning algorithm to be tested to evaluate the performance of the proposed method in terms of accuracy.

Application of local memory-based techniques for power transformer thermal overload protection

VACCARO, Alfredo
2001-01-01

Abstract

Power transformers are some of the most expensive components of electrical power plant. The failure of a transfoimer is a matter of significant concern for electrical utilities, not only for the consequent severe economic losses but also because the utility response to a customer during the outage condition is one of the major factors in determining the overall customer attitude towards the utility. Therefore, it is essential to predict the thermal behaviour of a transformer during load cycling and in particular in the presence of overload conditions. The authors propose a novel technique to predict the winding hottest spot temperature of a power transformer in the presence of overload conditions, as an alternative methodology to the radial basis function network (RBFN) based technique presented in a previous paper. The method proposed is based on a modified local memorybased algorithm which, working on the load current, the top oil temperature rise over ambient temperature and taking into account other meteorological parameters, permits the recognition of the hot spot temperature pattern. In particular some corrective actions for the classical local methods will be evidenced to customise it for real-time applications. Data obtained from experimental tests allow the local learning algorithm to be tested to evaluate the performance of the proposed method in terms of accuracy.
File in questo prodotto:
File Dimensione Formato  
3_RI.pdf

non disponibili

Licenza: Non specificato
Dimensione 752.9 kB
Formato Adobe PDF
752.9 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/168
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 15
social impact