#6440. Clustering of transformer condition using frequency response analysis based on k-means and GOA

December 2026publication date
Proposal available till 24-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Electrical and Electronic Engineering;
Energy Engineering and Power Technology;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №6440.1 Contract №6440.2 Contract №6440.3 Contract №6440.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
Frequency response analysis (FRA) is considered as the most popular and reliable method to detect mechanical deformations within power transformers. Despite this popularity, interpretation of FRA signatures has not yet been standardized worldwide. Correct interpretation of FRA signatures results in reliable diagnosis of the transformer mechanical integrity which facilitates timely and proper remedial action. As a further step towards the full understanding of the analysis of FRA signatures, this paper presents a k-means method to cluster power transformers under different fault types. In this regard, series of FRA measurements has been conducted on various transformer models under different fault types. Then, a feature based on interval maximum to global maximum (IMGM) is extracted from the obtained FRA measurements to facilitate data clustering. Using grasshopper optimization algorithm (GOA), the centers of the clusters are determined and by applying the data obtained from operating transformers, the performance of the proposed method is evaluated and compared under different cases.
Keywords:
Condition monitoring; Frequency response analysis; Grasshopper optimization algorithm; k-means clustering; Power transformers

Contacts :
0