In order to further improve the diagnostic accuracy and diagnostic efficiency of carbon constrained type transformer operating state

a transformer state anomaly diagnosis method is proposed based on the improved k-medoids clustering theory. The transformer operating state is divided into four states : normal

early warning

abnormal and fault as the k-medoids clustering target. The model first obtains the running state data of the transformer

and the voiceprint feature index uses the Mel cepstrum feature as the standard to measure the abnormal vibration and sound of the transformer. Then

the transformer operation data is normalized and standardized based on its physical characteristics

which is convenient for clustering model input. Then

three measures are introduced to improve the traditional k-medoids clustering process

which are the initial clustering center generation based on local density evaluation

the adaptive substitution direction adjustment and the chaotic search of clustering update. The initial clustering center generation is optimized by the improvement measures and the optimization efficiency is improved to avoid prematurely falling into the local optimal clustering point. Finally

the historical operation data of a regional power grid transformer are divided into a training group and a test group. The k-medoids clustering network is optimized based on the training group

and the trained k-medoids clustering is used for the transformer operation state clustering and diagnosis of the test group. The correctness of the proposed model is verified.