At present

there are some problems for online identification and alarm method of subsynchronous oscillation (SSO) based on phasor measurement unit (PMU). Parameter identification method only identifies the frequency and amplitude

and does not identify attenuation factor. Alarm threshold values need to be determined by personal experiences. Therefore

it is difficult to guarantee the rapidity and reliability of alarm criterion. In view of the problems mentioned above

the on-line monitoring SSO method is proposed combing synchroextracting transform (SET) and naive Bayes (NB)

which is one of the machine learning method. SET can quickly and accurately identify the mode parameters of SSO

and NB can automatically realize online warning. Firstly

SET identifies the historical data of SSO

and then NB method learns from the identified frequency and attenuation factor

which helps to generate a NB classifier. Secondly

when there are new upload oscillation data of PMU

the oscillation frequency and attenuation factor are identified by SET at the first step. Then

NB classifier is used to deal with these parameters to determine whether SSO occurs or not and realize accurate on-line early warning

so as to realize the on-line monitoring of SSO. The simulation of the second IEEE benchmark model verifies the effectiveness of the proposed method.