A Method for Diagnosing Mechanical Faults of on-Load Tap Changer Based on Ensemble Empirical Mode Decomposition, Volterra Model and Decision Acyclic Graph Support Vector Machine

A method combined ensemble empirical mode decomposition, Volterra model and decision acyclic graph support vector machine was proposed to improve adaptability, feature resolution, and identification accuracy when diagnosing mechanical faults in an on-load tap changer of a transformer. In detail, the ensemble empirical mode decomposition algorithm was applied to decompose the multi-channel vibration signals in the switchover process of the on-load tap changer. Then, a Volterra model for the mechanical state of the on-load tap changer was established based on time-frequency characteristics obtained through the use of the ensemble empirical mode decomposition algorithm. Moreover, a matrix of coefficient vectors was also used in the Volterra model. This method will not only reduce the aliasing effect of empirical mode decomposition but also obtain high-resolution characteristics of nonstationary vibration signals. Furthermore, taking the singular values of the Volterra coefficient matrix as the fault characteristic, the data states of the model for diagnosing the on-load tap changer were automatically classified and identified by establishing a rapid, multi-classification decision acyclic graph support vector machine model with a low misjudgment rate. Finally, based on a certain on-load tap changer, the test platform for simulating mechanical faults was built. On this basis, by using the proposed method, the vibration signals generated due to typical mechanical faults, such as loosening of moving contacts, lessening of transition contact, and motor jam were acquired and analyzed, thus validating the effectiveness of the method through case studies. Compared with other methods, the new method could overcome many defects in existing methods and it has higher fault identification accuracy.