A Method for the Localization of Partial Discharge Sources in Transformers Using TDOA and Truncated Singular Value Decomposition

Abstract

Ultrasonic sensor detection has been widely used in partial discharge (PD) location. However, this PD localization method based on an ultrasonic sensor is computationally expensive, inaccurate, and sensitive to the time differences. A localization method for PD based on time difference of arrival (TDOA) and truncated singular value decomposition (TSVD) regularization is proposed in this paper, which can effectively ensure the accuracy of PD location in a transformer. First, the PD signal is received by the ultrasonic sensor, and an oscilloscope is used to receive the ultrasonic signal from the operational amplifier. Second, a spherical linear transformation method is used to transform the general nonlinear localization equations. Third, a TSVD regularization method is used to solve the linear equations. Then, to eliminate the influence of coordinate selection on the matrix ${b}$ , the matrix ${b}$ is centred. By dealing with the balance of matrix ${A}$ , the ill-conditioned degree of linear equations is weakened. Finally, the optimal solution serves as a good approximation of the original problem. The proposed method is applied to the PD location in a transformer, and the localization results are compared with direct Newton iteration and direct TSVD methods. A comparison of the experimental results shows that the method in this paper can reduce the ill-conditioned degree of a linear equation, and effectively weaken the influence of time difference error on the solution. The localization results of this method are better than the other two methods.