Transient Frequency Response Test and Measurement Error Prediction of
DCTV based on AI
Abstract
To solve the problem that the Frequency Response (FR) test scheme and
equipment of the traditional DC Voltage transformer (DCTV) used for DC
transmission are insufficient, and the Measurement Error (M-E) of the
related measurement system is difficult to achieve accurate prediction,
a temporary FR test and M-E prediction method of DCTV based on
Artificial Intelligence (AI) is proposed. Firstly, the frequency
characteristic of DC side voltage of DCTV is analyzed. On this basis, a
DCTV transient FR testing method based on transient AC & DC
superposition was developed. Then, the method of voltage sudden change
and phase correction is used to achieve transient process DCTV response
time testing. Finally, the Ant Colony Optimization (ACO) algorithm was
improved by combining an adaptive inertia weight improvement strategy,
achieving accurate prediction of the M-E of DCTV. The proposed AI based
DCTV transient FR testing and M-E prediction method were compared and
analyzed with the other three methods through simulation experiments.
The results indicate that under the same experimental conditions, the
prediction results of the DCTV measurement ratio error and phase angle
error of the proposed method are optimal.