A self-correcting method for bad data in flexible DC line protection based on digital twinning

Abstract

DC line protection is the key to the development of flexible DC grids, and fault identification within 3 ms is required. Existing scholars have proposed a digital twinning based flexible DC line protection method with high speed and sensitivity advantages. However, its reliability is easily affected by transformer measurements, which may lead to protection misoperation. The requirements of DC control protection equipment can be hardly meted by existing bad data detection methods with insufficient accuracy and rapidity. Therefore, in order to improve the reliability of this protection method, a self-correcting method of bad data based on the moving average method is proposed in this paper. The predicted value of the measured data is obtained by using the moving average method according to the time sequence characteristics of the steady change of the measured data, and the bad data is detected and corrected by comparing the predicted error and the actual error, without iterative calculation and pre-trained model. The results of a simulation test using the four-terminal flexible DC grid show that the proposed method has higher accuracy and rapidity with good error correction performance than any existing method does, and it can be adapted to the protection method and improve the anti-interference capability as well as the reliability of protection in an effective manner.

Keywords

• flexible dc
• dynamic state estimation
• dc transformers
• bad data identification
• time series prediction
• moving average method