Type identification and time location of multiple power quality disturbances based on KF-ML-aided DBN

Type identification and time location of power quality disturbances (PQDs) is the key to adopting corresponding measures to suppress disturbances. More complex multiple disturbances caused by the overlapping of different micro-grids make it a challenging task. The paper proposes a hybrid approach combing KF-ML (Kalman filter based on maximum likelihood) with deep belief network (DBN) for dealing with PQDs. To be specific, the KF-ML is firstly applied to reduce noise from the original distorted signal, and the innovation sequence obtained by KF-ML can be used to locate starting-ending times of PQDs. Then, the DBN, which fuses feature extraction and classification into a single block, is capable of recognizing the type of PQDs accurately. To verify the effectiveness of the proposed method, 20 classes of PQDs with noise interference are tested, and experiment results show that the detection time of the proposed method is very close to the set time, and the absolute error of time location is less than 0.3 ms. The average classification accuracy at different noise levels reaches about 95%, and is very high even with more disturbances combined. Thus, the proposed method is immune to noise and less affected with more disturbances combined relative to other methods.

Automated summary

The paper proposes a hybrid approach combining KF-ML and DBN to identify power quality disturbances effectively, with experimental results showing close detection time to set time, minimal absolute error in time location, and high classification accuracy under different noise levels.