Power Data Quality Improvement Through PMU Bad Data Detection Based on Deep Complex Network

Abstract

Phasor Measurement Units (PMUs) enable the switching of devices in various power signal modes. A jitter or glitch in a signal cause bad data and also the PMU data will spike due to a disturbance or a transmitting data mistake. As a result of these difficulties, PMU data suffer from different degrees of data quality problems. To detect the bad data, several approaches have been already utilized however it provides some disadvantages such as complexity due to the utilization of dual identical systems separately for analyzing both real and imaginary values of PMU. Likewise, the bad data due to the topology variations have not been optimally identified. To overcome these issues a Robust Bad Data Detection Technique has been proposed in which a Deep complex neural network (DCNN) is incorporated to process the complex number having both voltage magnitude and phase angle. Deep complex Networks are also proposed with the conjunction of topology processor and AC state estimator (SE). Moreover, instead of Batch normalization weight normalization is altered due to the fusion of recurrent timestamps for measuring voltage magnitude and phase angle. The comparative analysis is done in terms of accuracy , Bad data detection capability , bad data detection range and running time with existing techniques The proposed technique provides accuracy of about 99.5% which is higher than the existing techniques.