Enhanced Denoising Autoencoder-aided Bad Data Filtering for Synchrophasor-based State Estimation

Due to its high accuracy and ease of calculation, synchrophasor-based linear state estimation (LSE) has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system (WAMS) applications. However, an increasing number of data quality concerns have been reported, among which bad data can significantly undermine the performance of LSE and many other WAMS applications it supports. Bad data filtering can be difficult in practice due to a variety of issues such as limited processing time, non-uniform and changing patterns, and etc. To pre-process phasor measurement unit (PMU) measurements for LSE, we propose an improved denoising autoencoder (DA)-aided bad data filtering strategy in this paper. Bad data is first identified by the classifier module of the proposed DA and then recovered by the autoencoder module. Two characteristics distinguish the proposed methodology: 1) The approach is lightweight and can be implemented at individual PMU level to achieve maximum parallelism and high efficiency, making it suited for real-time processing; 2) the system not only identifies bad data but also recovers it, especially for critical measurements. We use numerical experiments employing both simulated and real-world phasor data to validate and illustrate the effectiveness of the proposed method.