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Open Access

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

Guanyu Tian, Yingzhong Gu( ), Zhe Yu, Qibing Zhang, Di Shi, Qun Zhou, Zhiwei Wang
GEIRI North America, San Jose, CA 95134 USA
GEIRI North America, San Jose, CA 95134 USA on leave from Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816 USA
Grid Dispatch Center, State Grid Jiangsu Electric Power Company Ltd., Nanjing 210024, China
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816 USA
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Abstract

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.

Keywords

Autoencoderbad data processinglinear state estimationPMU

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CSEE Journal of Power and Energy Systems
Volume 8 Issue 2,
March 2022
Pages 640-651
DOI: 10.17775/CSEEJPES.2020.06270
Cite this article:
Tian G, Gu Y, Yu Z, et al. Enhanced Denoising Autoencoder-aided Bad Data Filtering for Synchrophasor-based State Estimation. CSEE Journal of Power and Energy Systems, 2022, 8(2): 640-651. https://doi.org/10.17775/CSEEJPES.2020.06270

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Received: 22 November 2020
Revised: 23 March 2021
Accepted: 10 May 2021
Published: 10 September 2021
© 2020 CSEE
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