A Power System Disturbance Classification Method Robust to PMU Data Quality Issues

Data quality issues exist in practical phasor measurement units (PMUs) due to communication errors or signal interferences. As a result, the performances of existing data-driven disturbance classification methods can be significantly affected. In this article, a fast disturbance classification metho...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 18; H. 1; S. 130 - 142
Hauptverfasser: Li, Zikang, Liu, Hao, Zhao, Junbo, Bi, Tianshu, Yang, Qixun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial neural networks
Classification
Convolution
Data integrity
Data quality problems
Deep learning
disturbance classification
Electrical measurement
Feature extraction
Informatics
Machine learning
Measuring instruments
multivariable temporal convolutional denoising network (MTCDN)
Noise reduction
Phasor measurement units
phasor measurement units (PMUs)
Phasors
Power systems
Robustness
univariate temporal convolutional denoising autoencoder (UTCN-DAE)
ISSN:1551-3203, 1941-0050
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Zusammenfassung:Data quality issues exist in practical phasor measurement units (PMUs) due to communication errors or signal interferences. As a result, the performances of existing data-driven disturbance classification methods can be significantly affected. In this article, a fast disturbance classification method that is robust to PMU data quality issues is proposed. The impacts of bad PMU measurements on disturbance classification are investigated by analyzing the feature distributions of deep learning methods. A new feature extraction scheme that uses the univariate temporal convolutional denoising autoencoder (UTCN-DAE) is proposed. It allows encoding and decoding univariate disturbance data through a temporal convolutional network to capture the temporal feature representation and is robust to bad data. Based on the features of the frequency and voltage measurements encoded by the UTCN-DAE, a two-stream enhanced network, i.e., the multivariable temporal convolutional denoising network is proposed to achieve optimal feature extraction of multivariate time series by feature fusion. The classification is performed using a multilayered deep neural network and Softmax classifier. Extensive results obtained on the IEEE 39-bus system as well as a large-scale power system in China with field PMU measurements show that the proposed method achieves the highest classification accuracy and computational efficiency as compared to other deep learning algorithms.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3072397