Influence of autoregressive noise on phasor data based disturbance classification

The online classification of grid disturbances in power transmission systems has been investigated since many years and shows promising results on measured and simulated PMU signals. Nonetheless, a practical deployment of machine learning techniques is still challenging due to robustness problems, which may lead to severe misclassifications in the model application. This paper formulates an advanced evaluation procedure for disturbance classification methods by introducing additional measurement noise, unknown operational points, and unknown disturbance events in the test dataset. Based on preliminary work, Siamese Sigmoid Networks are used as classification approach and are compared against several benchmark models for a simulated power transmission system at 400 kV. Different test scenarios are proposed to evaluate the disturbance classification models assuming a limited and full observability of the grid with PMUs.

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Influence of autoregressive noise on phasor data based disturbance classification

Cited by 2 publications

References 20 publications

Robust disturbance classification in power transmission systems with denoising recurrent autoencoders

Robust disturbance classification in power transmission systems with denoising recurrent autoencoders

Influence of nuisance variables on the PMU-based disturbance classification in power transmission systems

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