Anomaly Detection Using LSTM-Based Variational Autoencoder in Unsupervised Data in Power Grid
Dibyajyoti Guha, Rajdeep Chatterjee, Biplab Sikdar · IEEE Systems Journal · 2023
This article proposes a deep generative model for anomaly detection in unsupervised power grid data. One-class classifier-based methods suffer from performance degradation when training data contain anomalous samples. Due to the temporal characteristics in most of the power grid datasets, we explore a long short-term memory-variational autoencoder-based deep generative model that can tolerate the moderate presence of anomalous data during training instead of standard data. This work demonstrates the advantage of reconstruction-based methods over clustering-based methods. As part of the reparameterization of the latent layer, a method is proposed by employing wavelet decomposition of the wavelet coefficients found from the high and medium frequency representations of the input time-series data. For further improvement, we have incorporated a$\log \text{cos}h$-based cost function instead of the traditional consideration of the$L_{2}$norm-based cost function. The numerical results demonstrate an improvement of performance metrics, such as AUC by 0.1–0.2 of our method over other benchmark methods. The transient stability threshold ($\delta$) is an important system parameter in the performance assessment of power grid systems. Through time domain simulations, it has been shown that$\delta =0.3$obtains optimal accuracy for transient stability assessment in the IEEE NewEngland-39 bus.