The real time anomaly detection in wide area monitoring of smart grids is critical to enhance the reliability of power systems. However, capturing the features of anomalous interruption and then detecting them at real time is difficult for large-scale smart grids, because the measurement data volume and complexity increases drastically with the exponential growth of data from the immense intelligent monitoring devices to be rolled out and the need for fast information retrieval from those mass data. Most of existing anomaly detection methods fail to handle it well. This paper proposes a spatial-Temporal correlation based anomalous behavior model to capture the characteristics of anomaly such as transmission line outages in smart grid. Inspired by Ledoit-Wolf Shrinkage (LWS) method, we develop the real time anomaly detection (ReTAD) algorithm to overcome the issue of gigantic measurement data volume. The proposed algorithm is not only suitable for large number of power systems with high dimensional measurement data, but at the same time is also low computational complexity to apply for real time detection. Using 14-, 30, and 2383-bus systems, our experimental study demonstrates that our proposed ReTAD algorithm successfully detects the anomalous events at real time.