FIG. 21 is a diagram illustrating how the HTM Pattern Recognition and Machine Learning Engine works in conjunction with the other elements of the analytics system to make predictions about the operational aspects of a monitored system, in accordance with one embodiment. As depicted herein, the HTM Pattern Recognition and Machine Learning Engine 551 is housed within an analytics server 116 and communicatively connected via a network connection 114 with a data acquisition hub 112, a client terminal 128 and a virtual system model database 526. The virtual system model database 526 is configured to store the virtual system model of the monitored system. The virtual system model is constantly updated with real-time data from the data acquisition hub 112 to effectively account for the natural aging effects of the hardware that comprise the total monitored system, thus, mirroring the real operating conditions of the system. This provides a desirable approach to predicting the operational aspects of the monitored power system operating under contingency situations.
The HTM Machine Learning Engine 551 is configured to store and process patterns observed from real-time data fed from the hub 112 and predicted data output from a real-time virtual system model of the monitored system. These patterns can later be used by the HTM Engine 551 to make real-time predictions (forecasts) about the various operational aspects of the system.
