A system for conducting a real-time power capacity assessment of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The power analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine. The machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output, forecasting power capacity of the electrical system subjected to a simulated contingency event.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for real-time power capacity assessment of an electrical system, comprising: a data acquisition component configured to acquire real-time output data from the electrical system; a power analytics server communicatively connected to the data acquisition component, comprising, a virtual system modeling engine configured to create a virtual system model of the electrical system and generate predicted output data of the electrical system; an analytics engine configured to initiate a calibration and synchronization operation to update the virtual system model when a difference between the real-time data output data and the predicted output data output exceeds a threshold; a machine learning engine configured to store and process patterns observed from the real-time output data and the predicted output data, the machine learning engine further configured to forecast a power capacity of the electrical system subjected to a contingency event in real time, wherein the machine learning engine comprises an associative memory layer, a sensor layer and a neocortical model; and a client terminal communicatively connected to the power analytics server, the client terminal configured to enable the selection of the contingency event and display a report of the power capacity. 2. The system of claim 1, wherein the threshold is a Defined Difference Tolerance (DDT) value for at least one of the frequency deviation, voltage deviation, power factor deviation, and other deviations between the real-time output data and the predicted output data. 3. The system of claim 1, wherein the power capacity is a measure of the electrical system's ability to maintain an acceptable voltage profile under different electrical system topologies and load changes. 4. The system of claim 1, wherein the contingency event relates to load shedding. 5. The system of claim 1, wherein the contingency event relates to load adding. 6. The system of claim 1, wherein the contingency event relates to a loss of utility power supply to the electrical system. 7. The system of claim 1, wherein the contingency event relates to a loss of distribution infrastructure associated with the electrical system. 8. The system of claim 1, wherein the report includes a forecast of total system power capacity. 9. The system of claim 1, wherein the report includes a forecast of available system power capacity. 10. The system of claim 1, wherein the report includes a forecast of present utilized system capacity. 11. A method for assessing real-time power capacity of an electrical system, comprising: providing a data acquisition component and a client terminal constructed and configured in network communication with a server processor, wherein the server processor comprises a virtual system modeling engine, an analytics engine, and a machine learning engine; the data acquisition component acquiring real-time output data from the electrical system; the virtual system modeling engine generating predicted output data of the electrical system based on a virtual system model of the electrical system; the analytics engine initiating a calibration and synchronization operation to update the virtual system model when a difference between the real-time output data and the predicated output data exceeds a threshold; the machine learning engine forecasting a power capacity of the electrical system subjected to a contingency event in real time; the machine learning engine generating a report of the power capacity of the electrical system subjected to the contingency event; and the client terminal displaying the report of the power capacity. 12. The method of claim 11, wherein the threshold is a Defined Difference Tolerance (DDT) value for at least one of the frequency deviation, voltage deviation, power factor deviation, and other deviations between the real-time output data and the predicted output data. 13. The method of claim 11, wherein the virtual system model comprises voltage stability model data for components in the electrical system. 14. The method of claim 13, wherein the voltage stability model data comprises load scaling data, generation scaling data, load growth factor data, load growth increment data. 15. The method of claim 11, wherein the contingency event relates to load shedding, load adding, a loss of utility power supply to the electrical system, a loss of distribution infrastructure associated with the electrical system. 16. The method of claim 11, wherein the power capacity is a measure of the electrical system's ability to maintain an acceptable voltage profile when subjected to the contingency event. 17. The method of claim 11, wherein the report comprises a forecast of total system power capacity, a forecast of available system power capacity, and/or a forecast of present utilized system capacity. 18. The method of claim 11, wherein the machine learning engine comprises an associative memory layer, a sensor layer and a neocortical model. 19. The method of claim 11, further comprising the machine learning engine storing and processing patterns observed from the real-time output data and the predicted output data. 20. The method of claim 11, further comprising the client terminal selecting the contingent event for the forecasting of the power capacity.