Systems and Methods for Automatic Real-Time Capacity Assessment for Use in Real-Time Power Analytics of an Electrical Power Distribution System

Abstract

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.

1 - 25 . (canceled) 
     
     
         26 . A system for analyzing a real-time power capacity assessment capability 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 model and mirror the electrical system and generate predicted output data of the electrical system; 
 an analytics engine configured to monitor the real-time output data and the predicted output data of the electrical system; and 
 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 power capacity of the electrical system subjected to a contingency event; and 
   a client terminal communicatively connected to the power analytics server, the client terminal configured to allow for the selection of the contingency event and display a report of the forecasted power capacity.   
     
     
         27 . The system of  claim 26 , wherein the analytics server further comprises a calibration engine configured to update the virtual system model when a difference between the real-time output data and the predicted output data exceeds a threshold. 
     
     
         28 . The system of  claim 27 , 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 simulated output data. 
     
     
         29 . The system of  claim 26 , wherein the machine learning engine is comprised of:
 an associative memory layer;   a sensory layer; and   a neocortical model.   
     
     
         30 . The system of  claim 26 , 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. 
     
     
         31 . The system of  claim 26 , wherein the contingency event relates to load shedding. 
     
     
         32 . The system of  claim 26 , wherein the contingency event relates to load adding. 
     
     
         33 . The system of  claim 26 , wherein the contingency event relates to loss of utility power supply to the electrical system. 
     
     
         34 . The system of  claim 26 , wherein the contingency event relates to a loss of distribution infrastructure associated with the electrical system. 
     
     
         35 . The system of  claim 26 , wherein the report includes a forecast of total system power capacity. 
     
     
         36 . The system of  claim 26 , wherein the report includes a forecast of available system power capacity. 
     
     
         37 . The system of  claim 26 , wherein the report includes a forecast of present utilized system capacity. 
     
     
         38 . A system for conducting real-time power capacity assessment of an electrical system subjected to a contingency event, 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 monitor the real-time output data and the predicted output data of the electrical system, the analytics engine further configured to initiate 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; and 
 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 generate a report that forecasts power capacity of the electrical system subjected to a contingency event; and 
   a client terminal communicatively connected to the power analytics server, the client terminal configured to allow for the selection of the contingency event and display the report of the forecasted power capacity.   
     
     
         39 . The system of  claim 38 , 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 simulated output data. 
     
     
         40 . The system of  claim 38 , wherein the virtual system model includes voltage stability model data for components comprising the electrical system. 
     
     
         41 . The system of  claim 40 , wherein the voltage stability model data includes load scaling data. 
     
     
         42 . The system of  claim 40 , wherein the voltage stability model data includes generation scaling data. 
     
     
         43 . The system of  claim 40 , wherein the voltage stability model data includes load growth factor data. 
     
     
         44 . The system of  claim 40 , wherein the voltage stability model data includes load growth increment data. 
     
     
         45 . The system of  claim 38 , wherein the contingency event relates to load shedding. 
     
     
         46 . The system of  claim 38 , wherein the contingency event relates to load adding. 
     
     
         47 . The system of  claim 38 , wherein the contingency event relates to loss of utility power supply to the electrical system. 
     
     
         48 . The system of  claim 38 , wherein the contingency event relates to a loss of distribution infrastructure associated with the electrical system. 
     
     
         49 . The system of  claim 38 , 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. 
     
     
         50 . The system of  claim 38 , wherein the report includes a forecast of total system power capacity. 
     
     
         51 . The system of  claim 38 , wherein the report includes a forecast of available system power capacity. 
     
     
         52 . The system of  claim 38 , wherein the report includes a forecast of present utilized system capacity.