Real-time predictive systems for intelligent energy monitoring and management of electrical power networks

Abstract

A system for intelligent monitoring and management 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 acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

1 . A system for intelligent monitoring and management of an electrical system, comprising:
 a power analytics server and a data acquisition component in network communication;   wherein the data acquisition component is configured to acquire real-time data output from the electrical system, wherein the electrical system comprises a plurality of components;   wherein the power analytics server comprises a virtual system modeling engine, an analytics engine, and a 3D visualization engine;   wherein the virtual system modeling engine is configured to generate predicted data output for the electrical system based on a virtual system model of the electrical system, wherein the virtual system model comprises virtual component data corresponding to the plurality of components of the electrical system and relationships between the plurality of components of the electrical system;   wherein the analytics engine is configured to monitor the real-time data output and the predicted data output of the electrical system and determine a difference between the real-time data output and the predicted data output;   wherein the analytics engine is configured to institute a calibration and synchronization operation to update the virtual system model if the difference between the real-time data output and the predicted data output exceeds a first threshold but not a second threshold, wherein the second threshold is higher than the first threshold; and   wherein the 3D visualization engine is configured to generate at least one 3D view based on the virtual component data for at least one of the plurality of components of the electrical system.   
     
     
         2 . The system of  claim 1 , wherein the analytics engine does not initiate the calibration and synchronization operation when the difference between the real-time data output and the predicted data output does not exceed the first threshold. 
     
     
         3 . The system of  claim 1 , wherein the analytics engine generates an alarm when the difference between the real-time data output and the predicted data output exceeds the second threshold. 
     
     
         4 . The system of  claim 1 , wherein the power analytics engine further includes a machine learning engine, wherein the machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output and forecast an aspect of the electrical system. 
     
     
         5 . The system of  claim 4 , wherein the power analytics server further comprises an energy management system engine configured to process the real-time data output, the predicted data output, and the forecast aspect to generate a user interface that conveys an operational state of the electrical system. 
     
     
         6 . The system of  claim 4 , wherein the machine learning engine comprises an associative memory layer, a sensory layer, and a neocortical model. 
     
     
         7 . The system of  claim 1 , wherein the virtual system model comprises current system components and operational parameters of the electrical system. 
     
     
         8 . The system of  claim 1 , wherein the power analytics server further comprises a real-time energy pricing engine operable to generate real-time utility power pricing data using real-time dynamic utility power pricing data. 
     
     
         9 . The system of  claim 8 , wherein the virtual system modeling engine is further operable to generate predicted utility power pricing data using the virtual system model of the electrical system and the real-time dynamic utility power pricing data. 
     
     
         10 . The system of  claim 8 , wherein the real-time dynamic utility power pricing data is received from a utility power provider supplying electrical power to the electrical system. 
     
     
         11 . A system for intelligent monitoring and management of an electrical system, comprising:
 a power analytics server and a data acquisition component in network communication;   wherein the data acquisition component is configured to acquire real-time data output from the electrical system, wherein the electrical system comprises a plurality of components;   wherein the power analytics server comprises a virtual system modeling engine, an analytics engine, and a machine learning engine;   wherein the virtual system modeling engine is configured to generate predicted data output for the electrical system based on a virtual system model of the electrical system, wherein the virtual system model comprises virtual component data corresponding to the plurality of components of the electrical system and relationships between the plurality of components of the electrical system;   wherein the analytics engine is configured to monitor the real-time data output and the predicted data output of the electrical system and determine a difference between the real-time data output and the predicted data output;   wherein the machine learning engine configured to store and process patterns observed from the real-time data output and the predicted data output and forecast an aspect of the electrical system; and   wherein the analytics engine is configured to institute a calibration and synchronization operation to update the virtual system model if the difference between the real-time data output and the predicted data output exceeds a first threshold but not a second threshold, wherein the second threshold is higher than the first threshold.   
     
     
         12 . The system of  claim 11 , wherein the power analytics server further comprises a 3D visualization engine, wherein the 3D visualization engine is configured to generate at least one 3D view based on the virtual component data for at least one of the plurality of components of the electrical system. 
     
     
         13 . The system of  claim 11 , wherein the power analytics server further comprises an energy management system engine configured to process the real-time data output, the predicted data output, and the forecasted aspect. 
     
     
         14 . The system of  claim 13 , wherein the energy management system engine is further configured to apply a historical trending algorithm to the stored real-time data output, the predicted data output, and the forecasted aspect to provide a historical data trending display. 
     
     
         15 . The system of  claim 11 , wherein the forecasted aspect is a predicted ability of the electrical system to withstand a contingency event that results in stress to the electrical system. 
     
     
         16 . The system of  claim 15 , wherein the contingency event relates to at least one of load adding, load shedding, loss of utility power supply, and/or loss of distribution infrastructure with the electrical system. 
     
     
         17 . The system of  claim 11 , further comprising a client terminal communicatively connected to the power analytics server, wherein the client terminal is configured to convey an operational state of the electrical system. 
     
     
         18 . A method for intelligent monitoring and management of an electrical system, comprising:
 providing a power analytics server and a data acquisition component in network communication, wherein the power analytics server comprises a virtual system modeling engine and an analytics engine;   the data acquisition component acquiring real-time data output from the electrical system, wherein the electrical system comprises a plurality of components;   the virtual system modeling engine generating predicted data output for the electrical system based on a virtual system model of the electrical system, wherein the virtual system model comprises virtual component data corresponding to the plurality of components of the electrical system and relationships between the plurality of components of the electrical system; and   the analytics engine monitoring the real-time data output and the predicted data output of the electrical system and determining a difference between the real-time data output and the predicted data output, wherein the analytics engine is configured to institute a calibration and synchronization operation to update the virtual system model if the difference between the real-time data output and the predicted data exceeds a first threshold but not a second threshold, wherein the second threshold is higher than the first threshold.   
     
     
         19 . The method of  claim 18 , wherein the power analytics server further comprises a machine learning engine, and further comprising the machine learning engine storing and processing patterns observed from the real-time data output and the predicted data output, the machine learning engine forecasting an aspect of the electrical system based on the patterns observed from the real-time data output and the predicted data output, wherein the machine learning engine storing and processing patterns observed from the real-time data output and the predicted data output, and the machine learning engine forecasting an aspect of the electrical system based on the patterns observed from the real-time data output and the predicted data output. 
     
     
         20 . The method of  claim 19 , wherein the forecasted aspect is at least one of a predicted ability of the electrical system to resist system output deviations from defined tolerance limits of the electrical system, a predicted reliability and availability of the electrical system, a predicted total power capacity of the electrical system, a predicted ability of the electrical system to maintain availability of total power capacity, a predicted utilization of the total power capacity of the electrical system, and a predicted ability of the electrical system to withstand a contingency event that results in stress to the electrical system.