Systems and Methods for Real-Time Forecasting and Predicting of Electrical Peaks and Managing the Energy, Health, Reliability, and Performance of Electrical Power Systems Based on an Artificial Adaptive Neural Network

Abstract

A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are 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 virtual system modeling engine, an analytics engine, an adaptive prediction engine. 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 adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm. The adaptive prediction engine is further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm.

The invention claimed is: 
     
         1 . A system for making real-time predictions about the health, reliability, and performance of a monitored system, comprising:
 a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the monitored system;   a power analytics server communicatively connected to the data acquisition component, comprising,
 a virtual system modeling engine configured to generate predicted data output for the monitored system utilizing a virtual system model of the monitored system, 
 an analytics engine configured to monitor the real-time data output and the predicted data output of the monitored 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 data output and the predicted data output exceeds a threshold, and 
 an adaptive prediction engine configured to forecast an aspect of the monitored system, the adaptive prediction engine further configured to process the real-time data output and automatically minimizing a measure of error between the real-time data output and an estimated data output predicted by the adaptive prediction engine; and 
   a client terminal communicatively connected to the power analytics server, the client terminal configured to display the forecasted aspect.   
     
     
         2 . The system of  claim 1 , wherein the adaptive prediction engine is further configured to forecast the aspect of the monitored system when subjected to a simulated contingency event. 
     
     
         3 . The system of  claim 1 , wherein the monitored system is an electrical system. 
     
     
         4 . The system of  claim 1 , wherein the monitored system is a microgrid. 
     
     
         5 . The system of  claim 1 , wherein the monitored system is a data center. 
     
     
         6 . The system of  claim 1 , wherein the virtual system model includes current system components and operational parameters comprising the monitored system. 
     
     
         7 . The system of  claim 6 , wherein the current system components comprise static components and rotating components. 
     
     
         8 . The system for of  claim 1 , wherein the forecasted aspect is a predicted ability of the monitored system to resist system output deviations from defined tolerance limits of the monitored system. 
     
     
         9 . The system of  claim 1 , wherein the forecasted aspect is a predicted reliability and availability of an electrical system. 
     
     
         10 . The system of  claim 1 , wherein the forecasted aspect is a predicted total power capacity of an electrical system. 
     
     
         11 . The system of  claim 1 , wherein the forecasted aspect is a predicted ability of an electrical system to maintain availability of a total power capacity and/or a predicted ability of the electrical system to withstand the simulated contingency event that results in stress to the electrical system. 
     
     
         12 . The system of  claim 1 , wherein the forecasted aspect is a predicted utilization of the total power capacity of an electrical system. 
     
     
         13 . The system of  claim 2 , wherein the contingency event relates to load shedding, load adding, loss of a power supply, and/or loss of distribution infrastructure to an electrical system. 
     
     
         14 . A computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, comprising:
 receiving real-time data output from one or more sensors interfaced to the monitored system;   generating predicted data output for the one or more sensors interfaced to the monitored system utilizing a virtual system model of the monitored system;   calibrating the virtual system model of the monitored system when a difference between the real-time data output and the predicted data output exceeds a threshold;   processing the real-time data output;   minimizing a measure of error between the real-time data output and an estimated and/or predicted data output; and   forecasting an aspect of the monitored system.   
     
     
         15 . The method of  claim 14 , wherein the monitored system is an electrical system. 
     
     
         16 . The method of  claim 14 , wherein the monitored system is a microgrid. 
     
     
         17 . The method of  claim 14 , wherein the monitored system is a datacenter. 
     
     
         18 . The method of  claim 14 , further including the steps of:
 choosing a simulated contingency event to subject the monitored system to; and   forecasting the aspect of the monitored system by running an analysis of the calibrated virtual system model operating under conditions simulating the contingency event chosen.   
     
     
         19 . The method of  claim 18 , wherein the contingency event relates to execution of a start-up sequence for a component of an electrical system, load shedding, load adding, loss of a power supply, loss of distribution infrastructure to the electrical system, critical clearing time of a tripped circuit breaker within the electrical system, and/or a change in protective device operations. 
     
     
         20 . The method of  claim 18 , wherein the forecasted aspect is the electrical system's ability to maintain sufficient active and reactive power reserves to cope with the contingency event. 
     
     
         21 . The method of  claim 18 , wherein the forecasted aspect is the electrical system's ability to operate safely and/or reliably after the contingency event.