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.

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 using a neural network algorithm, the adaptive prediction engine 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; and 
   a client terminal communicatively connected to the power analytics server, the client terminal configured to display the forecasted aspect.   
     
     
         2 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 1 , wherein the neural network algorithm is a back propagation algorithm. 
     
     
         3 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 2 , wherein the measure of error is a sum squared error. 
     
     
         4 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  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. 
     
     
         5 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 1 , wherein the monitored system is an electrical system. 
     
     
         6 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 1 , wherein the virtual system model includes current system components and operational parameters comprising the monitored system. 
     
     
         7 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 6 , wherein the current system components are comprised of static components and rotating components. 
     
     
         8 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 1 , wherein the forecasted aspect is a predicted ability of the electrical system to resist system output deviations from defined tolerance limits of the electrical system. 
     
     
         9 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 1 , wherein the forecasted aspect is a predicted reliability and availability of the electrical system. 
     
     
         10 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 1 , wherein the forecasted aspect is a predicted total power capacity of the electrical system. 
     
     
         11 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 1 , wherein the forecasted aspect is a predicted ability of the electrical system to maintain availability of total power capacity. 
     
     
         12 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 1 , wherein the forecasted aspect is a predicted utilization of the total power capacity of the electrical system. 
     
     
         13 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 4 , wherein the forecasted aspect is a predicted ability of the electrical system to withstand the simulated contingency event that results in stress to the electrical system. 
     
     
         14 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 4 , wherein the contingency event relates to load shedding. 
     
     
         15 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 4 , wherein the contingency event relates to load adding. 
     
     
         16 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 4 , wherein the contingency event relates to loss of utility power supply to the electrical system. 
     
     
         17 . The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in  claim 4 , wherein the contingency event relates to a loss of distribution infrastructure associated with the electrical system. 
     
     
         18 . 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 using a neural network algorithm;   optimizing 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; and   forecasting an aspect of the monitored system using the neural network algorithm.   
     
     
         19 . The 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, as recited in  claim 18 , wherein the monitored system is an electrical system. 
     
     
         20 . The 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, as recited in  claim 19 , further including:
 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.   
     
     
         21 . The 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, as recited in  claim 20 , wherein the contingency event relates to execution of a start-up sequence for a component of the electrical system. 
     
     
         22 . The 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, as recited in  claim 20 , wherein the contingency event relates to load shedding. 
     
     
         23 . The 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, as recited in  claim 20 , wherein the contingency event relates to critical clearing time of a tripped circuit breaker within the electrical system. 
     
     
         24 . The 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, as recited in  claim 20 , wherein the contingency event relates to a change in protective device operations and interactions. 
     
     
         25 . The 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, as recited in  claim 20 , wherein the contingency event relates to loss of utility power supply to the monitored system. 
     
     
         26 . The 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, as recited in  claim 20 , wherein the contingency event relates to loss of a generator in the electrical system. 
     
     
         27 . The 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, as recited in  claim 20 , wherein the contingency event relates to a loss of distribution infrastructure associated with the electrical system. 
     
     
         28 . The 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, as recited in  claim 20 , wherein the forecasted aspect is the electrical system's ability to maintain sufficient active and reactive power reserves to cope with the contingency event. 
     
     
         29 . The 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, as recited in  claim 20 , wherein the forecasted aspect is the electrical system's ability to operate safely after the contingency event. 
     
     
         30 . The 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, as recited in  claim 20 , wherein the forecasted aspect is the electrical system's ability to operate reliably after the contingency event. 
     
     
         31 . The 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, as recited in  claim 30 , wherein the electrical system's operational reliability is measured as a system reliability index rating. 
     
     
         32 . The 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, as recited in  claim 20 , wherein the forecasted aspect is the electrical system's ability to continue to operate with minimum operating cost after the contingency event. 
     
     
         33 . The 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, as recited in  claim 20 , wherein the forecasted aspect is the electrical system's ability to provide an acceptably high level of power quality after the contingency event. 
     
     
         34 . The 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, as recited in  claim 33 , wherein the level of power quality is measured by the electrical system's ability to maintain voltage and frequency within a tolerance. 
     
     
         35 . The 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, as recited in  claim 18 , wherein the virtual system model is updated to reflect real-time weather conditions impacting the electrical system. 
     
     
         36 . The 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, as recited in  claim 20 , wherein the forecasted aspect is the electrical system's operational stability under real-time weather conditions. 
     
     
         37 . The 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, as recited in  claim 20 , wherein the forecasted aspect is the electrical system's ability to recover from a contingency event without violating operational constraints of the electrical system.