# US 2010/0168931 A1 · Systems and methods for alarm filtering and management within a real-time data acquisition and monitoring environment > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2010/0168931 A1 - Document ID: US20100168931A1 - Status: Published application - Kind: A1 - Application number: 12/690,062 - Filed: 2010-01-19 - Published: 2010-07-01 - Earliest claimed: 2006-04-12 - Assignees: Edsa Micro Corporation - Inventors: Adib Nasle - Web page: https://patentgrep.com/patent/US20100168931A1 ## CPC - G05B 17/02: electric - G05B 23/0272: {Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user} - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G06F 2111/02: CAD in a network environment, e.g. collaborative CAD or distributed simulation - G06F 2119/06: Power analysis or power optimisation - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A system for filtering and interpreting real-time sensory data from 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 virtual system modeling engine, an analytics engine, and a decision 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 decision engine compares the real-time data output against the predicted data output to filter out and interpret indicia of electrical system health and performance. The client terminal is communicatively connected to the power analytics server and configured to display the filtered and interpreted indicia. ## Claims ### Claim 1 A method for filtering and interpreting real-time sensory data from an electrical system, comprising: updating a virtual system model of the electrical system in response to real-time data; generating predicted data output from the electrical system using the updated virtual system model; comparing the real-time data against the predicted data; identifying an alarm condition based on deviations detected during the comparison; and communicating the alarm condition to a display. ### Claim 2 (depends on 1) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, wherein the displayed alarm condition is color coded to indicate severity. ### Claim 3 (depends on 1) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, wherein the alarm condition is related to a power flow parameter of the electrical system. ### Claim 4 (depends on 3) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 3, wherein the power flow parameter is a kilowatt (kW) value. ### Claim 5 (depends on 3) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 3, wherein the power flow parameter is a kVAR value. ### Claim 6 (depends on 3) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 3, wherein the power flow parameter is a power factor value. ### Claim 7 (depends on 1) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, wherein the alarm condition relates to a short circuit fault current value that is indicative of stress and withstand capabilities of protective devices on the electrical system. ### Claim 8 (depends on 1) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, wherein the alarm condition relates to power quality on the electrical system. ### Claim 9 (depends on 8) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 8, wherein power quality is indicated by voltage value distortions on the electrical system. ### Claim 10 (depends on 8) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 8, wherein power quality is indicated by current value distortions on the electrical system. ### Claim 11 (depends on 1) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, wherein alarm condition relates to electrical system loading. ### Claim 12 (depends on 1) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, wherein the alarm condition relates to electrical system power capacity. ### Claim 13 (depends on 1) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, wherein the alarm condition relates to electrical system frequency. ### Claim 14 (depends on 1) The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, wherein the alarm condition relates to electrical system harmonics waveform. ### Claim 15 A method for filtering and interpreting real-time sensory data from an electrical system, comprising: updating a virtual system model of the electrical system in response to real-time data; generating predicted data output from the electrical system using the updated virtual system model; comparing the real-time data against the predicted data; identifying an alarm condition based on the comparison; and communicating the alarm condition to a display.