# US 10,371,740 B2 · Power system disturbance localization using recurrence quantification analysis and minimum-volume-enclosing ellipsoid > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,371,740 B2 - Document ID: US10371740B2 - Status: Granted - Kind: B2 - Application number: 15/609,861 - Filed: 2017-05-31 - Published: 2018-12-06 - Granted: 2019-08-06 - Earliest claimed: 2017-05-31 - Assignees: University of Tennessee Research Foundation - Applicants: University of Tennessee Research Foundation - Inventors: Yi Cui; Feifei Bai; Wenxuan Yao; Yong Liu; Ling Wu; Yilu Liu - Web page: https://patentgrep.com/patent/US10371740B2 ## CPC - G01R 31/086: {in power transmission or distribution networks, i.e. with interconnected conductors} - G01R 31/088: {Aspects of digital computing} - G05F 1/66: Regulating electric power - G06Q 50/06: Energy or water supply - H02H 1/0092: {concerning the data processing means, e.g. expert systems, neural networks} - H02H 3/042: {combined with means for locating the fault (locating faults in cables G01R31/08)} - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) - G05F: SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H02H: EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS (indicating or signalling undesired working conditions G01R, e.g. G01R31/00, G08B; locating faults along lines G01R31/08; emergency protective devices H01H) ## Abstract A method for determining a location of a disturbance in a power system is provided. The method includes receiving data from a plurality of sensors distributed across the power system; performing a recurrence quantification analysis on the received data to identify a predetermined number of sensors, from the plurality of sensors, that are closest to the disturbance; constructing a plurality of minimum-volume-enclosing ellipsoids based on and enclosing the data received from the identified sensors; extracting one or more parameters from the plurality of minimum-volume-enclosing ellipsoids; inputting the one or more parameters into a multivariate-random-forest regression algorithm to determine the location of the disturbance and a power mismatch corresponding to the disturbance; and presenting, on one or more display units, the determined location of the disturbance and the determined power mismatch. ## Claims ### Claim 1 A method for determining a location of a disturbance in a power system, comprising: receiving data from a plurality of sensors distributed across the power system; identifying, based on the data from the plurality of sensors, a number of sensors that are closest to the disturbance; constructing a plurality of minimum-volume-enclosing ellipsoids based on and enclosing the data received from the identified sensors; extracting one or more parameters from the plurality of ellipsoids; inputting the one or more parameters into a regression algorithm to determine the location of the disturbance; and modifying an operation of at least one element in the power system to mitigate an impact of the disturbance on the power system based on the determined location of the disturbance. ### Claim 2 (depends on 1) The method of claim 1, wherein the data comprise frequency data. ### Claim 3 (depends on 1) The method of claim 1, wherein the identifying the number of sensors comprises performing a recurrence quantification analysis on the received data. ### Claim 4 (depends on 3) The method of claim 3, wherein the identifying the number of sensors by performing a recurrence quantification analysis on the received data comprises: identifying, from the data, an occurrence time of the disturbance; calculating, about the occurrence time, step changes in subsequent samples of the data; calculating, based on the step changes, a distance matrix for each of the plurality of sensors; calculating a space diameter based on the distance matrix for each of the plurality of sensors; determining a recurrence rate, for each of the plurality of sensors, based on the corresponding distance matrix and the corresponding space diameter; and identifying the number of sensors as a subset of the plurality of sensors with the highest recurrence rates. ### Claim 5 (depends on 1) The method of claim 1, wherein the one or more parameters comprise volumes of the plurality of ellipsoids, centers of the plurality of ellipsoids, orientations of the plurality of ellipsoids, time derivatives of the volumes, time derivatives of the centers, or time derivatives of the orientations. ### Claim 6 (depends on 1) The method of claim 1, wherein the regression algorithm is a multivariate-random-forest regression algorithm. ### Claim 7 (depends on 1) The method of claim 1, further comprising, prior to the inputting the one or more parameters into the regression algorithm, training the regression algorithm with historical data from the plurality of sensors. ### Claim 8 (depends on 1) The method of claim 1, wherein the inputting the one or more parameters into the regression algorithm comprises determining a power mismatch corresponding to the disturbance. ### Claim 9 (depends on 8) The method of claim 8, further comprising presenting, on one or more display units, the determined location of the disturbance and the determined power mismatch. ### Claim 10 A system, comprising: a power system; a plurality of sensors distributed across the power system; and a computer system including one or more processors, one or more display units, and memory storing instructions adapted to be executed by the plurality of processors to perform operations comprising: receiving data from the plurality of sensors; identifying, based on the data and from the plurality of sensors, a number of sensors that are closest to a disturbance in the power system; constructing a plurality of minimum-volume-enclosing ellipsoids based on and enclosing the data received from the identified sensors; extracting one or more parameters from the plurality of ellipsoids; inputting the one or more parameters into a regression algorithm to determine the location of the disturbance; and modifying an operation of at least one element in the power system to mitigate an impact of the disturbance on the power system based on the determined location of the disturbance. ### Claim 11 (depends on 10) The system of claim 10, wherein the data comprise frequency data. ### Claim 12 (depends on 10) The system of claim 10, wherein the operation of identifying the number of sensors comprises performing a recurrence quantification analysis on the received data. ### Claim 13 (depends on 12) The system of claim 12, wherein the operation of identifying the number of sensors by performing a recurrence quantification analysis on the received data comprises: identifying, from the data, an occurrence time of the disturbance; calculating, about the occurrence time, step changes in subsequent samples of the data; calculating, based on the step changes, a distance matrix for each of the plurality of sensors; calculating a space diameter based on the distance matrix for each of the plurality of sensors; determining a recurrence rate, for each of the plurality of sensors, based on the corresponding distance matrix and the corresponding space diameter; and identifying the number of sensors as a subset of the plurality of sensors with the highest recurrence rates. ### Claim 14 (depends on 10) The system of claim 10, wherein the one or more parameters comprise volumes of the plurality of ellipsoids, centers of the plurality of ellipsoids, orientations of the plurality of ellipsoids, time derivatives of the volumes, time derivatives of the centers, or time derivatives of the orientations. ### Claim 15 (depends on 10) The system of claim 10, wherein the regression algorithm is a multivariate-random-forest regression algorithm. ### Claim 16 (depends on 10) The system of claim 10, the operations further comprising, prior to the operation of inputting the one or more parameters into the regression algorithm, training the regression algorithm with historical data from the plurality of sensors. ### Claim 17 (depends on 10) The system of claim 10, wherein the operation of inputting the one or more parameters into the regression algorithm comprises determining a power mismatch corresponding to the disturbance. ### Claim 18 (depends on 17) The system of claim 17, wherein the operations further comprises presenting, on one or more display units, the determined location of the disturbance and the determined power mismatch. ### Claim 19 A computer program product, comprising: a tangible computer readable storage medium comprising computer readable program code embodied in the medium that when executed by a processor causes the processor to perform operations comprising: receiving data from a plurality of sensors distributed across the power system; identifying, based on the data from the plurality of sensors, a number of sensors that are closest to the disturbance; constructing a plurality of minimum-volume-enclosing ellipsoids based on and enclosing the data received from the identified sensors; extracting one or more parameters from the plurality of ellipsoids; inputting the one or more parameters into a regression algorithm to determine the location of the disturbance; and modifying an operation of at least one element in the power system to mitigate an impact of the disturbance on the power system based on the determined location of the disturbance. ### Claim 20 (depends on 19) The computer program product of claim 19, wherein the one or more parameters comprise volumes of the plurality of ellipsoids, centers of the plurality of ellipsoids, orientations of the plurality of ellipsoids, time derivatives of the volumes, time derivatives of the centers, or time derivatives of the orientations.