# US 11,451,053 B2 · Method and arrangement for estimating a grid state of a power distribution grid > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,451,053 B2 - Document ID: US11451053B2 - Status: Granted - Kind: B2 - Application number: 16/901,342 - Filed: 2020-06-15 - Published: 2020-12-17 - Granted: 2022-09-20 - Earliest claimed: 2019-06-13 - Assignees: Siemens Aktiengesellschaft - Applicants: Siemens Aktiengesellschaft - Inventors: Sylwia Henselmeyer - Web page: https://patentgrep.com/patent/US11451053B2 ## CPC - H02J 3/00: Circuit arrangements for AC mains or AC distribution networks - G06F 18/24155: {Bayesian classification} - G06Q 50/06: Energy or water supply - H02J 3/002: {Flicker reduction, e.g. compensation of flicker introduced by non-linear load} - H02J 3/003: {Load forecast, e.g. methods or systems for forecasting future load demand} - H02J 13/13: characterised by the transmission of data to equipment in the power network - H02J 2103/30: Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks - Y02B 90/20: Smart grids as enabling technology in buildings sector (smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00) - Y04S 40/12: characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment - H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - 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 - Y02B: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS - Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS ## Abstract A method estimates a grid state of an electrical power distribution grid having a multiplicity of network sections, in which a central computer arrangement is used to receive measured values from measuring devices. A state estimation device is used to make a prediction of a future grid state, wherein a voltage and a phase angle are respectively ascertained for each network section, and in that a naive Bayes method is used for the prediction. ## Claims ### Claim 1 A method for estimating a grid state of an electrical power distribution grid having a plurality of network sections, where a grid state contains a statement about the voltage magnitude or a voltage and the voltage angle or phase angle on each network section, which comprises the steps of: measuring, with measuring devices, a voltage and a phase angle for each of the plurality of network sections of the electrical power distribution grid and generating corresponding measured values; receiving, via a central computer configuration, the measured values from the measuring devices; using a state estimation device to make a prediction of a future grid state based on the measured values, wherein a voltage and a phase angle are respectively ascertained for each of the network sections; and using a machine learning method, being a naive Bayes method, for formulating the prediction; taking into consideration, for the prediction, specific predictions for individual ones of the measuring devices that each have a measurement-location-specific degree of error; and determining a measurement-location-specific weighting factor, using a respective degree of error, such that the specific predictions are weighted all more highly a lower their degree of error; and presenting the predictions to a central controller, and initiating countermeasures to the grid based on the predictions when necessary. ### Claim 2 (depends on 1) The method according to claim 1, wherein a low-voltage grid is used for at least part of the electrical power distribution grid. ### Claim 3 (depends on 1) The method according to claim 1, which further comprising disposing at least some of the measuring devices in substations. ### Claim 4 (depends on 1) The method according to claim 1, which further comprises disposing at least some of the measuring devices at feed points of the network sections. ### Claim 5 (depends on 1) The method according to claim 1, wherein the prediction estimates a grid state at least 5 hours in advance. ### Claim 6 (depends on 1) The method according to claim 1, wherein the prediction has a mean average percentage error of no more than 10%. ### Claim 7 A central computer configuration for estimating a grid state of an electrical power distribution grid having a plurality of network sections, where a grid state contains a statement about the voltage magnitude or a voltage and the voltage angle or phase angle on each network section, the central computer configuration comprising: measuring devices configured to measure a voltage and a phase angle for each of the plurality of network sections of the electrical power distribution grid and generating corresponding measured values a receiving device for receiving the measured values from said measuring devices disposed in the electrical power distribution grid; and a state estimation device configured to make a prediction of a future grid state based on the measured values, wherein a voltage and a phase angle are respectively ascertained for each of the network sections, said state estimation device is configured to use a machine learning method, being a naive Bayes method, for formulating the prediction, and specific predictions for individual ones of the measuring devices that each have a measurement-location-specific degree of error are taken into consideration for the prediction, and a respective degree of error is used to determine a measurement-location-specific weighting factor such that the specific predictions are weighted all more highly a lower their degree of error; and a central controller configured for presenting the predictions, and initiating countermeasures to the grid based on the predictions when necessary. ### Claim 8 (depends on 7) The central computer configuration according to claim 7, wherein at least part of the electrical power distribution grid has a low-voltage grid. ### Claim 9 (depends on 7) The central computer configuration according to claim 7, wherein said state estimation device is configured to take into consideration for the prediction specific predictions for individual ones of the measuring devices that each have a measurement-location-specific degree of error, wherein a respective degree of error is used to determine a measurement-location-specific weighting factor such that the specific predictions are weighted all more highly a lower their degree of error. ### Claim 10 (depends on 7) The central computer configuration according to claim 7, wherein at least some of the measuring devices are disposed in substations. ### Claim 11 (depends on 7) The central computer configuration according to claim 7, wherein at least some of the measuring devices are disposed at feed points of the network sections. ### Claim 12 (depends on 7) The central computer configuration according to claim 7, wherein said state estimation device is configured to use the prediction to estimate the grid state at least 5 hours in advance. ### Claim 13 (depends on 7) The central computer configuration according to claim 7, wherein said state estimation device is configured with a mean average percentage error of no more than 10%. ### Claim 14 A non-transitory computer readable medium having computer executable instructions for performing a method for estimating a grid state of an electrical power distribution grid having a plurality of network sections, where a grid state contains a statement about the voltage magnitude or a voltage and the voltage angle or phase angle on each network section, which comprises the steps of: measuring, with measuring devices, a voltage and a phase angle for each of the plurality of network sections of the electrical power distribution grid and generating corresponding measured values; receiving, via a central computer configuration, the measured values from the measuring devices; using a state estimation device to make a prediction of a future grid state based on the measured values, wherein a voltage and a phase angle are respectively ascertained for each of the network sections; and using a machine learning method, being a naive Bayes method, for generating the prediction; and taking into consideration, for the prediction, specific predictions for individual ones of the measuring devices that each have a measurement-location-specific degree of error; and determining a measurement-location-specific weighting factor, using a respective degree of error, such that the specific predictions are weighted all more highly a lower their degree of error; and presenting the predictions via a central controller, and initiating countermeasures to the grid based on the predictions when necessary.