DETAILED ACTIONS
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 11/13/2025.
Status of the Claims
Claims 1-24 are pending.
Claims 1, 7, 13, and 19 are amended.
Response to Arguments
Rejections Under 35 U.S.C. 102
Applicant's arguments, see remarks page 7-13, filed 02/25/2025.
with respect to the rejection(s) of Claims under 35 U.S.C. 103 has been considered, and are moot because the amendment has necessitated a new ground of rejections. The new rejections are set forth below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-24 are rejected under 35 U.S.C. 103 as being unpatentable over Jeffrey D. Taft. (US 2015/0002186 A1, hereinafter Taft, previously cited) and in view of Yan et al. (US 2021/0088563 A1, hereinafter Yan)
Regarding Claim 1, Taft teaches,
A power line event determination process implemented in a power grid (Taft, Figure 1,8 [0003], “The present invention relates generally to a system and method for managing a power grid, and more particularly to a system and method for managing outage and fault conditions in a power grid”) comprising:
implementing a plurality of implementations of a power grid event monitor; implementing at least one implementation of a power grid event analytics system (Taft, Figure 2, Grid data/ analytics service 123, Table 1, Grid data/ analytics service 123, Services (such as Sensor Data Services 124 and Analytics Management Services 125) to support access to grid data and grid analytics; management of analytics., and , Figure 5B, Event Analysis, 518, Table 3, Event analysis and triggers 518, Processing of all analytics for event detection); and
connecting the plurality of implementations of the power grid event monitor to the power grid (Taft, Figure 2, Centralized Grid Analytics Applications 139),
wherein the power grid event monitor is a voltage monitor, a current monitor, and/or a
voltage and current monitor (Taft, [0074], “local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing,
including event stream processing”. Figure 5A-5B, Ring buffer 502, Table 3, Local circular buffer storage for digital waveforms sampled from analog transducers (voltage and current waveforms for example) which may be used hold the data for waveform at different time periods so that if ru1 event is detected, the waveform data leading up to the event may also be stored”) ;
wherein the plurality of implementations of the power grid event monitor are located in
and electrically connected to certain respective implementations of a power grid component of the power grid (Taft, Figure; and Figure 2, connectivity warehouse 131, [0058] Network location data may include the information about the grid component on the communication network. This information may be used to send messages and information to the particular grid component. Network location data may be either entered manually into the Smart Grid database as new Smart Grid components are installed or is extracted from an Asset Management System if this information is maintained externally”); and
wherein the power grid component comprises one of the following: power stations, electrical substations, electric power transmission components, powerlines, electric power distribution components, electricity generation components, generators, high-voltage substations, local substations, high voltage transmission lines, step-up substations, step-down substations, distribution substations, transformers, circuit breakers, switches, lightning arresters, capacitors, electric power distribution components, distribution lines, distribution transformers, and/or feeders. .(Taft, Table 1, The data in the connectivity warehouse 131 may describe the hierarchical information about all the components of the grid (substation, feeder, section, segment, branch, t-section, circuit breaker, recloser, switch, etc -basically all the assets” NOTE: Power grid assets are included but not limited to electricity generation, electric power transmission, and electricity distribution. Power plants, transmission lines etc. see [0005]).
Taft teaches a PMU receiving power grid data with time stamp and analyzing data to determine a grid event location,
Taft is silent on determine an estimate of a fault location within the power grid based on times of arrival of a fault signature at each of a first one of the plurality of implementations of the power grid event monitor and a second one of the plurality of implementations of the power grid event monitor
However, Yan teaches determine an estimate of a fault location within the power grid based on times of arrival of a fault signature at each of a first one of the plurality of implementations of the power grid event monitor and a second one of the plurality of implementations of the power grid event monitor (Yan, Figure 2-4, [0023] “time-series data may be received from a collection of monitoring nodes (e.g., sensor, actuator, and/or controller nodes). Figure 9, [0094] PMU 906 may use voltage and current sensors (e.g., voltage sensors 902, current sensors 904) that may measure voltages and currents at principal intersecting locations (e.g., substations) on a power grid using a common time source for synchronization and may output accurately time-stamped voltage and current phasors. [0099], “the data may be location-tagged. For example, it may comprise a station identification of a particular station in which a power delivery device being measured is located (e.g., "CANADAS"). The data may comprise a particular node number designated for a location. The data may comprise the identity of the measure equipment (e.g., the identification number of a circuit breaker associated with an equipment). The data may also be time tagged, indicating the time at which the data was measured by a measurement device. The PMU/SCADA-based equipment data may also contain, for example, information regarding a particular measurement device (e.g., a PMU ID identifying the PMU from which measurements were taken.”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft analyzing method in view of Yan to include time series measurement data with tagged location to determine power grid event at different locations as taught by Yan with the benefit of accurate event detection and identification performance. (Yan, [0019]).
Regarding Claim 2, combination of Taft and Yan teaches the power line event determination process according to claim 1,
Taft further teaches wherein a first implementation of the power grid event monitor is located in and/or connected to a first implementation of the power grid component of the power grid (Taft, Figure 4, 0071] FIG. 4 illustrates an example of the high-level architecture for the INDE SUBSTATION 180 group. This group may comprise elements that are actually hosted in the substation 170 at a substation control house on one or more servers co-located with the substation electronics and systems); and wherein a second implementation of the power grid event monitor is located in and/or connected to a second implementation of the power grid component of the power grid (Taft, Figure 15A-15B, Data collected from different assets, each asset contains analytic).
Regarding Claim 3, combination of Taft and Yan teaches the power line event determination process according to claim 1,
Taft further teaches wherein the power grid event monitor is configured to detect a fault-criteria (Taft, Table 5, Fault Identification Criteria) and generate fault data that includes a time of fault and fault criteria (Taft, Figure 8, Fault Intelligence, characterize faults, determine fault location and log fault data); and wherein the power grid event monitor is configured to send the fault data to the power grid event analytics system. (Taft, Figure [0056], Grid components like grid devices (smart power sensors (such as a sensor with an embedded processor that can be programmed for digital processing capability) temperature sensors, etc.), power system components that includes additional embedded processing (RTU s, etc), smart meter networks (meter health, meter readings, etc), and mobile field force devices (outage events, work order completions, etc) may generate event data, operational and non-operational data. The event data generated within the smart grid may be transmitted via an event bus 147 (Figure 1B)).
Regarding Claim 4, combination of Taft and Yan teaches the power line event determination process according to claim 3,
Taft further teaches wherein the power grid event monitor uses a GPS (Global Positioning System) or other timing methodology to generate the time of fault (Taft, Figure 5A, GPS timing 524, GPS Data Frame Time Stamp 526, [0067], Geographic Information System 149 (Figure 6C) is a database that contains information about where assets are located geographically and how the assets are connected together); and
wherein the power grid event monitor is configured to sample instantaneous voltage waveforms and/or current waveforms at a location of a respective implementation of the power grid component within the power grid that a particular implementation of the power grid event monitor is located to generate the fault criteria (Taft, [0074], local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing, including event stream processing”);.
Regarding Claim 5, combination of Taft and Yan teaches the power line event determination process according to claim 1,
Taft further teaches wherein the power grid event monitor is configured to sample instantaneous voltage waveforms and/or current waveforms with sub-millisecond timing accuracy. (Taft, [0074], local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing, including event stream processing”. NOTE: “a sub-second” refers to milli or micro seconds, less than a second).
Regarding Claim 6, combination of Taft and Yan teaches the power line event determination process according to claim 3,
Taft further teaches wherein the fault criteria (Taft, Table 5, Fault Identification Criteria) includes specific signatures, traditional waveshape triggering, such as RMS (root mean square) or peak value change or trigger (Taft, Figure 5A,Vrms, Irms, Table 3, Frequency domain signal analysis 512, processing of the signals in the frequency domain; extraction of RMS and power parameters, waveshape change based on THD (total harmonic distortion) and/or mean square difference from previous cycle, level trigger after high pass filtering, power line frequency comb filtering, or 60Hz comb filtering (Taft, Figure 5A, Waveform streaming service 522, Ring buffer(60 cycles)).
Taft is silent on wherein the power grid event monitor is configured to sample continuously, with triggering based on prior machine learning training information to match the fault criteria;
However, Yan teaches wherein the power grid event monitor is configured to sample continuously, with triggering based on prior machine learning training information to match the fault criteria;(Yan, figure 2-3, 0051] FIG. 2, “the process may be trained in an off line phase or mode and may subsequently be implemented in an online monitoring and diagnosis phase or application. [0025] Signature identification using data-driven machine learning techniques may be treated as a feature selection problem. [0030] In accordance with one or more embodiments, a machine learning-based power substation asset monitoring
system is provided which may determine various signatures corresponding to different power system events. For example, such a machine learning-based power substation asset monitoring system may receive and process data from
various sources, such a system may include components to perform
operations such as feature generation or extraction, auto-associative model building, residual generation, residual generation, and signature identification, Features may comprise individual quantities extracted from one or more measured data streams”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft analyzing method in view of Yan to include machine learning data to include a machine learning method to process the power grid state data as taught by Yan and obtain a more stable and more accurate classification of a fault data with the benefit of better event detection and identification performance. (Yan, [0030-[0037]).
Regarding Claim 7, combination of Taft and Yan teaches the power line event determination process according to claim 1,
and
wherein the power grid event analytics system is configured to determine the estimate of the fault location within the power grid based (Taft, [0125] The fault intelligent processes may be responsible for interpreting the grid data to derive information about current and potential faults within the grid. Specifically, faults
may be detected using the fault intelligent processes [0127] The fault intelligence may also determine fault location. See table 5, left, first col. and see Figure 18A, , Substation ID, location)
a first distance (Taft, Figure 18A, Asset ID, elect distances”). from the first one of the plurality of implementations of the power grid event monitor (Taft, Figure 18A various sources of distributed energy generation/storage 162 (such as solar panels, etc .) may send data to a monitor control 161 for communication with the operations control center 116 via the utility management network 160 Figure 3B) wherein the first distance is determined based on a speed of light and the first time of arrival, and (Taft, Figure 5A. 524 GPS timing, and 526, GPS data time stamp” NOTE: The “time stamp” of the signal reads the time of arrival, and , electromagnetic wave ravels at light speed Measuring distance using light travel speed time) a second distance from the second one of the plurality of implementations of the power grid event monitor, wherein the second distance is determined based on the speed of light and the second time of arrival (Taft, Figure 1C, “event/file records. The network management sytem 112, and distribution management system 157, 184, detection classification processing”. NOTE: there are multiple places to detect events )
Taft teaches a PMU receiving power grid data with time stamp and analyzing data to determine a grid event location,
Taft is silent on wherein the times of arrival include 1') a first time of arrival of the fault signature at the first one of the plurality of implementations of the power grid event monitor, and 2) a second time of arrival of the fault signature at the second one of the plurality of implementations of the power grid event monitor,
However, Yan teaches wherein the times of arrival include 1') a first time of arrival of the fault signature at the first one of the plurality of implementations of the power grid event monitor, and 2) a second time of arrival of the fault signature at the second one of the plurality of implementations of the power grid event monitor, (Yan, Figure 2-4, [0023] “time-series data may be received from a collection of monitoring nodes (e.g., sensor, actuator, and/or controller nodes). Figure 9, [0094] PMU 906 may use voltage and current sensors (e.g., voltage sensors 902, current sensors 904) that may measure voltages and currents at principal intersecting locations (e.g., substations) on a power grid using a common time source for synchronization and may output accurately time-stamped voltage and current phasors. [0099], “the data may be location-tagged. For example, it may comprise a station identification of a particular station in which a power delivery device being measured is located (e.g., "CANADAS"). The data may comprise a particular node number designated for a location. The data may comprise the identity of the measure equipment (e.g., the identification number of a circuit breaker associated with an equipment). The data may also be time tagged, indicating the time at which the data was measured by a measurement device. The PMU/SCADA-based equipment data may also contain, for example, information regarding a particular measurement device (e.g., a PMU ID identifying the PMU from which measurements were taken.”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft analyzing method in view of Yan to include time series measurement data with tagged location to determine power grid event at different locations as taught by Yan with the benefit of accurate event detection and identification performance. (Yan, [0019]).
Regarding Claim 8, combination of Taft and Yan teaches the power line event determination process according to claim 1,
Taft further teaches wherein an implementation of the power grid event monitor is placed at an electrical substation at a head of each implementation of a feeder line, and an implementation of the power grid event monitor is placed at an end of the feeder line (Taft, Figure 21, [0169] FIG. 21-24 is an operational flow diagram of the outage intelligence application configured to determine outage conditions associated w;H1 Ene sensors ;n the power grid. ln one example, the line sensors may also include the feeder meters that are electrically coupled to the line sensors to provide information concerning line sensor activity”).
Regarding Claim 9, combination of Taft and Yan teaches the power line event determination process according to claim 1,
Taft further teaches wherein the power grid event monitor includes a voltage transducer, a current transducer, and an A/D (analog to digital) converter. (Taft, Figure 5A, 502, sensors, [0056], Grid components like grid devices (smart power sensors (such as a sensor with an embedded processor that can be programmed for digital processing capability) temperature sensors, etc.), power system components that includes additional embedded processing (RTU s, etc), smart meter networks (meter health, meter readings, etc), and mobile field force devices (outage events, work order completions, etc) may generate event data”)
Regarding Claim 10, combination of Taft and Yan teaches the power line event determination process according to claim 9,
Taft further teaches wherein the current transducer is configured to measure a current associated with the power grid component of the power grid (Taft, figure 5A,502, Ia, Ib, Ic, see Figure 8, Grid state measurement); and wherein the voltage transducer is configured to measure a voltage associated with the power grid component of the power grid. (Taft, see Figure 8, Grid state measurement ,figure 5A,502, Va, Vb, Vc, Table 3- storage for digital waveforms sampled from analog transducers (voltage and current waveforms for example) which may be used hold the data for wavefonns at different time periods so that if ru1 event is detected, the waveform data leading up to the event may also be stored)
Regarding Claim 11, combination of Taft and Yan teaches the power line event determination process according to claim 1,
Taft further teaches wherein the power grid event monitor is configured to collect sensor readings and provide the sensor readings to the power grid event analytics system. (Taft, Figure 9A , Line sensors RTUs, collect operational Data, [0056], The event data generated within the smart grid may be transmitted via an event bus 147 )
Regarding Claim 12, combination of Taft and Yan teaches the power line event determination process according to claim 3,
Taft further teaches wherein the power grid event analytics system is implemented with a processor (Taft, Figure 5, [0083], The smart grid device may include an embedded processor);
wherein the power grid event analytics system is configured to receive and parse the fault data from one or more implementations of the power grid event monitor (Taft, Figure 14A-B, receive event data 1420); and wherein the power grid event analytics system is configured process alerts from the power grid event monitor (Taft, Figure 14, [0123]. The various fault data, grid state, connectivity data, and switch state may be sent to the substation analytics for event detection and characterization,
as shown at block 1430. The event bus may also receive event messages (block 1434) and send the event messages to the substation analytics (block 1436). The substation analytics may determine the type of event, as shown at block 1432 “)
Regarding Claim 13, Taft teaches,
A power line event determination system implemented in a power grid comprising:
a plurality of implementations of a power grid event monitor (Taft, Figure 2, Grid data/ analytics service 123, Table 1, Grid data/ analytics service 123, Services (such as Sensor Data Services 124 and Analytics Management Services 125) to support access to grid data and grid analytics; management of analytics., and , Figure 5B, Event Analysis, 518, Table 3, Event analysis and triggers 518, Processing of all analytics for event detection);
; and the plurality of implementations of the power grid event monitor connected to the power grid, (Taft, Figure 2, Centralized Grid Analytics Applications 139),
wherein the power grid event monitor is a voltage monitor, a current monitor, and/or a voltage and current monitor (Taft, [0074], “local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing,
including event stream processing”. Figure 5A-5B, Ring buffer 502, Table 3, Local circular buffer storage for digital waveforms sampled from analog transducers (voltage and current waveforms for example) which may be used hold the data for waveform at different time periods so that if ru1 event is detected, the waveform data leading up to the event may also be stored”) ;
wherein the plurality of implementations of the power grid event monitor are located in and electrically connected to certain respective implementations of a power grid component of the power grid (Taft, Figure; and Figure 2, connectivity warehouse 131, [0058] Network location data may include the information about the grid component on the communication network. This information may be used to send messages and information to the particular grid component. Network location data may be either entered manually into the Smart Grid database as new Smart Grid components are installed or is extracted from an Asset Management System if this information is maintained externally”); ; and
wherein the power grid component comprises one of the following: power stations, electrical substations, electric power transmission components, powerlines, electric power distribution components, electricity generation components, generators, high-voltage substations, local substations, high voltage transmission lines, step-up substations, step-down substations, distribution substations, transformers, circuit breakers, switches, lightning arresters, capacitors, electric power distribution components, distribution lines, distribution transformers, and/or feeders. .(Taft, Table 1, The data in the connectivity warehouse 131 may describe the hierarchical information about all the components of the grid (substation, feeder, section, segment, branch, t-section, circuit breaker, recloser, switch, etc -basically all the assets” NOTE: Power grid assets are included but not limited to electricity generation, electric power transmission, and electricity distribution. Power plants, transmission lines etc. see [0005]).
Taft teaches a PMU receiving power grid data with time stamp and analyzing data to determine a grid event location,
Taft is silent on at least one implementation of a power grid event analytics system that is configured to determine an estimate of a fault location within the power grid based on times of arrival of a fault signature at each of a first one of the plurality of implementations of the power grid event monitor and a second one of the plurality of implementations of the power grid event monitor.
However, Yan teaches determine an estimate of a fault location within the power grid based on times of arrival of a fault signature at each of a first one of the plurality of implementations of the power grid event monitor and a second one of the plurality of implementations of the power grid event monitor (Yan, Figure 2-4, [0023] “time-series data may be received from a collection of monitoring nodes (e.g., sensor, actuator, and/or controller nodes). Figure 9, [0094] PMU 906 may use voltage and current sensors (e.g., voltage sensors 902, current sensors 904) that may measure voltages and currents at principal intersecting locations (e.g., substations) on a power grid using a common time source for synchronization and may output accurately time-stamped voltage and current phasors. [0099], “the data may be location-tagged. For example, it may comprise a station identification of a particular station in which a power delivery device being measured is located (e.g., "CANADAS"). The data may comprise a particular node number designated for a location. The data may comprise the identity of the measure equipment (e.g., the identification number of a circuit breaker associated with an equipment). The data may also be time tagged, indicating the time at which the data was measured by a measurement device. The PMU/SCADA-based equipment data may also contain, for example, information regarding a particular measurement device (e.g., a PMU ID identifying the PMU from which measurements were taken.”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft analyzing method in view of Yan to include time series measurement data with tagged location to determine power grid event at different locations as taught by Yan with the benefit of accurate event detection and identification performance. (Yan, [0019]).
Regarding Claim 14, combination of Taft and Yan teaches the power line event determination system according to claim 13,
Taft further teaches wherein a first implementation of the power grid event monitor is located in and/or connected to a first implementation of the power grid component of the power grid (Taft, Figure 4, 0071] FIG. 4 illustrates an example of the high-level architecture for the INDE SUBSTATION 180 group. This group may comprise elements that are actually hosted in the substation 170 at a substation control house on one or more servers co-located with the substation electronics and systems); and wherein a second implementation of the power grid event monitor is located in and/or connected to a second implementation of the power grid component of the power grid (Taft, Figure 15A-15B, Data, Data collected from different assets, each asset contains analytics).
Regarding Claim 15, combination of Taft and Yan teaches the power line event determination system according to claim 13,
Taft further teaches wherein the power grid event monitor is configured to detect a fault-criteria (Taft, Table 5, Fault Identification Criteria) and generate fault data that includes a time of fault and fault criteria (Taft, Figure 8, Fault Intelligence, characterize faults, determine fault location and log fault data); and wherein the power grid event monitor is configured to send the fault data to the power grid event analytics system. (Taft, Figure [0056], Grid components like grid devices (smart power sensors (such as a sensor with an embedded processor that can be programmed for digital processing capability) temperature sensors, etc.), power system components that includes additional embedded processing (RTU s, etc), smart meter networks (meter health, meter readings, etc), and mobile field force devices (outage events, work order completions, etc) may generate event data, operational and non-operational data. The event data generated within the smart grid may be transmitted via an event bus 147 (Figure 1B)).
Regarding Claim 16, combination of Taft and Yan teaches the power line event determination system according to claim 15,
Taft further teaches wherein the power grid event monitor uses a GPS (Global Positioning System) or other timing methodology to generate the time of fault (Taft, Figure 5A, GPS timing 524, GPS Data Frame Time Stamp 526, [0067], Geographic Information System 149 (Figure 6C) is a database that contains information about where assets are located geographically and how the assets are connected together); and
wherein the power grid event monitor is configured to sample instantaneous voltage waveforms and/or current waveforms at a location of a respective implementation of the power grid component within the power grid that a particular implementation of the power grid event monitor is located to generate the fault criteria (Taft, [0074], local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing, including event stream processing”).
Regarding Claim 17, combination of Taft and Yan teaches the power line event determination system according to claim 13,
Taft further teaches wherein the power grid event monitor is configured to sample instantaneous voltage waveforms and/or current waveforms with sub-millisecond timing accuracy. (Taft, [0074], local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing, including event stream processing”. NOTE: “a sub-second” refers to milli or micro seconds, less than a second.)
Regarding Claim 18, combination of Taft and Yan teaches the power line event determination system according to claim 15,
Taft further teaches wherein the fault criteria (Taft, Table 5, Fault Identification Criteria) includes specific signatures, traditional waveshape triggering, such as RMS (root mean square) or peak value change or trigger (Taft, Figure 5A,Vrms, Irms, Table 3, Frequency domain signal analysis 512, processing of the signals in the frequency domain; extraction of RMS and power parameters, waveshape change based on THD (total harmonic distortion) and/or mean square difference from previous cycle, level trigger after high pass filtering, power line frequency comb filtering, or 60Hz comb filtering (Taft, Figure 5A, Waveform streaming service 522, Ring buffer(60 cycles)).
Taft is silent on wherein the power grid event monitor is configured to sample continuously, with triggering based on prior machine learning training information to match the fault criteria;
However, Yan teaches wherein the power grid event monitor is configured to sample continuously, with triggering based on prior machine learning training information to match the fault criteria;(Yan, figure 2-3, 0051] FIG. 2, “the process may be trained in an off line phase or mode and may subsequently be implemented in an online monitoring and diagnosis phase or application. [0025] Signature identification using data-driven machine learning techniques may be treated as a feature selection problem. [0030] In accordance with one or more embodiments, a machine learning-based power substation asset monitoring
system is provided which may determine various signatures corresponding to different power system events. For example, such a machine learning-based power substation asset monitoring system may receive and process data from
various sources, such a system may include components to perform
operations such as feature generation or extraction, auto-associative model building, residual generation, residual generation, and signature identification, Features may comprise individual quantities extracted from one or more measured data streams”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft analyzing method in view of Yan to include machine learning data to include a machine learning method to process the power grid state data as taught by Yan and obtain a more stable and more accurate classification of a fault data with the benefit of better event detection and identification performance. (Yan, [0030-[0037]).
Regarding Claim 19, combination of Taft and Yan teaches the power line event determination system according to claim 13,
Taft further teaches the power grid event analytics system is configured to utilize relative times of arrival a fault signature to estimate a fault location within the power grid. (Taft, [0205], one example, fault types may be determined by the fault intelligence application based on the fault identification criteria categories of Table 5. Synchrophasor data for each phase within the power grid may be obtained from a phasor measurement unit (PMU) data collection head
located in the INDE SUBSTATION 180 group or may be located centrally in a central authority to the power grid. The PMU measures and may provide phase information including the synchrophasor data, such as phasor magnitude and phasor angle data, for each phase, A, B, and C, may be generated and
analyzed to determine if a fault is present and determine the type of fault. Phase information may be received by the fault intelligence application at block 2600.A detem1ination that a possible fault may be present may be performed at block
2602. In one example, the fault intelligence application may make the determination at block 2602 based on thresholds associated with phasor magnitude and phasor angle data for each phase being analyzed”).
Regarding Claim 20, combination of Taft and Yan teaches the power line event determination system according to claim 13,
Taft further teaches wherein an implementation of the power grid event monitor is placed at an electrical substation at a head of each implementation of a feeder line, and an implementation of the power grid event monitor is placed at an end of the feeder line (Taft, Figure 21, [0169] FIG. 21-24 is an operational flow diagram of the outage intelligence application configured to determine outage conditions associated with the sensors in the power grid. ln one example, the line sensors may also include the feeder meters that are electrically coupled to the line sensors to provide information concerning line sensor activity”).
Regarding Claim 21, combination of Taft and Yan teaches the power line event determination system according to claim 13,
Taft further teaches wherein the power grid event monitor includes a voltage transducer, a current transducer, and an A/D (analog to digital) converter. (Taft, Figure 5A, 502, sensors, [0056], Grid components like grid devices (smart power sensors (such as a sensor with an embedded processor that can be programmed for digital processing capability) temperature sensors, etc.), power system components that includes additional embedded processing (RTU s, etc), smart meter networks (meter health, meter readings, etc), and mobile field force devices (outage events, work order completions, etc) may generate event data”)
Regarding Claim 22, combination of Taft and Yan teaches the power line event determination system according to claim 21,
Taft further teaches wherein the current transducer is configured to measure a current associated with the power grid component of the power grid (Taft, figure 5A,502, Ia, Ib, Ic, see Figure 8, Grid state measurement); and wherein the voltage transducer is configured to measure a voltage associated with the power grid component of the power grid. (Taft, see Figure 8, Grid state measurement, figure 5A,502, Va, Vb, Vc, Table 3- storage for digital waveforms sampled from analog transducers (voltage and current waveforms for example) which may be used hold the data for wavefonns at different time periods so that if ru1 event is detected, the waveform data leading up to the event may also be stored)
Regarding Claim 23, combination of Taft and Yan teaches the power line event determination system according to claim 13,
Taft further teaches wherein the power grid event monitor is configured to collect sensor readings and provide the sensor readings to the power grid event analytics system. (Taft, Figure 9A, Line sensors RTUs, collect operational Data, [0056], The event data generated within the smart grid may be transmitted via an event bus 147) .
Regarding Claim 24, combination of Taft and Yan teaches the power line event determination system according to claim 15
Taft further teaches wherein the power grid event analytics system is implemented with a processor (Taft, Figure 5, [0083], The smart grid device may include an embedded processor);
wherein the power grid event analytics system is configured to receive and parse the fault data from one or more implementations of the power grid event monitor (Taft, Figure 14A-B, receive event data 1420); and wherein the power grid event analytics system is configured process alerts from the power grid event monitor (Taft, Figure 14, [0123]. The various fault data, grid state, connectivity data, and switch state may be sent to the substation analytics for event detection and characterization,
as shown at block 1430. The event bus may also receive event messages (block 1434) and send the event messages to the substation analytics (block 1436). The substation analytics may determine the type of event, as shown at block 1432”).
Conclusions
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sen et al. (US 9620959 B2) recites “A power grid stabilizing system may include a processor and a network interface executable by the processor to monitor for new event data from power consumption devices over a network. The new event data may include information such as device location, operating information, and sensor data. The system may include an estimation engine operable to analyze the new event data to determine power consumption behavior of a consumption device, and a predictor operable to anticipate an occurrence of a future event responsive to the analysis. The predictor may also predict the outcome of the future event based on analysis of the new event data in relation to past behavior data of the consumption device. The network interface may further communicate the anticipated future event and the predicted outcome to one or more of the other consumption devices” (abstract)
Menzel et al. (US 2021/0103006 A1) recites “A method of analyzing events for an electrical system includes: receiving event stream(s) of events occurring in the electrical system, the events being identified from captured energy-related signals in the system; analyzing, an event stream(s) of the events to identify different actionable triggers therefrom, the different triggers including a scenario in which a group of events satisfies one or more predetermined triggering conditions; analyzing, over time, the different actionable triggers to identify a combination of occurring and/or non-occurring actionable triggers which satisfies a predefined trigger combination condition and an analysis time constraint; and in response to the observation of the combination, taking one or more actions to address the events. The analysis time constraint can be a time period duration and/or sequence within which time-stamped data of events in the event stream(s) and the associated actionable triggers are considered or not considered in the analysis to identify the combination” (abstract).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DILARA SULTANA/Examiner, Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 3/3/2026