Enhanced power substation digital twins

1 . A method for virtualizing power substation physical components including electrical assets and Intelligent Electronic Devices (IEDs) into a substation digital twin architecture, the method comprising:

generating, for a first IED of a power substation, in a first virtual machine, a first IED digital twin that models historical and operational characteristics of the first IED;

generating, based on forecasted weather and operational parameters of the power substation, adaptive or predictive settings for the first IED using the first IED digital twin;

generating, based on physical sensor data measured or monitored by the first IED for the power substation, virtual sensors;

generating, based on the virtual sensors, first virtual sensor data corresponding to first electrical assets represented by the first IED;

generating, in a second virtual machine, based on the first virtual sensor data, an asset digital twin model of the first electrical assets;

generating, based on the first virtual sensor data and the asset digital twin model, a cyber digital twin for the first electrical assets;

generating, based on the first virtual sensor data and the asset digital twin model, a physics-based digital twin for the first electrical assets;

generating a substation digital twin virtually representing the power substation, wherein the substation digital twin comprises a logical combination of cyber digital twins, comprising the cyber digital twin, and physics-based digital twins, comprising the physics-based digital twin, of electrical assets of IEDs, comprising the first IED, in the power substation;

generating a substation digital twin architecture using a logical combination of substation digital twins comprising the substation digital twin; and

managing a real-time power substation operation using the substation digital twin architecture and adaptive and predictive settings for the IEDs.

2 . The method of claim 1 , further comprising:

generating, for second electrical assets of the second IED of the power substation, in a second virtual machine, a second IED digital twin that models historical and operational characteristics of the second IED;

generating, based on the forecasted weather and the operational parameters of the power substation, second adaptive or predictive settings for the second IED using the second IED digital twin;

generating, based on physical sensor data measured or monitored by the second IED for the power substation, second virtual sensors; generating, in a third virtual machine, based on second virtual sensor data from the second virtual sensors, a second asset digital twin model of second electrical assets monitored, protected, and controlled by or represented by the second IED;

generating, based on the second virtual sensor data and the second asset digital twin model, a second cyber digital twin for the second electrical assets; and

generating, based on the second virtual sensor data and the second asset digital twin model, a second physics-based digital twin for the second electrical assets; and

wherein the substation digital twin further comprises the second cyber digital twin and the second physics-based digital twin.

3 . The method of claim 1 , wherein the adaptive or the predictive settings comprise at least one of thresholds, a maximum load, or a predicted performance or health degradation for at least one of the electrical assets and operational settings for protection, control and automation algorithms in the first IED.

4 . The method of claim 1 , further comprising:

mapping, using the first virtual sensor data, data for the first IED to at least one of a current or voltage transformer, a power transformer, a substation asset, or a switchgear.

5 . The method of claim 4 , wherein the generating of the cyber digital twin and the generating of the physics-based digital twin are based on the mapping, asset design, the operational characteristics, the historical characteristics, learning insights from the data, data-driven analytics, a finite element (FEA) analysis, a computer-aided design (CAD) model, and a failure mode and effects analysis (FMEA).

6 . The method of claim 1 , further comprising:

detecting sensor issues or calibration issues by identifying, using the substation digital twin, a correlation between metering data of the power substation and at least one of current transformer digital twin data or predicted current transformer digital twin data.

7 . The method of claim 1 , further comprising:

detecting trip circuit issues by identifying, using the substation digital twin, a correlation between control data of the power substation and switchgear digital twin data.

8 . A non-transitory computer-readable storage medium comprising instructions to cause processing circuitry of a system for virtualizing power substation physical components including electrical assets and Intelligent Electronic Devices (IEDs) into a substation digital twin architecture, upon execution of the instructions by the processing circuitry, to:

generate, for a first IED of a power substation, in a first virtual machine, a first IED digital twin that models historical and operational characteristics of the first IED;

generate, based on forecasted weather and operational parameters of the power substation, adaptive or predictive settings for the first IED using the first IED digital twin;

generate, based on physical sensor data measured or monitored by the first IED for the power substation, virtual sensors;

generate, based on the virtual sensors, first virtual sensor data corresponding to electrical assets represented by the first IED;

generating, in a second virtual machine, based on the first virtual sensor data, an asset digital twin model of the electrical assets;

generate, based on the first virtual sensor data and the asset digital twin model, a cyber digital twin for the electrical assets;

generate, based on the first virtual sensor data and the asset digital twin model, a physics-based digital twin for the electrical assets;

generate a substation digital twin virtually representing the power substation, wherein the substation digital twin comprises a logical combination of cyber digital twins, comprising the cyber digital twin, and physics-based digital twins, comprising the physics-based digital twin, of electrical assets of IEDs, comprising the first IED, in the power substation;

generate a substation digital twin architecture using a logical combination of substation digital twins comprising the substation digital twin; and

manage a real-time power substation operation using the substation digital twin architecture and adaptive and predictive settings for the IEDs.

9 . The non-transitory computer-readable storage medium of claim 8 , wherein execution of the instructions further causes the processing circuitry to:

generate, for second electrical assets of a second IED of the power substation, in the second virtual machine, a second IED digital twin that models historical and operational characteristics of the second IED;

generate, based on the forecasted weather and the operational parameters of the power substation, second adaptive or predictive settings for the second IED using the second IED digital twin;

generate, based on physical sensor data measured or monitored by the second IED for the power substation, second virtual sensors; generate, in a third virtual machine, based on second virtual sensor data from the second virtual sensors, a second asset digital twin model of second electrical assets monitored, protected, and controlled by or represented by the second IED;

generate, based on the second virtual sensor data and the second asset digital twin model, a second cyber digital twin for the second electrical assets; and

generate, based on the second virtual sensor data and the second asset digital twin model, a second physics-based digital twin for the second electrical assets; and wherein the substation digital twin further comprises the second cyber digital twin and the second physics-based digital twin.

10 . The non-transitory computer-readable storage medium of claim 8 , wherein the adaptive or the predictive settings comprise at least one of thresholds, a maximum load, or a predicted performance or health degradation for at least one of the electrical assets and operational settings for protection, control and automation algorithms in the first IED.

11 . The non-transitory computer-readable storage medium of claim 8 , wherein execution of the instructions further causes the processing circuitry to:

map, using the first virtual sensor data, data for the first IED to at least one of a current or voltage transformer, a power transformer, a substation asset, or a switchgear.

12 . The non-transitory computer-readable storage medium of claim 11 , wherein to generate the cyber digital twin and to generate the physics-based digital twin are based on the mapping, asset design, the operational characteristics, the historical characteristics, learning insights from the data, data-driven analytics, a finite element (FEA) analysis, a computer-aided design (CAD) model, and a failure mode and effects analysis (FMEA).

13 . The non-transitory computer-readable storage medium of claim 8 , wherein execution of the instructions further causes the processing circuitry to:

detect sensor issues or calibration issues by identifying, using the substation digital twin, a correlation between metering data of the power substation and at least one of current transformer digital twin data or predicted current transformer digital twin data.

14 . The non-transitory computer-readable storage medium of claim 8 , wherein execution of the instructions further causes the processing circuitry to:

detect trip circuit issues by identifying, using the substation digital twin, a correlation between control data of the power substation and switchgear digital twin data.

15 . A system for virtualizing power substation physical components including electrical assets and Intelligence Electronic Devices (IEDs) into a substation digital twin architecture, the system comprising:

a power substation; and

a memory coupled to processing circuitry, wherein the processing circuitry is configured to:

generate, for a first IED of a power substation, in a first virtual machine, a first IED digital twin that models historical and operational characteristics of the first IED;

generate, based on forecasted weather and operational parameters of the power substation, adaptive or predictive settings for the first IED using the first IED digital twin; generate, based on physical sensor data measured or monitored by the first IED for the power substation, virtual sensors;

generate, based on the virtual sensors, first virtual sensor data corresponding to first electrical assets represented by the first IED;

generate, in a second virtual machine, based on the first virtual sensor data, an asset digital twin model of the first electrical assets;

generate, based on the first virtual sensor data and the asset digital twin model, a cyber digital twin for the first electrical assets; generate, based on the first virtual sensor data and the asset digital twin model, a physics-based digital twin for the first electrical assets;

generate a substation digital twin virtually representing the power substation, wherein the substation digital twin comprises a logical combination of cyber digital twins, comprising the cyber digital twin, and physics-based digital twins, comprising the physics-based digital twin, of first electrical assets of IEDs, comprising the first IED, in the power substation;

generate a substation digital twin architecture using a logical combination of substation digital twins comprising the substation digital twin; and

manage a real-time power substation operation using the substation digital twin architecture and adaptive and predictive settings for the IEDs.

16 . The system of claim 15 , wherein the processing circuitry is further configured to:

generate, for second electrical assets of a second IED of the power substation, in the second virtual machine, a second IED digital twin that models historical and operational characteristics of the second IED;

generate, based on the forecasted weather and the operational parameters of the power substation, second adaptive or predictive settings for the second IED using the second IED digital twin;

generate, based on physical sensor data monitored by the second IED for the power substation, second virtual sensors;

generate, in a third virtual machine, based on second virtual sensor data from the second virtual sensors, a second asset digital twin model of second electrical assets monitored, protected, and controlled by or represented by the second IED;

generate, based on the second virtual sensor data and the second asset digital twin model, a second cyber digital twin for the second electrical assets; and

generate, based on the second virtual sensor data and the second asset digital twin model, a second physics-based digital twin for the second electrical assets; and wherein the substation digital twin further comprises the second cyber digital twin and the second physics-based digital twin.

17 . The system of claim 15 , wherein the adaptive or the predictive settings comprise at least one of thresholds, a maximum load, or a predicted performance or health degradation for at least one of the electrical assets and operational settings for protection, control and automation algorithms in the first IED.

18 . The system of claim 15 , wherein the processing circuitry is further configured to:

map, using the first virtual sensor data, data for the first IED to at least one of a current or voltage transformer, a power transformer, a substation asset, or a switchgear.

19 . The system of claim 15 , wherein the processing circuitry is further configured to:

detect sensor issues or calibration issues by identifying, using the substation digital twin, a correlation between metering data of the power substation and at least one of current transformer digital twin data or predicted current transformer digital twin data.

20 . The system of claim 15 , wherein the processing circuitry is further configured to:

detect trip circuit issues by identifying, using the substation digital twin, a correlation between control data of the power substation and switchgear digital twin data.