Methods and systems for benchmarking asset performance

1. A processor implemented method for benchmarking asset performance based on operating conditions ( 200 ), the method comprising the steps of:

obtaining, by one or more hardware processors, a plurality of condition-aware reference curves pertaining to a plurality of assets, wherein the step of obtaining the plurality of condition-aware reference curves comprises receiving the plurality of condition-aware reference curves encompassing an operating range of the asset in a factory setting or obtaining the plurality of condition-aware reference curves via on-site test results, and wherein if an operating condition is not seen in the past, then a current observation is considered as part of the on-site test results ( 202 );

estimating, by the one or more hardware processors, a maximum operating efficiency associated with each of the plurality of assets from the obtained plurality of condition-aware reference curves, wherein the maximum operating efficiency is the maximum operating efficiency under predefined operating conditions ( 204 );

monitoring in real time, by the one or more hardware processor, one or more operating parameters pertaining to each of the plurality of assets ( 206 );

computing, by the one or more hardware processors, an inter-asset metric for each of the plurality of assets using the monitored one or more operating parameters at each instance of time and a corresponding condition-aware reference curve, wherein the inter-asset metric is an efficiency metric based on the first law of thermodynamics ( 208 );

computing, by the one or more hardware processors, an intra-asset metric for each of the plurality of assets at each instance of time, using the inter-asset metric and the maximum operating efficiency of a corresponding asset from the plurality of assets, wherein the intra-asset metric is an efficiency metric based on the second law of thermodynamics ( 210 );

converting, by the one or more hardware processors, a time-series associated with the inter-asset metric and the intra-asset metric for each of the plurality of assets to point values using a statistical metric ( 212 ); and

analyzing, by the one or more hardware processors, performance of an asset from the plurality of assets using the point values, wherein the step of analyzing performance of the asset comprises generating a scatter plot of the inter-asset metric along x-axis and the intra-asset metric along y-axis and comparing assets from the plurality of assets in two-dimensions, wherein the inter-asset metric involves comparing the performance of the asset with other assets and the intra-asset metric involves comparing the performance of the asset with the maximum operating efficiency associated with the asset, wherein the intra-asset metric helps in identifying deviation of the asset's performance from the maximum operational efficiency achievable, wherein the plurality of assets in the two-dimensions enable identifying inefficient assets that are analyzed further for finding a root cause, wherein analyzing the performance of the asset over time reduces downtime of an energy system includes an energy transferring device including a chiller and an energy converting device including a wind turbine, and wherein the chiller includes one of an air-cooled chiller and a water-cooled chiller ( 214 ).

2. The processor implemented method of claim 1 , wherein the statistical metric is either mean or median of the inter-asset metric and the intra-asset metric calculated for every point in the time series.

3. The processor implemented method of claim 1 , wherein the assets in the plurality of assets are cross-domain assets.

4. The processor implemented method of claim 3 , wherein the inter-asset metric pertains to assets within a domain.

5. A system for benchmarking asset performance based on operating conditions, the system comprising:

one or more data storage devices operatively coupled to one or more hardware processors and configured to store instructions configured for execution by the one or more hardware processors to:

obtain a plurality of condition-aware reference curves pertaining to a plurality of assets, wherein the step of obtaining the plurality of condition-aware reference curves comprises receiving the plurality of condition-aware reference curves encompassing an operating range of the asset in a factory setting or obtaining the plurality of condition-aware reference curves via on-site test results, and wherein if an operating condition is not seen in the past, then a current observation is considered as part of the on-site test results;

estimate, a maximum operating efficiency associated with each of the plurality of assets from the obtained plurality of condition-aware reference curves, wherein the maximum operating efficiency is the maximum operating efficiency under predefined operating conditions;

monitor in real time one or more operating parameters pertaining to each of the plurality of assets;

compute an inter-asset metric for each of the plurality of assets using the monitored one or more operating parameters at each instance of time and a corresponding condition-aware reference curve, wherein the inter-asset metric is an efficiency metric based on the first law of thermodynamics;

compute an intra-asset metric for each of the plurality of assets at each instance of time, using the inter-asset metric and the maximum operating efficiency of a corresponding asset from the plurality of assets, wherein the intra-asset metric is an efficiency metric based on the second law of thermodynamics;

convert a time-series associated with the inter-asset metric and the intra-asset metric for each of the plurality of assets to point values using a statistical metric; and

analyze performance of an asset from the plurality of assets using the point values, wherein analyzing the performance of the asset by generating a scatter plot of the inter-asset metric along x-axis and the intra-asset metric along y-axis and comparing assets from the plurality of assets in two-dimensions, wherein the inter-asset metric involves comparing the performance of the asset with other assets and the intra-asset metric involves comparing the performance of the asset with the maximum operating efficiency associated with the asset, wherein the intra-asset metric helps in identifying deviation of the asset's performance from the maximum operational efficiency achievable, wherein the plurality of assets in the two-dimensions enable identifying inefficient assets that are analyzed further for finding a root cause, wherein analyzing the performance of the asset over time reduces downtime of an energy system includes an energy transferring device including a chiller and an energy converting device including a wind turbine, and wherein the chiller includes one of an air-cooled chiller and a water-cooled chiller.

6. The system of claim 5 , wherein the statistical metric is either mean or median of the inter-asset metric and the intra-asset metric calculated for every point in the time series.

7. The system of claim 5 , wherein the assets in the plurality of assets are cross-domain assets.

8. The system of claim 7 , wherein the inter-asset metric pertains to assets within a domain.

9. A computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:

obtain a plurality of condition-aware reference curves pertaining to a plurality of assets, wherein the step of obtaining the plurality of condition-aware reference curves comprises receiving the plurality of condition-aware reference curves encompassing an operating range of the asset in a factory setting or obtaining the plurality of condition-aware reference curves via on-site test results, and wherein if an operating condition is not seen in the past, then a current observation is considered as part of the on-site test results;

estimate, a maximum operating efficiency associated with each of the plurality of assets from the obtained plurality of condition-aware reference curves, wherein the maximum operating efficiency is the maximum operating efficiency under predefined operating conditions;

monitor in real time one or more operating parameters pertaining to each of the plurality of assets;

compute an inter-asset metric for each of the plurality of assets using the monitored one or more operating parameters at each instance of time and a corresponding condition-aware reference curve, wherein the inter-asset metric is an efficiency metric based on the first law of thermodynamics;

compute an intra-asset metric for each of the plurality of assets at each instance of time, using the inter-asset metric and the maximum operating efficiency of a corresponding asset from the plurality of assets, wherein the intra-asset metric is an efficiency metric based on the second law of thermodynamics;

convert a time-series associated with the inter-asset metric and the intra-asset metric for each of the plurality of assets to point values using a statistical metric; and

analyze performance of an asset from the plurality of assets using the point values, wherein the step of analyzing performance of the asset comprises generating a scatter plot of the inter-asset metric along x-axis and the intra-asset metric along y-axis and comparing assets from the plurality of assets in two-dimensions, wherein the inter-asset metric involves comparing the performance of the asset with other assets and the intra-asset metric involves comparing the performance of the asset with the maximum operating efficiency associated with the asset, wherein the intra-asset metric helps in identifying deviation of the asset's performance from the maximum operational efficiency achievable, wherein the plurality of assets in the two-dimensions enable identifying inefficient assets that are analyzed further for finding a root cause, wherein analyzing the performance of the asset over time reduces downtime of an energy system includes an energy transferring device including a chiller and an energy converting device including a wind turbine, and wherein the chiller includes one of an air-cooled chiller and a water-cooled chiller.

10. The processor implemented method of claim 1 , wherein the inter-asset metric COP I (Coefficient Of Performance) with reference to the chiller is computed as:

COP

I

=

Cooling

⁢

load

W

.

comp

where {dot over (W)} comp is an electrical power consumed by a compressor and the cooling load is calculated as,

Cooling load= {dot over (m)} w C pw ΔT

where {dot over (m)} w is a mass flow rate of chilled water through an evaporator, C pw is a specific heat capacity of water and ΔT is a temperature difference of the chilled water across the evaporator.

11. The processor implemented method of claim 1 , wherein the intra-asset metric with reference to the chiller is computed as:

COP

II

=

COP

I

COP

max

Wherein the COP I is the inter-asset metric and the COP max is the maximum operating efficiency.

12. The system of claim 5 , wherein the inter-asset metric COP I (Coefficient Of Performance) with reference to the chiller is computed as:

COP

I

=

Cooling

⁢

load

W

.

comp

where {dot over (W)} comp is an electrical power consumed by a compressor and the cooling load is calculated as,

Cooling load= {dot over (m)} w C pw ΔT

where {dot over (m)} w is a mass flow rate of chilled water through an evaporator, C pw is a specific heat capacity of water and ΔT is a temperature difference of the chilled water across the evaporator.

13. The system of claim 5 , wherein the intra-asset metric with reference to the chiller is computed as:

COP

II

=

COP

I

COP

max

Wherein the COP I is the inter-asset metric and the COP max is the maximum operating efficiency.