1. A method of managing power loss of a wind turbine (102), the method comprising: obtaining, by a data processing system (106), a first set of operational data of a wind 10 turbine (102) and a second set of operational data of the wind turbine (102) from one or more data sources (104); determining, by the data processing system (106), values corresponding to physicsbased power loss parameters related to the wind turbine (102) based on the first set of 15 operational data; predicting, by the data processing system (106), a rotor speed of the wind turbine (102) using the second set of operational data of the wind turbine (102) and the determined values corresponding to the physics-based power loss parameters for an input pitch angle 20 of blades of the wind turbine (102); predicting, by the data processing system (106), a power loss value indicating a deviation of a power output of the wind turbine (102) corresponding to the input pitch angle from an ideal power output, based on the predicted rotor speed, the second set of 25 operational data of the wind turbine (102) and the determined values corresponding to the physics-based power loss parameters; and determining, by the data processing system (106), an adjusted pitch angle of the blades based on the predicted power loss value, wherein the adjusted pitch angle is adapted 30 for managing power loss of the wind turbine (102).

2. The method as claimed in claim 1, wherein determining the adjusted pitch angle of the blades comprises: performing one of: when the predicted power loss value is less than a pre-defined 35 threshold value, inferring the input pitch angle as the adjusted pitch angle; or when the predicted power loss value is greater than the pre-defined threshold value, iteratively varying the input pitch angle to predict a new rotor speed and a new power loss value corresponding to the varied input 10 pitch angle in each iteration, until the new power loss value is less than the pre-defined threshold value; and inferring the varied input pitch angle as the adjusted pitch angle. 15 3. The method as claimed in claim 2, wherein the varied input pitch angle is determined by one of incrementing or decrementing the input pitch angle based on the new power loss value predicted in each iteration.

4. The method as claimed in claim 1, wherein the first set of operational data comprises wind 20 related data and wind turbine (102) related data that impact power generated by the wind turbine (102).

5. The method as claimed in claim 1, wherein the second set of operational data is one of a subset of the first set of operational data or same as the first set of operational data. 25

6. The method as claimed in claim 1, wherein the physics-based power loss parameters comprises at least one of Coefficient of power (Cp), wind power, power loss through the blades of the wind turbine (102), power loss through gearbox, and power loss through generator. 30

7. The method as claimed in claim 1, wherein the rotor speed is predicted using a first trained AI model.

8. The method as claimed in claim 1, wherein the power loss value is predicted using a second 35 trained AI model

9. The method as claimed in claim 1 further comprises transmitting, by the data processing system (106), the adjusted pitch angle to a pitch control system (108) associated with the wind turbine (102) to change the pitch angle of the blades to the adjusted pitch angle.

10. A data processing system (106) for managing power loss of a wind turbine (102), the 10 system comprising: a processor (202); and a memory (206) communicatively coupled to the processor (202), wherein the memory (206) stores the processor-executable instructions, which, on execution, causes the processor (202) to: 15 obtain a first set of operational data of a wind turbine (102) and a second set of operational data of the wind turbine (102) from one or more data sources (104); determine values corresponding to physics-based power loss parameters related to the wind turbine (102) based on the first set of operational data; 20 predict a rotor speed of the wind turbine (102) using the second set of operational data of the wind turbine (102) and the determined values corresponding to the physics-based power loss parameters for an input pitch angle of blades of the wind turbine (102); 25 predict a power loss value indicating a deviation of a power output of the wind turbine (102) corresponding to the input pitch angle from an ideal power output, based on the predicted rotor speed, the second set of operational data of the wind turbine (102) and the values corresponding to the physics-based power loss parameters; and 30 determine an adjusted pitch angle of the blades based on the predicted power loss value adapted for managing power loss of the wind turbine (102).

11. The data processing system (106) as claimed in claim 10, wherein to determine the adjusted 35 pitch angle of the blades, the processor (202) is configured to: perform one of: when the predicted power loss value is less than a pre-defined threshold value, infer the input pitch angle as the adjusted pitch angle; or when the predicted power loss value is greater than the pre-defined 10 threshold value, iteratively vary the input pitch angle to predict a new rotor speed and a new power loss value corresponding to the varied input pitch angle in each iteration, until the new power loss value is less than the pre-defined threshold value; and 15 infer the varied input pitch angle as the adjusted pitch angle.

12. The data processing system (106) as claimed in claim 11, wherein the processor (202) determines the varied input pitch angle by one of incrementing or decrementing the input pitch angle based on the new power loss value predicted in each iteration. 20

13. The data processing system (106) as claimed in claim 10, wherein the first set of operational data comprises wind related parameters and turbine related parameters of the wind turbine (102). 25 14. The data processing system (106) as claimed in claim 10, wherein the second set of operational data is one of a subset of the first set of operational data or same as the first set of operational data.

15. The data processing system (106) as claimed in claim 10, wherein the physics-based power 30 loss parameters comprises at least one of Coefficient of power (Cp), wind power, power loss through the blades of the wind turbine (102), power loss through gearbox, and power loss through generator.

16. The data processing system (106) as claimed in claim 10, wherein the processor (202) 35 predicts the rotor speed using a first trained AI model. 35 5 17. The data processing system (106) as claimed in claim 10, wherein the processor (202) predicts the power loss value using a second trained AI model.

18. The data processing system (106) as claimed in claim 10, wherein the processor (202) is further configured to transmit the adjusted pitch angle to a pitch control system (108) 10 associated with the wind turbine (102) to change the pitch angle of the blades to the adjusted pitch angle.

19. A wind turbine (102) comprising a data processing system (106) as claimed in the claims 10-18 for managing power loss of a wind turbine (102).

TECHNICAL FIELD
[0001] The present subject matter is related, in general, to wind turbines, and particularly
to method, apparatus and system for managing power loss of wind turbines.
BACKGROUND
10
[0002] Of-late costs of energy and finite supplies of energy-related resources are increasing
day by day. Thus, the wide-scale implementation of renewable energy resources, such as wind
and solar power, is the primary focus of much research in the field. Environmental concerns
related to the use of traditional energy sources, such as coal, oil natural gas and nuclear power,
15 even further bolster the need for more widespread use of environmentally friendly wind and solar
power. One hurdle yet to be overcome in wind energy implementation is to achieving the ideal
power curve of a wind turbine, as provided by a manufacturer. The ideal power curve of the wind
turbine, as provided by the manufacturer, represents the optimal relationship between wind speed
and power output. It illustrates the turbine's ability to efficiently harness wind energy across
20 various wind speeds, showcasing a steady increase in power production as wind velocity rises,
eventually reaching a peak power output before leveling off.
[0003] The horizontal-axis wind turbines used in a conventional wind turbine have a
plurality of blades. Generally, the pitch angles of the blades need to be changed according to the
25 wind speed so as to change the rotational speed of the rotor and thereby to control the energy
efficiency of the blades. If the pitch angle is not optimized according to the wind speed, the
turbine may not efficiently convert wind energy into rotational motion leading to lower power
output. Suboptimal pitch angles can lead to inefficient energy capture, resulting in decreased
power output. Conversely, an overly aggressive pitch angle can cause excessive drag, impeding
30 the turbine's rotation and diminishing its performance. To address these challenges and maximize
power output of the wind turbine, pitch angle optimization is crucial.
[0004] To address the pitch angle optimization issues, the wind turbine often includes a
pitch control system, that facilitates a mechanism for changing the pitch angles of the blades. It
35 has been observed that pitch controller systems do not always operate optimally to produce maximum power output under normal operating conditions. Various solutions have been
proposed earlier for controlling optimal pitch angle based on data driven wind turbine modelling
. These data driven wind turbine modeling are developed using Gated Recurrent Neural Networks
(GRNN) are used to develop the data driven model of wind turbine and power loss is calculated
based on rated power. However, these solutions are entirely based on data driven models and
10 ignore the system physics.
[0005] Therefore, there exists a need to provide a method, system, and apparatus for
control of optimal pitch angle to enables the turbine to operate at its peak efficiency, contributing
to the advancement of wind energy technology and its broader adoption in the renewable energy
15 landscape.
[0006] The information disclosed in this background of the disclosure section is only for
enhancement of understanding of the general background of the present disclosure and should
not be taken as an acknowledgement or any form of suggestion that this information forms the
20 prior art already known to a person skilled in the art.
SUMMARY
[0007] Disclosed herein is a method of managing power loss of a wind turbine. The method
comprises obtaining, by a data processing system, a first set of operational data of a wind turbine
and a second set of operational data of the wind turbine from one or more data sources. The data
25 processing system determines the values corresponding to physics-based power loss parameters
related to the wind turbine based on the first set of operational data. Further, the data processing
system predicts a rotor speed of the wind turbine using the second set of operational data of the
wind turbine and the determined values corresponding to the physics-based power loss
parameters for an input pitch angle of blades of the wind turbine. Upon predicting the rotor speed,
30 the data processing system, predicts a power loss value indicating a deviation of a power output
of the wind turbine corresponding to the input pitch angle from an ideal power output, based on
the predicted rotor speed, the second set of operational data of the wind turbine and the
determined values corresponding to the physics-based power loss parameters. Additionally, the
data processing system determines an adjusted pitch angle of the blades based on the predicted power loss value. The adjusted pitch angle is adapted for managing power loss of the wind
turbine.
[0008] Further, the present disclosure discloses a data processing system for managing
power loss of a wind turbine. The system comprises a processor and a memory communicatively
10 coupled to the processor. The memory stores the processor-executable instructions, which, on
execution, causes the processor to, obtain a first set of operational data of a wind turbine and a
second set of operational data of the wind turbine from one or more data sources. Further, the
processor determines values corresponding to physics-based power loss parameters related to the
wind turbine based on the first set of operational data. The processor predicts a rotor speed of the
15 wind turbine using the second set of operational data of the wind turbine and the determined
values corresponding to the physics-based power loss parameters for an input pitch angle of
blades of the wind turbine. Additionally, a power loss value is predicted indicating a deviation of
a power output of the wind turbine corresponding to the input pitch angle from an ideal power
output, based on the predicted rotor speed, the second set of operational data of the wind turbine
20 and the values corresponding to the physics-based power loss parameters. Upon determining the
power loss value, an adjusted pitch angle of the blades is determined based on the predicted
power loss value adapted for managing power loss of the wind turbine.
[0009] Further, the present disclosure also discloses a wind turbine comprising a data
25 processing system for managing power loss of a wind turbine.
[0010] The foregoing summary is illustrative only and is not intended to be in any way
limiting. In addition to the illustrative aspects, embodiments, and features described above,
further aspects, embodiments, and features will become apparent by reference to the drawings
30 and the following detailed description.

We Claim:
1. A method of managing power loss of a wind turbine (102), the method comprising:
obtaining, by a data processing system (106), a first set of operational data of a wind
10 turbine (102) and a second set of operational data of the wind turbine (102) from one or
more data sources (104);
determining, by the data processing system (106), values corresponding to physicsbased power loss parameters related to the wind turbine (102) based on the first set of
15 operational data;
predicting, by the data processing system (106), a rotor speed of the wind turbine
(102) using the second set of operational data of the wind turbine (102) and the determined
values corresponding to the physics-based power loss parameters for an input pitch angle
20 of blades of the wind turbine (102);
predicting, by the data processing system (106), a power loss value indicating a
deviation of a power output of the wind turbine (102) corresponding to the input pitch angle
from an ideal power output, based on the predicted rotor speed, the second set of
25 operational data of the wind turbine (102) and the determined values corresponding to the
physics-based power loss parameters; and
determining, by the data processing system (106), an adjusted pitch angle of the
blades based on the predicted power loss value, wherein the adjusted pitch angle is adapted
30 for managing power loss of the wind turbine (102).
2. The method as claimed in claim 1, wherein determining the adjusted pitch angle of the
blades comprises:
performing one of:
when the predicted power loss value is less than a pre-defined
35 threshold value,
inferring the input pitch angle as the adjusted pitch angle; or
when the predicted power loss value is greater than the pre-defined
threshold value,
iteratively varying the input pitch angle to predict a new rotor
speed and a new power loss value corresponding to the varied input
10 pitch angle in each iteration, until the new power loss value is less
than the pre-defined threshold value; and
inferring the varied input pitch angle as the adjusted pitch
angle.
15 3. The method as claimed in claim 2, wherein the varied input pitch angle is determined by
one of incrementing or decrementing the input pitch angle based on the new power loss
value predicted in each iteration.
4. The method as claimed in claim 1, wherein the first set of operational data comprises wind
20 related data and wind turbine (102) related data that impact power generated by the wind
turbine (102).
5. The method as claimed in claim 1, wherein the second set of operational data is one of a
subset of the first set of operational data or same as the first set of operational data.
25
6. The method as claimed in claim 1, wherein the physics-based power loss parameters
comprises at least one of Coefficient of power (Cp), wind power, power loss through the
blades of the wind turbine (102), power loss through gearbox, and power loss through
generator.
30
7. The method as claimed in claim 1, wherein the rotor speed is predicted using a first trained
AI model.
8. The method as claimed in claim 1, wherein the power loss value is predicted using a second
35 trained AI model
9. The method as claimed in claim 1 further comprises transmitting, by the data processing
system (106), the adjusted pitch angle to a pitch control system (108) associated with the
wind turbine (102) to change the pitch angle of the blades to the adjusted pitch angle.
10. A data processing system (106) for managing power loss of a wind turbine (102), the
10 system comprising:
a processor (202); and
a memory (206) communicatively coupled to the processor (202), wherein the memory
(206) stores the processor-executable instructions, which, on execution, causes the
processor (202) to:
15 obtain a first set of operational data of a wind turbine (102) and a second set of
operational data of the wind turbine (102) from one or more data sources (104);
determine values corresponding to physics-based power loss parameters
related to the wind turbine (102) based on the first set of operational data;
20
predict a rotor speed of the wind turbine (102) using the second set of
operational data of the wind turbine (102) and the determined values corresponding to
the physics-based power loss parameters for an input pitch angle of blades of the wind
turbine (102);
25
predict a power loss value indicating a deviation of a power output of the wind
turbine (102) corresponding to the input pitch angle from an ideal power output, based
on the predicted rotor speed, the second set of operational data of the wind turbine (102)
and the values corresponding to the physics-based power loss parameters; and
30
determine an adjusted pitch angle of the blades based on the predicted power
loss value adapted for managing power loss of the wind turbine (102).
11. The data processing system (106) as claimed in claim 10, wherein to determine the adjusted
35 pitch angle of the blades, the processor (202) is configured to:
perform one of:
when the predicted power loss value is less than a pre-defined
threshold value,
infer the input pitch angle as the adjusted pitch angle; or
when the predicted power loss value is greater than the pre-defined
10 threshold value,
iteratively vary the input pitch angle to predict a new rotor
speed and a new power loss value corresponding to the varied input
pitch angle in each iteration, until the new power loss value is less
than the pre-defined threshold value; and
15 infer the varied input pitch angle as the adjusted pitch angle.
12. The data processing system (106) as claimed in claim 11, wherein the processor (202)
determines the varied input pitch angle by one of incrementing or decrementing the input
pitch angle based on the new power loss value predicted in each iteration.
20
13. The data processing system (106) as claimed in claim 10, wherein the first set of operational
data comprises wind related parameters and turbine related parameters of the wind turbine
(102).
25 14. The data processing system (106) as claimed in claim 10, wherein the second set of
operational data is one of a subset of the first set of operational data or same as the first set
of operational data.
15. The data processing system (106) as claimed in claim 10, wherein the physics-based power
30 loss parameters comprises at least one of Coefficient of power (Cp), wind power, power
loss through the blades of the wind turbine (102), power loss through gearbox, and power
loss through generator.
16. The data processing system (106) as claimed in claim 10, wherein the processor (202)
35 predicts the rotor speed using a first trained AI model.
35
5 17. The data processing system (106) as claimed in claim 10, wherein the processor (202)
predicts the power loss value using a second trained AI model.
18. The data processing system (106) as claimed in claim 10, wherein the processor (202) is
further configured to transmit the adjusted pitch angle to a pitch control system (108)
10 associated with the wind turbine (102) to change the pitch angle of the blades to the
adjusted pitch angle.
19. A wind turbine (102) comprising a data processing system (106) as claimed in the claims
10-18 for managing power loss of a wind turbine (102).