Optimal wind farm operation

• US 10,161,386 B2
• Filed: 12/12/2016
• Issued: 12/25/2018
• Est. Priority Date: 06/10/2014
• Status: Active Grant

• Current Assignee: Hitachi Energy Limited (Hitachi, Ltd.)
• Original Assignee: ABB Schweiz AG (ABB Limited)
• Inventors: Timbus, Adrian, Franke, Carsten, Zima, Marija
• Primary Examiner(s): Lin, Jason
• Application Number:
  US15/375,904
• Publication Number:
  US 20170089325A1
• Time in Patent Office: 743 Days
• Field of Search: None
• CPC Class Codes: F03D 7/0292 to reduce fatigue
• CPC Class Codes: F03D 7/043 characterised by the type of control logic
• CPC Class Codes: F03D 7/045 with model-based controls
• CPC Class Codes: F03D 7/048 controlling wind farms
• CPC Class Codes: F03D 80/50 Maintenance or repair
• CPC Class Codes: F03D 9/257 the wind motor being part of a wind farm
• CPC Class Codes: F05B 2270/20 to optimise the performance of a machine
• CPC Class Codes: F05B 2270/332 Maximum loads or fatigue criteria
• CPC Class Codes: F05B 2270/404 active, predictive, or anticipative
• CPC Class Codes: G05B 13/026 using a predictor
• CPC Class Codes: G05B 19/048 Monitoring; Safety
• CPC Class Codes: G05B 2219/24001 Maintenance, repair
• CPC Class Codes: G05B 2219/2619 Wind turbines
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: Y02E 10/72 Wind turbines with rotation axis in wind direction

Abstract

The present application is concerned with a flexible way of operating a wind farm with a plurality of degrading wind turbine components. According to the invention, maintenance scheduling and power production in the wind farm are handled concurrently in a single optimization step. Instead of a serial approach first scheduling maintenance activities and subsequently adapting the power production and/or wind turbine operation the two aspects are optimized together. The wind farm operation takes maintenance aspects into account by adapting life index or health status based on modeled mechanical and electrical stress. Accordingly, the wind farm owner may decide when and how much energy to produce accepting which level of stress to the turbine equipment. The proposed optimization of wind farm operation includes all aspects of transmission network operator settings, the topology of wind farms and the underlying collector grid, the short and long term wind conditions forecasts, the conditions of the turbines, the estimated remaining operational time under different usage patterns and times, as well as aspects of the electricity market.

Claims

1. A method of operating a wind farm including plurality of wind turbines with a plurality of turbine components comprising a first and further turbine components, said plurality of turbine components subject to degradation, comprising:

predicting, for a turbine component of a first wind turbine and for each of a sequence of time intervals (t₁ . . . t_N1) into the future, based on a sequence of first turbine control input values (u₁(t₁) . . . u1(t_N1)) including a component maintenance action at a maintenance interval tM1, a component life index L(t) of the first turbine component,

determining a sequence of optimum turbine control input values (u₁*(t₁) . . . u₁*(t_N1)) including

a) an optimum maintenance interval t_Ml* for the first turbine component and

b) optimum maintenance intervals t_Mj* for each of the further turbine components that optimize an objective function J(u) depending on the component life index L of the first turbine component and on component life indices L_j of the further turbine components, and

operating the first wind turbine according to at least one optimum turbine control input value u₁*(t₁), wherein the method further comprises

predicting the component life index of the component based on a sequence of second turbine control input values (u₂(t₁) . . . u₂(t_N2)) of a second wind turbine electrically connected to a same branch of a collector grid of the wind farm as the first wind turbine, by evaluating voltage phase and amplitude difference between the first turbine and the second turbine, and

determining optimum first and second turbine control input values u₁*(t), u₂*(t).

2. The method of claim 1, comprising

predicting the component life index of the component based on the sequence of second turbine control input values (u₂(t₁) . . . u₂(t_N2)) of the second wind turbine located upstream of the first wind turbine by evaluating predicted wind turbulences at the first turbine and caused by operational behavior of the second wind turbine, and

determining optimum first and second turbine control input values u₁*(t), u₂*(t).

3. The method of claim 2, comprising

providing for each of the sequence of time intervals (t₁ . . . t_Nj) into the future a wind forecast, and calculating a sequence of electrical power output values (p_j(t₁) . . . p_j(t_Nj)) of each of the plurality of turbines of the wind farm based on the wind forecast and based on a respective sequence of turbine control input values (u_j(t₁) . . . u_j(t_Nj)),

providing for each of the sequence of time intervals (t₁ . . . t_Nj) into the future an electrical power demand or power generation forecast P(t) for the wind farm, and

providing an objective function J penalizing a deviation of a sum of the calculated electrical power outputs p_j(t) of the plurality of turbines of the wind farm from the power demand or power generation forecast P(t).

4. The method of claim 2, comprising

determining the sequence of optimum turbine control input values (u₁*(t₁) . . . u₁*(t_N1)) at least once per hour.

5. The method of claim 2, wherein the wind turbines of the wind farm are electrically connected to a collector grid, comprising

providing an objective function including a term penalizing power flow imbalance in the collector grid.

6. The method of claim 2, wherein the objective function includes a term indicative of earnings from electrical power generated by the first turbine over a time span including the component maintenance time t_M.

7. The method of claim 1, comprising

providing for each of the sequence of time intervals (t₁ . . . t_Nj) into the future a wind forecast, and calculating a sequence of electrical power output values (p_j(t₁) . . . p_j(t_Nj)) of each of the plurality of turbines of the wind farm based on the wind forecast and based on a respective sequence of turbine control input values (u_j(t₁) . . . u_j(t_Nj)),

providing for each of the sequence of time intervals (t₁ . . . t_Nj) into the future an electrical power demand or power generation forecast P(t) for the wind farm, and

providing an objective function J penalizing a deviation of a sum of the calculated electrical power outputs p_j(t) of the plurality of turbines of the wind farm from the power demand or power generation forecast P(t).

8. The method of claim 7, comprising

determining the sequence of optimum turbine control input values (u₁*(t₁) . . . u₁*(t_N1)) at least once per hour.

9. The method of claim 7, wherein the wind turbines of the wind farm are electrically connected to a collector grid, comprising

providing an objective function including a term penalizing power flow imbalance in the collector grid.

10. The method of claim 7, wherein the objective function includes a term indicative of earnings from electrical power generated by the first turbine over a time span including the component maintenance time t_M.

11. The method of claim 1, comprising

determining the sequence of optimum turbine control input values (u₁*(t₁) . . . u₁*(t_N1)) at least once per hour.

12. The method of claim 11, wherein the wind turbines of the wind farm are electrically connected to a collector grid, comprising

providing an objective function including a term penalizing power flow imbalance in the collector grid.

13. The method of claim 11, wherein the objective function includes a term indicative of earnings from electrical power generated by the first turbine over a time span including the component maintenance time t_M.

14. The method of claim 1, wherein the wind turbines of the wind farm are electrically connected to a collector grid, comprising

providing an objective function including a term penalizing power flow imbalance in the collector grid.

15. The method of claim 14, wherein the objective function includes a term indicative of earnings from electrical power generated by the first turbine over a time span including the component maintenance time t_M.

16. The method of claim 1, wherein the objective function includes a term indicative of earnings from electrical power generated by the first turbine over a time span including the component maintenance time t_M.

17. A wind farm management system for operating a wind farm including a plurality of wind turbines with a plurality of turbine components comprising a first and further turbine components, said plurality of turbine components subject to degradation, comprising:

a processor;

memory in electronic communication with the processor; and

instruction stored in the memory, the instructions being executable by the processor to;

predict a component life index L(t) of a turbine component of a first wind turbine, for each of a sequence of time intervals (t₁ . . .t_N) into the future, based on a model of the first wind turbine and based on a sequence of first turbine control input values (u₁*(t₁) . . . u₁*(t_N)) including a component maintenance action at a maintenance interval t_M,

determine a sequence of optimum turbine control input values (u₁*(t₁) . . . u₁*(t_N)) including

a) an optimum maintenance interval t_M* for the first turbine component and

b) optimum maintenance intervals t_Mj* for each of the further turbine components that optimize an objective function J(u) depending on the component life index L of the first turbine component and on component life indices L_j of the further turbine components, and

operate the first wind turbine according to at least one optimum turbine control input value u₁*(t₁),

predict the component life index of the component based on a sequence of second turbine control input values (u₂(t₁) . . . u₂(t_N2)) of a second wind turbine electrically connected to a same branch of a collector grid of the wind farm as the first wind turbine, by evaluating voltage phase and amplitude difference between the first turbine and the second turbine, and

determine optimum first and second turbine control input values u₁*(t), u₂*(t).