Turbine Control Parameters Using Residual Lifetime Modeling

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Solution Overview

Problem

Existing methods for determining control parameters of turbines, such as wind and gas turbines, do not effectively maximize annual energy production (AEP) without risking component overload or reduced lifetime, as they lack consideration of individual manufacturing tolerances and specific performance characteristics.

Innovation Solution

A computer-implemented method using a turbine model to determine control parameters based on actual operation and ambient conditions, incorporating manufacturing tolerance data to simulate and estimate the residual lifetime of components, allowing for optimized power generation and extended operation within safe limits.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If control parameters are optimized to maximize power output, then annual energy production (AEP) increases, but component lifetime is reduced due to overloading

Engineering Contradiction:
Improveannual energy productionVSAvoidcomponent lifetime
Core Design Contradiction:
ProductivityVSDuration of action of stationary object

Solution Approach 1:

The system dynamically adjusts control parameters (such as power output limits, operational thresholds) based on real-time component condition data and predicted remaining lifetime. This allows the turbine to operate at optimized power levels that maximize AEP while keeping stress on components within safe limits, preventing premature failure.

Inventive Principle:
Principle #35Parameter changes

Solution Approach 2:

The system implements a closed-loop feedback mechanism where component condition data (vibration, temperature, stress) is continuously monitored, and the control parameters are adjusted based on this feedback. This ensures that power output is optimized without exceeding component capacity, resolving the contradiction between maximizing energy production and preserving component lifetime.

Inventive Principle:
Principle #23Feedback

2Productivity

If individual manufacturing tolerances are considered, then control parameters can be optimized for specific turbines, but system complexity increases

Engineering Contradiction:
Improvepower generation efficiencyVSAvoidcontrol system complexity
Core Design Contradiction:
ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary characterization of each turbine during manufacturing or initial operation, capturing individual manufacturing tolerances and performance characteristics. This data is stored and used to pre-calculate optimized control parameters specific to each turbine, avoiding the need for complex real-time adjustments while maximizing power generation efficiency.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The system creates a digital model or 'copy' of each turbine's specific characteristics based on manufacturing data and initial performance measurements. This digital twin is then used to simulate and optimize control parameters without requiring physical modifications or complex real-time sensing systems, reducing overall system complexity.

Inventive Principle:
Principle #26Copying

3Measurement precision

If additional sensors are installed to monitor component condition, then lifetime prediction accuracy improves, but cost and device complexity increase

Engineering Contradiction:
Improvelifetime prediction accuracyVSAvoidsensor system complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The system utilizes existing turbine operational data (power output, rotational speed, temperature from standard monitoring) and manufacturing tolerance data to predict component lifetime. By leveraging data already available from normal operation and using physics-based models, the system achieves accurate lifetime prediction without requiring additional specialized sensors or monitoring equipment.

Inventive Principle:
Principle #25Self-service

Solution Approach 2:

The system introduces a computational model as an intermediary that processes existing operational data and manufacturing information to derive lifetime predictions. This software-based intermediary replaces the need for additional physical sensors, achieving high measurement precision while avoiding the cost and complexity of hardware additions.

Inventive Principle:
Principle #24Intermediary (Mediator)

Data Source

PatentEP3987177B1Method for computer-implemented determination of control parameters of a turbine
Publication Date: 2024.10.09 SIEMENS GAMESA RENEWABLE ENERGY AS
  • EP3987177B1 patent drawingFigure 1
  • EP3987177B1 patent drawingFigure 2~3

AI summary

The invention describes a method for computer-implemented de- termination of improved control parameters (CP) of a turbine (T1,...,Tn) by consideration of a lifetime information. The turbine (T1,", Tn) is either a wind turbine having a genera- tor or a gas turbine having a generator. The method considers the impact of individual turbine manufacturing tolerances on the turbine performance, thereby avoiding under-utilization of those wind turbines. The invention comprises the steps of: receiving, by an interface (IF), actual operation (OC) param- eters of the turbine (T1,",Tn) and/or ambient condition (AC) information; determining, by a processing unit (PU), a life- time information (RLT) about a residual lifetime of the tur- bine (T1,",Tn) or a turbine component by a simulation of the operation of the turbine (T1,...,Tn), the simulation being made with a given turbine model (TM) in which the actual op- eration (OC) parameters of the turbine (T1,",Tn) and/or ambi- ent condition (AC) information and one or more characteristic values (AG, MP, MDM, TC, CR) of the turbine (T1,...,Tn) are used as input parameters; and deriving, by the processing unit (PU), the control parameters (CP) for the turbine (T1,...,Tn) from lifetime information (RLT).