Power Generation Control Using Digital Twin Parameter Estimation
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Solution Overview
Problem
Industrial electromechanical systems, such as electric drive trains, face challenges in performance optimization and health assessment due to the complexity of monitoring and controlling their operational parameters in real-time, leading to inefficiencies and potential faults that can disrupt operations.
Innovation Solution
The implementation of digital twin models that create real-time operational models of prime mover and generator units, allowing for the estimation of parameters and health metrics, and enabling controlled operation based on received data, thereby enhancing predictive maintenance and performance optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital twin models are implemented for real-time parameter estimation, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The patent creates digital twin models (virtual copies) of the prime mover and generator units to estimate operational parameters. These digital models replicate the behavior of physical units, enabling accurate parameter estimation without directly measuring all physical parameters, thus improving measurement precision while managing system complexity through virtual representation.
Solution Approach 2:
The digital twin models act as intermediaries between the physical power generation system and the control system. Instead of directly measuring all parameters from physical sensors, the system uses digital models to estimate parameters based on available data, reducing the need for complex sensor networks while improving estimation accuracy.
2Productivity
If real-time operational modeling is implemented, then productivity and response time are improved, but use of energy increases
Solution Approach 1:
The system implements real-time operational modeling selectively for critical parameters that most impact control decisions, rather than continuously modeling all possible parameters. This partial action approach maintains productivity benefits while reducing the computational energy required compared to comprehensive real-time modeling of all system aspects.
3Reliability
If comprehensive parameter monitoring is implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent uses digital twin models to create virtual representations of the prime mover and generator units, enabling comprehensive parameter monitoring through these digital copies. This approach provides reliable monitoring of multiple parameters simultaneously while avoiding the complexity of installing and maintaining numerous physical sensors for each parameter.
Solution Approach 2:
The digital twin models serve multiple functions simultaneously: they estimate operational parameters, assess unit health, predict failures, and support control decisions. This multi-functionality achieves comprehensive monitoring for improved reliability without the complexity of separate dedicated systems for each function.
Data Source
AI summary
A method for controlling operation of an electric power generation system includes receiving power generator data corresponding to the electric power generation system. The method further includes receiving, using a digital prime mover unit, a set-point parameter corresponding to a prime mover unit and generating one or more prime mover parameter estimates corresponding to the plurality of prime mover parameters. Further, the method includes receiving, using a digital generator unit, one or more prime mover parameter estimates and generating one or more generator parameter estimates corresponding to the plurality of generator parameters. The digital prime mover unit and the digital generator unit are real-time operational models of the prime mover unit and the generator unit. The method also includes controlling the operation of the electric power generation system based on at least one or more of the power generator data, the prime mover parameter estimates, and the generator parameter estimates.