Model-Based Control Perturbation Feedback Power Plant
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Solution Overview
Problem
Model-based control systems for power generation plants may not accurately predict optimal performance due to variations between plants and unforeseen degradation, leading to suboptimal operation, increased fuel consumption, reduced power generation, higher emissions, and increased wear on equipment.
Innovation Solution
A method that perturbs control inputs in power generation plants and models to compare actual and predicted performance, allowing for real-time adjustments to accurately simulate and achieve optimal operating conditions by modifying the model based on improvements from perturbations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model-based control system uses standard model assumptions and estimates to predict optimal performance, then the control system can operate autonomously with simple structure, but the predicted performance may not accurately represent true optimal performance due to plant variations and degradation
Solution Approach 1:
The patent implements a feedback mechanism where the actual plant response to perturbations is compared with the model's predicted response. This comparison generates error signals that are used to update and refine the model parameters, ensuring the model continuously adapts to actual plant behavior and degradation patterns while maintaining autonomous operation
Solution Approach 2:
The control system performs self-diagnosis and self-adjustment by automatically detecting deviations between predicted and actual plant responses to perturbations. The system autonomously identifies model inaccuracies and corrects them through iterative refinement without requiring external intervention or complex manual calibration procedures
2Productivity
If the model accurately predicts optimal performance conditions, then fuel consumption is minimized and power generation is maximized, but the model requires complex adjustments to account for plant variations and degradation
Solution Approach 1:
Rather than attempting to model all possible plant variations and degradation modes from the beginning, the system applies small, targeted perturbations to specific control inputs and only adjusts model parameters when actual responses deviate from predictions. This incremental approach achieves high accuracy without requiring comprehensive complex modeling of all potential plant conditions
Solution Approach 2:
The system dynamically adjusts model parameters based on observed plant responses to perturbations. By changing only the necessary parameters that show deviations between predicted and actual behavior, the system maintains high productivity while avoiding the complexity of redesigning the entire model structure
3Measurement precision
If perturbations are applied to control inputs to test model accuracy, then the model can be refined to capture plant variations and degradation, but the control settings deviate from optimal during the testing process
Solution Approach 1:
The system applies perturbations periodically rather than continuously, introducing small deviations at scheduled intervals to test model accuracy. Between perturbation events, the system operates at optimal settings determined by the model, minimizing the impact on productivity while still gathering data for model refinement
Solution Approach 2:
The perturbations applied are small and limited in magnitude, affecting only specific control inputs rather than all system parameters. This partial action approach allows the system to test model accuracy with minimal deviation from optimal operation, maintaining high productivity during the refinement process
Data Source
AI summary
A method to control an power generation plant including: applying control settings to operate the plant; collecting plant data indicative of the performance of the plant; applying the control settings to a model of the plant; collecting prediction data from the model; comparing the plant data to the predicted data and adjusting the control settings applied to the plant and model; perturbing the control settings and applying the perturbed control settings to operate the plant and the model; collecting perturbed plant data and perturbed prediction data, and modifying the model if the perturbed plant data represents an improvement as compared to the perturbed prediction data.