Command Controller for Subsystem Performance Tuning
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Solution Overview
Problem
In combined cycle power plants, achieving overall system target performance requires human intervention to determine which subsystems to adjust and what target performance each should achieve, lacking an automated command controller to determine target settings for subsystems.
Innovation Solution
A control system with a command controller that analyzes the gap between a site reference model and a site-specific model to set target performance for each subsystem, using a rule-based multi-dimensional optimization routine or neural network to determine the necessary control adjustments, balancing parameter priorities and tolerances to minimize costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If human intervention is used to determine subsystem adjustments and target performance, then overall system target performance can be achieved, but the system lacks automation and requires manual operation
Solution Approach 1:
The command controller automatically determines target performance for each subsystem by analyzing the gap between site reference model and site-specific model, enabling the system to self-adjust without human intervention. The controller performs rule-based multi-dimensional optimization and neural network calculations to autonomously set subsystem targets.
Solution Approach 2:
The patent replaces manual human decision-making with an automated command controller that uses rule-based optimization algorithms and neural networks to determine subsystem target performance. This substitution of mechanical/manual operations with automated computational systems resolves the contradiction between automation and ease of operation.
2Productivity
If multiple subsystems are adjusted to achieve overall system performance, then system performance improves, but the complexity of determining which subsystems to adjust increases
Solution Approach 1:
The command controller divides the overall system performance optimization into separate subsystem target determinations. It analyzes the performance gap and distributes adjustment targets to individual subsystems (gas turbine, steam turbine, etc.) independently, making the complex coordination problem manageable through segmentation of the control task.
Solution Approach 2:
The system uses rule-based multi-dimensional optimization routines and neural networks to automatically determine optimal parameter changes for each subsystem. By changing control parameters systematically based on performance gaps, the system achieves overall performance improvement without manually complex coordination.
3Manufacturing precision
If parameter changes are made to achieve target performance, then system performance reaches quoted levels, but operational costs increase
Solution Approach 1:
The command controller uses rule-based multi-dimensional optimization routines that explicitly consider operational costs when determining parameter changes. The optimization balances the need to achieve quoted performance levels with the objective of minimizing operational costs by selecting the most economical parameter adjustments.
Solution Approach 2:
The system applies different optimization strategies to different subsystems based on their specific characteristics and cost structures. By tailoring the target performance determination to each subsystem's local conditions rather than applying uniform adjustments, the system achieves accurate overall performance while minimizing total operational costs.
Data Source
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AI summary
A system 10 to performance tune a site system 110 includes one or more controllers 180 each controlling a subsystem of the site system by changing values of a set of parameters 130. The system also includes a site reference model 105 configured to indicate a target performance of the site system, and a processor configured to instruct the one or more controllers 120 based on the target performance for the site system.