Power Plant Control Parameter Tuning for Faster Load Changes
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Solution Overview
Problem
Existing power plants face limitations in improving load change rate due to numerous process control factors, requiring expert tuning and often resulting in instability issues like cycling or hunting, which are not adequately addressed by current methods.
Innovation Solution
An apparatus and method that include a model generator to configure a forecast model, a model corrector to adjust process model parameters based on real power plant operation data, and a tuner to optimize time delay parameters in the forecast model to achieve a target load increase rate, using RMSD or MAD minimization techniques within preset correction ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If expert tuning of control parameters is performed, then load change rate can be improved, but the system becomes complex and difficult to operate
Solution Approach 1:
The system performs automatic tuning of control parameters using a forecast model and optimization algorithm, eliminating the need for expert intervention. The model self-adjusts parameters based on process data and performance targets, making the system self-sufficient in parameter optimization without requiring specialized knowledge.
Solution Approach 2:
The system automatically modifies control parameters by analyzing the relationship between process parameters and performance indicators through the forecast model. It identifies optimal parameter settings that achieve the desired load change rate by systematically adjusting parameters based on model predictions and actual performance feedback.
2Speed
If control parameters are tuned to improve load change rate, then responsiveness improves, but stability deteriorates due to cycling or hunting
Solution Approach 1:
The system uses a forecast model that continuously monitors process parameters and performance indicators, comparing predicted outcomes with actual results. This feedback mechanism allows the system to adjust control parameters dynamically, preventing instability issues like cycling or hunting by making incremental adjustments based on observed performance deviations.
Solution Approach 2:
The forecast model predicts the outcome of potential parameter changes before actual implementation. By simulating and evaluating different parameter settings in advance, the system identifies optimal adjustments that improve load change rate while maintaining stability, avoiding harmful cycling or hunting behaviors before they occur.
3Ease of operation
If simple tuning methods are used, then ease of operation is maintained, but manufacturing precision deteriorates as sufficient improvement cannot be achieved
Solution Approach 1:
The forecast model acts as an intermediary between simple operation and precise control. It automatically performs complex analysis and optimization calculations, translating simple operational inputs into precise control parameter adjustments. This mediator handles the computational complexity while maintaining ease of operation for the user.
Solution Approach 2:
The system replaces manual expert tuning with an automated computational model. Instead of relying on human expertise and manual adjustment, the forecast model uses algorithms to automatically determine optimal parameter settings, achieving high precision control through computational methods rather than mechanical or manual processes.
Data Source
AI summary
An apparatus for optimizing control parameters of a power plant is provided. The apparatus for optimizing control parameters of a power plant includes: a model generator configured to configure a forecast model including a process model and a control model, a model corrector configured to correct a first parameter of the process model through operation data of a real power plant, and a tuner configured to tune a second parameter, which is a parameter related to a time delay of the forecast model, so as to have a target load increase rate.