Plant Analysis Using Variation Models for Operating Conditions
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Solution Overview
Problem
Existing analysis methods for plant behavior fail to accurately account for variations in device characteristics due to operating conditions, leading to incomplete or inaccurate predictions of plant operations.
Innovation Solution
An analysis apparatus and method that incorporates a variation model storage unit to store models of characteristic variations, a model extraction unit to acquire and match structure and variation models, and an analysis unit to analyze the plant based on these models, considering operating conditions such as load, control information, and ambient environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing analysis methods are used for plant behavior, then analysis can be performed with simple models, but accuracy of predictions is insufficient due to failure to account for variations in device characteristics
Solution Approach 1:
The plant model is segmented into multiple device models, where each device model can be independently configured with its own variation characteristics. This allows the system to capture complex plant behavior through composition of simpler, modular device-level models, resolving the contradiction between prediction accuracy and model complexity.
Solution Approach 2:
The system changes parameters by introducing variation characteristics (such as load-dependent parameters, control-dependent parameters, and ambient environment-dependent parameters) to device models. This enables accurate prediction of plant behavior under different operating conditions without requiring entirely new complex models for each scenario.
2Measurement precision
If variation models for all device characteristics are incorporated, then prediction accuracy improves, but data processing requirements and computational load increase
Solution Approach 1:
Instead of applying uniform complex models to all devices, the system applies variation characteristics locally to each device based on its specific needs and operating conditions. Each device model includes only the variation parameters relevant to its function and operating environment, reducing overall data processing requirements while maintaining analysis accuracy.
Solution Approach 2:
Variation models and device characteristics are configured in advance before actual plant operation analysis. This preliminary configuration allows the system to have accurate models ready for use, reducing computational load during runtime analysis while maintaining high prediction accuracy.
Data Source
AI summary
Provided is an analysis apparatus comprising: a variation model storage unit configured to store a plurality of variation models indicating variation in characteristics of a plant corresponding to an operating condition of the plant; a model extraction unit configured to acquire structure information indicating a structure model of an analysis target plant and to extract the variation model corresponding to the structure model; and an analysis unit configured to analyze the analysis target plant, based on the structure model of the analysis target plant and the variation model extracted by the model extraction unit.