Dynamic Operating Parameter Identification in Power Plants
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Solution Overview
Problem
Existing methods for determining optimal operating conditions in power plants are limited by their reliance on static data and fail to account for current variations in load, fuel characteristics, and circumstances, leading to inaccurate energy-loss analysis and inefficient operations.
Innovation Solution
A method using a partitional clustering algorithm, such as k-means, to generate a statistical model from historical operating condition data, allowing for the calculation of dynamic operating condition target values and real-time energy-loss analysis, which identifies key operating parameters contributing to energy loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If known methods (designed target values, overhauled target values, off-design target values) are used to determine optimal operating conditions, then implementation cost and time are reduced, but accuracy of energy-loss analysis deteriorates because they use only constant or static data and do not account for current variations in load, fuel characteristics, or present circumstances
Solution Approach 1:
The patent transforms static target value determination into a dynamic process by using a statistical model that continuously adapts to current operating conditions. The system calculates dynamic optimal target values based on real-time data from sensors monitoring load, fuel characteristics, and environmental conditions, allowing the power plant to optimize performance under varying circumstances rather than relying on fixed predetermined values
Solution Approach 2:
The system implements feedback by continuously monitoring current operating conditions through sensors and using this information to update the statistical model and recalculate optimal target values. The energy-loss analysis module compares actual operating parameters against these dynamic targets and provides feedback to operators for adjustment, creating a closed-loop control system that improves accuracy over time
2Adaptability or versatility
If known methods are used to determine target values, then implementation is simpler and faster, but they fail to account for current variations in load, fuel characteristics, or present circumstances leading to suboptimal performance
Solution Approach 1:
The system performs preliminary action by pre-processing historical operating data to build a statistical model that captures relationships between operating conditions and performance. This pre-computed model enables rapid calculation of dynamic target values when new operating conditions arise, eliminating the need for time-consuming manual analysis while maintaining adaptability to current variations
Solution Approach 2:
The patent replaces manual or mechanical methods of determining optimal operating conditions with an automated computational system. The statistical model and computer algorithms automatically calculate dynamic target values based on sensor inputs, substituting human judgment and manual calculations with automated data processing that is both faster and more adaptable to varying conditions
Data Source
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AI summary
A method (200) for obtaining operating parameters of a power plant includes data mining (202) a historical operating condition database for the power plant with a partitional clustering algorithm to generate a statistical model, and calculating (204) dynamic operating condition target values from the statistical model taking into account current operating condition data of the power plant. The method further includes performing (208) real-time energy-loss calculating based on the dynamic operating condition target values and automatically identifying (210) at least one operating parameter of the power plant from the energy-loss calculating. The partitional clustering algorithm can be a k-means clustering algorithm.