Gas Turbine Self-Learning Control for Critical State Prevention
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Solution Overview
Problem
Existing gas turbine control systems are inadequate in preventing critical events such as unstable combustion conditions and excessive emissions, which can lead to structural damage and inefficiency, as they only respond after the events have occurred, rather than proactively.
Innovation Solution
A self-learning control system for gas turbines that utilizes sensors and processing modules to detect critical states and implement corrective actions before adverse conditions escalate, incorporating a state detection stage, plant controller, and a self-learning module to associate critical states with appropriate interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If corrective actions are taken only after critical events are detected, then the control system responds to actual problems, but the intervention is delayed and cannot prevent damage
Solution Approach 1:
The control system performs preliminary actions by detecting critical states before they evolve into critical events. The self-learning module identifies patterns and correlates current states with historical data to predict potential issues, enabling preventive interventions rather than reactive corrections after damage occurs.
Solution Approach 2:
The system applies beforehand cushioning by maintaining a database of critical states and their associated corrective actions. When a critical state is detected, the system has pre-prepared remediation strategies ready to be deployed immediately, cushioning against the potential development of critical events and their harmful effects.
2Reliability
If the control system monitors all operational parameters continuously, then critical events can be detected earlier, but the system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary organization of operational data by structuring it into meaningful states and storing them in a database. This pre-processing allows for efficient pattern recognition and correlation analysis without requiring complex real-time processing of all raw sensor data, thus maintaining detection accuracy while managing system complexity.
Solution Approach 2:
The system creates copies of operational data in structured formats (states database, critical states database) that can be efficiently queried and analyzed. Instead of processing all raw data continuously, the system works with these organized copies, reducing computational complexity while preserving the information needed for accurate detection.
3Reliability
If the control system implements proactive prevention mechanisms, then critical events can be avoided, but the system requires more sophisticated analysis and decision-making capabilities
Solution Approach 1:
The control system implements self-service through its self-learning module, which automatically analyzes operational data, identifies critical states, correlates them with historical patterns, and selects appropriate corrective actions without human intervention. This automated self-service capability enables proactive prevention while managing the complexity of sophisticated analysis through autonomous operation.
Solution Approach 2:
The system uses feedback mechanisms where the outcomes of corrective actions are fed back into the database, allowing the self-learning module to continuously improve its detection and decision-making capabilities. This feedback loop enables the system to automate sophisticated analysis by learning from past experiences and refining its prevention strategies over time.
Data Source
Figure 1~5
Figure 2
AI summary
A control system of a gas turbine includes: a state detection stage (4), providing state signals (S0, ..., SN); a monitoring module (15), which receives a first subset (SS1) of the state signals and detects the occurrences of critical events as a function of the state signals of the first subset (SS1); a state registration module (16), which receives a second subset (SS2) of the state signals and associates critical states (CRS) to the detected occurrences of critical events; a database (19) defining associations between critical events and corrective actions (ACT) to be executed in response to the occurrence of respective critical events; a self-learning module (17), which correlates critical states (CRS) and corrective actions (ACT) as a function of the associations between critical states (CRS) and critical events produced by the state registration module (16) and of the associations contained in the first database (19).