Automated Energy Parameter Extraction from Sensor Data
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Solution Overview
Problem
Current energy management systems require significant time, personnel, and financial resources for manually configuring and updating parameters of energy conversion systems, which are prone to errors due to varying boundary conditions and operational changes.
Innovation Solution
A method to automatically extract and update parameters from measurement data using a plant model, involving signal preprocessing, anomaly detection, and correlation analysis to determine static, transient, and dependency parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If parameters are manually configured and updated during commissioning and operation, then initial system setup is possible, but significant time, personnel, and financial resources are consumed and errors are prone
Solution Approach 1:
The energy conversion system automatically determines its own operating parameters by processing measurement data through the control unit, eliminating the need for manual configuration. The system self-updates parameters during operation based on actual measured values, making it self-servicing and reducing dependency on external commissioning personnel.
Solution Approach 2:
Manual mechanical configuration processes are replaced by an automated electronic system. The control unit electronically processes measurement data to automatically determine and update parameters, substituting the manual mechanical process of parameter configuration with an automated computational process.
2Adaptability or versatility
If parameters are manually configured, then initial setup is achieved, but parameters must be re-analyzed and re-configured after software updates or user adjustments
Solution Approach 1:
The parameter configuration system transitions from static manual configuration to dynamic automatic updates. Parameters are continuously adapted based on real-time measurement data and operational conditions, allowing the system to dynamically adjust to software updates and user requirements without manual reconfiguration.
Solution Approach 2:
The system implements continuous feedback loops where measurement data from sensors is processed by the control unit to automatically update parameters. This feedback mechanism ensures parameters remain accurate and adaptive after software updates or operational changes, eliminating the need for manual re-analysis and re-configuration.
3Reliability
If comprehensive parameter information is collected for energy management systems, then operational planning and control reliability improve, but system complexity and data processing requirements increase
Solution Approach 1:
The control unit extracts only the essential operating parameters needed for energy management from the comprehensive measurement data. By selectively extracting relevant parameters (such as power output, efficiency metrics, and operational state) rather than processing all available data, the system reduces processing complexity while maintaining operational reliability.
Solution Approach 2:
The parameter determination process is segmented into distinct functional modules within the control unit: data acquisition from sensors, signal preprocessing, parameter calculation, and parameter storage. This segmentation allows each module to handle specific tasks efficiently, reducing overall system complexity while comprehensively collecting necessary parameter information.
Data Source
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AI summary
The problem addressed by the invention, which relates to a method for calculating parameters of one or more energy conversion systems (2), is that of indicating a method which allows the automated production of the necessary parameters from measurement data (9) of sensors (6). This problem is solved in that: measurement data (9) are sensed by means of sensors (6) and, in a measurement data assignment (17) step, are assigned to measurement points and, after a first signal preprocessing (19) in which anomalies are detected, are saved in a memory (22); the saved measurement data (9) undergo a second signal preprocessing (24) in which state changes are detected (27) and state changes are analysed (30) by means of correlation, and in which state change curves (34) comprising their measurement data (9) are produced; and subsequently, on the basis of the state change curves (34) comprising their measurement data (9), stationary parameters (31) are determined (47), transient parameters (32) are determined (38), and dependencies (33) are determined (41).