Power Supply Fault Pattern Generation Without Nominal State Influence
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Solution Overview
Problem
Current fault detection methods in power supply systems rely heavily on manual evaluation and are inefficient in identifying causal faults due to the influence of nominal states on fault patterns, making it difficult to reconstruct and address critical events effectively.
Innovation Solution
A method that generates fault patterns by eliminating the influence of nominal power supply system states, using measurable values and modeling to create patterns independent of fault-free states, allowing for real-time and offline pattern generation and comparison with stored patterns to identify faults and propose countermeasures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of fault messages is used, then detailed analysis is possible, but time consumption and efficiency are reduced
Solution Approach 1:
The system performs preliminary actions by pre-generating fault patterns through simulations of various fault scenarios and storing them in a database before actual fault detection occurs. When a fault happens, the system only needs to compare measured values against pre-computed patterns, dramatically reducing analysis time while maintaining accuracy
Solution Approach 2:
The system creates copies of fault conditions through simulations, generating representative fault patterns that replicate actual fault behaviors. These copied fault scenarios are stored and used for comparison during real fault detection, enabling automated analysis without manual intervention
2Loss of information
If fault patterns are generated including nominal state influence, then complete system state information is captured, but pattern complexity and difficulty in identification increase
Solution Approach 1:
The system extracts and separates the nominal state influence from fault patterns by generating patterns that represent only the fault-specific deviations. This is achieved by simulating faults on top of nominal states and extracting only the differential characteristics, resulting in simpler, more identifiable patterns while preserving essential fault information
Solution Approach 2:
The fault detection process is segmented into distinct components: nominal state characterization, fault scenario simulation, and differential pattern generation. By separating these functions, the system captures complete system state information while keeping the actual fault patterns simple and focused on deviation characteristics
3Extent of automation
If model-based fault detection is implemented, then automated fault identification is possible, but computational complexity and system requirements increase
Solution Approach 1:
The system performs preliminary modeling work by pre-generating fault patterns through simulations and storing them in a database. This preliminary action transfers computational complexity from real-time operation to offline preparation, enabling simple automated comparison during actual fault detection without requiring complex real-time modeling capabilities
Solution Approach 2:
The system introduces an intermediary layer of pre-computed fault patterns that mediate between complex system models and simple detection algorithms. Instead of directly comparing complex models with measured data, the system uses the intermediate fault pattern database to enable automated detection with reduced computational requirements
Data Source
AI summary
A method is for the generation of patterns for identifying faults in power supply systems. In this case, values characterizing a state of the power supply system are used for measurable variables specific to the power supply system for various times in a power supply system and values are determined for the variables specific to the power supply system via a model for the power supply system. In this case, the determination via a model is based on known system-specific input variables and unknown system-specific variables, and the unknown system-specific variables are determined in accordance with a fault-free functioning power supply system. Finally, a pattern for identifying faults is generated by forming the difference between the values characterizing the state and the values determined via the model for the various times.