Three-Phase Asset Fault Detection Using Sequence Error Metrics
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Solution Overview
Problem
Existing systems fail to effectively detect early signs of failure conditions in three-phase electrical assets, such as transformers, leading to potential damage and unplanned outages.
Innovation Solution
A monitoring system that analyzes electrical power flow data from three-phase assets to determine performance metrics and error metrics, using filters and transformations to identify developing fault conditions by comparing these metrics to pre-determined thresholds, and issuing maintenance alerts when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used for three-phase electrical assets, then the system structure remains simple, but the ability to detect developing fault conditions is insufficient leading to unplanned outages
Solution Approach 1:
The patent segments the fault detection process into multiple independent analysis components: negative sequence current analysis, d-axis component analysis, q-axis component analysis, and differential current analysis. Each component processes specific aspects of the three-phase electrical data independently and contributes to the overall fault detection decision, allowing the complex monitoring task to be divided into manageable segments that can be implemented and maintained separately
Solution Approach 2:
The system performs preliminary analysis of electrical parameters by continuously calculating performance metrics, error metrics, and comparing them against thresholds before actual fault conditions develop. The monitoring module proactively identifies developing fault conditions by analyzing trends in negative sequence currents and differential components, enabling preventive maintenance before complete asset failure occurs
2Measurement precision
If comprehensive performance metrics are calculated to improve detection accuracy, then the reliability of fault detection increases, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies different analysis methods to different aspects of the electrical data based on their specific diagnostic value. Negative sequence current analysis is applied specifically to detect asymmetrical faults, while d-axis and q-axis component analysis are used for different types of anomalies. This localized application of specialized analysis techniques optimizes detection accuracy for specific fault types without unnecessarily complicating the overall system with universal but less effective methods
Solution Approach 2:
The system calculates multiple performance metrics including negative sequence current, d-axis component, q-axis component, and differential current, but only uses the most significant ones for fault detection decisions. The monitoring module compares error metrics against pre-determined thresholds and can identify developing faults using subsets of these metrics, allowing the system to maintain high detection accuracy while avoiding unnecessary computational overhead from analyzing all possible parameters equally
Data Source
AI summary
A system includes a monitoring module configured to: receive a first indication related to a three-phase input of a three-phase AC electrical asset; receive a second indication related to a three-phase output of the three-phase AC electrical asset; determine performance metrics for the three-phase AC electrical asset based on the first indication and the second indication; determine error metrics by comparing each determined performance metrics to a pre-determined estimated performance metric; compare each of the error metrics to one of a plurality of pre-determined thresholds, the thresholds being determined when the three-phase AC electrical asset is in a healthy condition; and determine whether a developing fault condition exists in the three-phase AC electrical asset based on the comparison.