Transformer Voiceprint Fault Detection for Fast, Noise-Robust Diagnosis
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Solution Overview
Problem
Traditional transformer fault diagnosis methods are time-consuming and require significant manpower, often failing to accurately diagnose fault types and locations, posing risks to power system stability and safety.
Innovation Solution
A method involving wavelet packet analysis, feature extraction, and a backpropagation algorithm is used to train a transformer fault detection model, preprocessing voiceprint signals to reduce noise, and iteratively adjusting model weights for accurate fault detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrical testing and oil sample analysis methods are used for transformer fault diagnosis, then comprehensive fault information can be obtained, but the diagnosis process requires significant time and manpower
Solution Approach 1:
The patent replaces traditional manual electrical testing and oil sample analysis with an acoustic detection system using microphones and signal processing algorithms. The voiceprint recognition model automatically analyzes transformer sounds to identify faults, substituting mechanical/manual inspection methods with automated acoustic sensing and digital signal processing.
Solution Approach 2:
The patent creates a digital voiceprint copy of the transformer's acoustic signature and compares it against a database of known fault patterns. By copying and analyzing acoustic characteristics rather than physically testing electrical parameters or analyzing oil samples, the system achieves rapid fault diagnosis without direct physical intervention.
2Ease of operation
If traditional fault diagnosis methods are used, then manual inspection can be performed, but accurate diagnosis of fault type and location becomes difficult
Solution Approach 1:
The patent transforms the fault diagnosis approach by changing from electrical parameter measurement to acoustic parameter analysis. The system captures sound frequency, amplitude, and temporal characteristics, then uses machine learning models to map these acoustic parameters to specific fault types and locations, improving both accuracy and operational simplicity.
Solution Approach 2:
The patent introduces a voiceprint recognition model as an intermediary between the transformer's acoustic emissions and the fault diagnosis result. This intermediary automatically processes raw audio signals, extracts features, and classifies faults, eliminating the need for manual interpretation while improving diagnostic accuracy.
3Productivity
If acoustic signals are used for fault detection, then real-time monitoring is possible, but environmental noise may interfere with detection accuracy
Solution Approach 1:
The patent converts environmental noise from a harmful interference into a useful diagnostic feature. The voiceprint recognition model is trained to distinguish between useful transformer acoustic emissions and background noise, and in some cases uses noise characteristics to identify specific fault conditions. The system learns to filter and interpret signals in noisy environments, turning the challenge of environmental noise into an advantage for comprehensive fault detection.
Data Source
AI summary
Provided are a method for training a transformer fault detection model, a fault diagnosis method, and a related device. The method includes: obtaining an initial voiceprint signal of a transformer and a fault type corresponding to the initial voiceprint signal; preprocessing the initial voiceprint signal to obtain an input signal, and establishing an input signal dataset; performing feature extraction on a first input signal in the training dataset based on a preset feature extraction algorithm to obtain a first voiceprint feature; training an initial detection model based on the first voiceprint feature and a first fault type corresponding to the first input signal to obtain a first training result; determining a loss function based on the first training result and the first fault type; and iteratively adjusting a weight value of the initial detection model until the loss function converges to obtain a fault detection model.