Defect Type Identification in Equipment Using Fuzzy C-Means Clustering Algorithm

Analysis of SF6 Equipment Fault Characteristics Using Fuzzy C-Means Clustering Algorithm

In the field of high-voltage electrical equipment monitoring, accurate defect identification in SF6 insulation equipment directly impacts grid security operations. Traditional threshold-based judgment methods struggle with complex working conditions involving multi-feature coupling. The introduction of Fuzzy C-Means (FCM) clustering enables the establishment of more realistic defect classification models that better reflect actual operational scenarios.

The core implementation steps involve: First, collecting partial discharge signals from typical equipment defects and extracting statistical features from Phase-Resolved Partial Discharge (PRPD) patterns as clustering dimensions. The algorithm implementation typically requires preprocessing sensor data through feature extraction functions like statistical moment calculations. Subsequently, membership functions quantify the association degree between each sample and cluster centers, providing better representation of transitional fault states compared to hard clustering methods. During iteration, cluster center positions are dynamically adjusted using update equations that minimize the objective function, ultimately outputting fuzzy classification results for defect types. The iterative process can be implemented with a while-loop structure that checks for convergence based on center movement thresholds.

The advantage of this method lies in allowing samples to belong to multiple defect categories with different probabilities, making it particularly suitable for identifying latent faults that haven't fully developed. In practical applications, careful attention must be paid to feature dimension selection and optimization of the fuzziness exponent 'm' to avoid cluster boundary overlap or excessive sensitivity. Code implementation should include parameter tuning routines that test different 'm' values (typically between 1.5-2.5) using validity indices like partition coefficient. Engineering cases demonstrate approximately 18% improvement in identification accuracy for discharge-type defects compared to traditional methods, with effective differentiation between easily confused faults such as internal floating discharges and insulator surface creepage. The algorithm's classification performance can be validated through confusion matrix analysis and cross-validation techniques.