Processing of n-q-phi and dltU for Partial Discharge Analysis

Partial discharge detection serves as a critical method for assessing the insulation condition of electrical equipment, where n-q-phi (discharge count-discharge magnitude-phase) and dltU (voltage variation) represent two key characteristic parameter processing techniques. n-q-phi analysis establishes a three-dimensional pattern model by statistically analyzing the phase distribution of discharge pulses within power frequency cycles, the relationship between discharge magnitude and discharge count. This processing method effectively distinguishes different discharge patterns such as corona discharge, internal discharge, and surface discharge. Typical implementation involves phase window segmentation, pulse clustering, and statistical histogram generation algorithms. Code implementation generally includes phase-domain binning using fixed intervals (e.g., 360°/1024 bins), DBSCAN clustering for pulse grouping, and 3D visualization with libraries like Matplotlib or Plotly. dltU processing focuses on transient voltage fluctuation characteristics caused by discharges. By capturing nanosecond-level voltage waveform changes combined with wavelet transform or time-frequency analysis techniques, it extracts subtle features related to insulation defects. The main challenges include noise suppression and effective signal extraction. Implementation typically employs discrete wavelet transform (DWT) with Daubechies wavelets for multi-resolution analysis, accompanied by digital filters (Butterworth/Chebyshev) for noise reduction. Other practical utilities commonly include: - Noise suppression algorithms (e.g., adaptive filtering using LMS or RLS methods) - Discharge pattern recognition (machine learning-based with SVM or CNN classifiers) - 3D pattern visualization tools (WebGL-based interactive displays) - Discharge trend prediction modules (time-series analysis with ARIMA or LSTM networks) - Multi-sensor data fusion processing (Kalman filtering or Bayesian inference) These processing methods collectively form a complete partial discharge monitoring system, providing technical support for fault预警 in high-voltage equipment like transformers and GIS. Modern systems increasingly incorporate edge computing architectures to enable real-time online analysis, often implemented through containerized microservices (Docker/Kubernetes) with message queue systems (MQTT/Kafka) for distributed data processing.