Single-Phase Fault Analysis in Distributed Distribution Networks

Single-phase fault analysis in distributed distribution networks is a critical component of power system fault diagnosis, particularly as distributed generation integration continues to increase, altering the fault characteristics of traditional distribution networks. Single-phase grounding faults represent the most common fault type in distribution networks, with analysis primarily focusing on detecting and evaluating zero-sequence current variations.

In distributed distribution networks, the integration of distributed energy resources (such as photovoltaic and wind power systems) may cause fault current distribution and magnitude to differ from traditional radial distribution networks. Zero-sequence current serves as a key indicator for identifying single-phase grounding faults, as it exhibits significant amplification near the fault location during fault conditions. By monitoring zero-sequence currents across various feeder lines, fault locations can be pinpointed and impact areas analyzed. Implementation typically involves current transformer data acquisition and real-time zero-sequence calculation algorithms using symmetrical component transformation methods.

Analysis must account for the fault contribution capability of distributed generators, where different types (inverter-based or rotating-machine-based) may contribute differently to zero-sequence currents. Furthermore, neutral grounding methods (such as isolated neutral, Petersen coil grounding, or low-resistance grounding) significantly influence zero-sequence current distribution characteristics. Code implementations often incorporate fault type classification modules that adapt to different grounding configurations through parameterized settings in protective relay algorithms.

Through simulation or field measurement data, zero-sequence current magnitude and phase characteristics can be modeled. Integrating fault recorder waveforms and protective relay information enhances fault location accuracy. This analysis not only facilitates rapid fault isolation but also provides critical references for distribution network planning and protection coordination. Typical implementations include waveform analysis libraries using Fast Fourier Transform (FFT) for harmonic analysis and machine learning algorithms for pattern recognition in fault classification systems.