Stator Single-Phase-to-Ground Fault Protection for Bus-connected Powerformers Based on Hierarchical Clustering Algorithm

Abstract

The traditional stator single-phase-to-ground fault protective schemes are hard to completely satisfy the requirement of selectivity for bus-connected Powerformers. In order to overcome the aforementioned problem, a new stator single-phase-toground fault protective scheme based on the hierarchical clustering algorithm is proposed in this paper. The direction and magnitude of zero-sequence current and leakage current are discussed and selected as feature vectors. Then, the data set are divided into two groups by hierarchical clustering algorithm, and cluster center of each group is calculated. Finally, the space relative distance among detected pattern and two cluster centers is calculated to discriminate the faulty Powerformer. The fundamental component and the third harmonic component of the data set are combined to realize 100% coverage of fault detection for the stator windings of Powerformer. Simulation results have shown that, under different fault conditions, the new scheme can distinguish internal faults from external faults reliably, and can detect in which machine a stator single-phase-to-ground fault is occurring even if the fault resistance is at 5 kΩ.

Simulation and Experiment Analysis of 10 kV Flexible Grounding Device

“…According to Formula (4), the current I i is irrelevant to fault transition resistance R f [25][26][27]. The injected current can fully compensate the active and reactive components of the fault current.…”

m‐ISODATA : Unsupervised clustering algorithm to capture representative scenarios in power systems

“…The application of hierarchical-based clustering algorithms in power system problems has been proposed to extract consumption patterns for photovoltaic generation, 22 evaluate wind turbine allocation, 23 identify single-phase fault patterns in protection schemes, 24 and capture load demand scenarios for distribution system expansion planning. 25 Merrick 4 performed an analysis to find the number of scenarios needed to represent sources of variability in generation expansion planning (GEP) problem, by applying HAC to historical series related to three sources of variability: load level, photovoltaic, and wind power dispatch.…”

“…The application of hierarchical‐based clustering algorithms in power system problems has been proposed to extract consumption patterns for photovoltaic generation, 22 evaluate wind turbine allocation, 23 identify single‐phase fault patterns in protection schemes, 24 and capture load demand scenarios for distribution system expansion planning 25 …”

Stator Single-Line-to-Ground Fault Protection for Bus-Connected Powerformers Based on S-Transform and Bagging Ensemble Learning

“…Step3: The zero-sequence current in the first quarter of the power frequency cycle (T 1 /4) after the fault is decomposed by ST. And all the amplitude-time-frequency elements are calculated according to (6) and (7) to form the amplitude-time-frequency matrix A. Then the ST transient energy e, the comprehensive correlation coefficient ρ and the zero-sequence active power λ are calculated according to (9), (11) and (13), respectively.…”

“…However, these independent protection schemes use only one fault feature as a criterion, and their reliability is low. On the basis of [6], in order to fuse as much fault features as possible to improve the reliability of the protection scheme, [7] and [8] further proposed protection schemes of the stator SLG fault for Powerformer based on hierarchical clustering and fuzzy clustering, respectively. The magnitude and direction of the zero-sequence current, the magnitude and direction of the leakage current are extracted as fault features and are used as inputs to the classification model.…”