Detection and localization of faults in smart hybrid distributed generation systems: A Stockwell transform and artificial neural network-based approach

Abstract

Summary The paper presents a Stockwell transform (ST) and artificial neural network‐based approach for the detection and localization of faults in distribution systems considering the complexities of network architecture and the distributed generation (DG) integration. Firstly, a faulty‐line detection technique is developed based upon the total harmonic distortion of the fault current signal captured from the line ends. Then, the ST coefficients of the faulty signal are used as an attribute to classify the fault. Finally, the root‐mean‐square values calculated from the ST coefficients are used for the fault localization. The algorithm is tested on an IEEE 13‐bus unbalanced and a 52‐bus practical Indian balanced distribution system with DG. The results are compared with some existing algorithms. The comparisons and the test results show that the proposed technique can be a useful tool in detecting the faults in complicated fault scenarios.

Comparison of different regression models to estimate fault location on hybrid power systems

“…In [24], the authors presented a Stockwell transform and ANNbased approach for the detection and location of faults on distribution systems connected with hybrid power plants. Another ANN-based fault location estimation technique for unbalanced distribution feeder with distributed generation was also proposed by Dehghani et al [25].…”

Research on Distributed Power Distribution Fault Detection Based on Edge Computing

“…The overall structure of the distributed power distribution fault handling system based on edge computing is shown in Figure 2. The system consists of the sensing layer, the network layer, the platform layer and the application layer, and has typical ubiquitous power Internet of Things structure features [11]- [13].…”

A Relaying Coordination for Distribution Network Protection

“…The presented approach is developed using discrete wavelet transform (DWT) and artificial neural network (ANN). In [2], a fault diagnosis and fault location technique is developed using ANN-Stockwell transform in smart hybrid distribution generation systems. Authors in [3], discussed a protection system for high-impedance faults detection in power systems.…”