Abstract
This paper proposes statistical feature extraction methods combined with artificial intelligence (AI) approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF) detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM) techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP) in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.
Highlights
IntroductionSupervisory control and data acquisition (SCADA) has been the traditional load monitoring method for several years
Thebyaccuracy rateElectromagnetic of the proposed. These results show thatthe the Electromagnetic proposed methods can analyze the methods is tested in a(EMTP)
This paper has presented statistical feature extraction methods combined with support vector machine (SVM) to enhance the recognition accuracy of fault locations and faulty phase detection in low voltage distribution
Summary
Supervisory control and data acquisition (SCADA) has been the traditional load monitoring method for several years. In this method, sensors are installed at each load point to detect the actions of switches or breakers. Sensors deliver messages to load recorders and the data center. As power systems have become more complex, this approach incurs in significant time delays and cost for installation and maintenance. The increase in the number of sensors will complicate the system and reduce the reliability
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