Abstract

Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section due to the high-fault current. The effectiveness of the proposed method was investigated on an IEEE 11-node test feeder in MATLAB SIMULINK 2020b, under several situations, such as different fault resistances, distances, inception angles, and types. In some cases, the algorithm found two or three candidates for FL. In these cases, the section estimation helped to identify the real fault among all candidates. Section estimation method performs well for all simulated cases. The results showed that the proposed method was accurate and was able to precisely detect the real faulty section. To experimentally evaluate the proposed method’s powerfulness, a laboratory test and its simulation were carried out. The algorithm was precisely able to distinguish the real faulty section among all candidates in the experiment. The results revealed the robustness and effectiveness of the proposed method.

Highlights

  • Clinical-Laboratory Center of Power System & Protection, Faculty of Intelligent Systems Engineering and Center for Energy Informatics, University of Southern Denmark, DK-5230 Odense, Denmark

  • The results showed that the proposed method was accurate and was able to precisely detect the real faulty section

  • The results revealed that extreme learning machine (ELM) operates better in terms of performance and training time

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Summary

The Proposed Methodology

We present the proposed FL method in three subsections. FL equations for both grounded and non-grounded faults are presented. A subsection describes the calculation of ELI at the end of a section. A new robust and costeffective method is presented for determining the real faulty section among all candidate sections

Fault Location Method
Equivalent Load Impedance Determination
Results
Different Fault Distances
Different Fault Resistances
Different Fault Inception Angles
Different Fault Types
Laboratory Single Phase Experiment
Laboratory and simulation of single-phase to ground
Conclusions
Full Text
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