Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Particle Swam Optimization Based Fault Location and Restoration Approach in Microgrids

Version 1 : Received: 11 September 2019 / Approved: 12 September 2019 / Online: 12 September 2019 (03:59:27 CEST)

How to cite: Huang, W.; Yao, K.; Wang, F.; Ma, C.; Chen, H.; Hsieh, P. Particle Swam Optimization Based Fault Location and Restoration Approach in Microgrids. Preprints 2019, 2019090127. https://doi.org/10.20944/preprints201909.0127.v1 Huang, W.; Yao, K.; Wang, F.; Ma, C.; Chen, H.; Hsieh, P. Particle Swam Optimization Based Fault Location and Restoration Approach in Microgrids. Preprints 2019, 2019090127. https://doi.org/10.20944/preprints201909.0127.v1

Abstract

This work aims to develop an integrated fault location and restoration approach for microgrids (MGs). This work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations is utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve the fault location and restoration problem in MGs.

Keywords

fault location; service restoration; particle swam optimization; microgrid; power flow; short-circuit fault

Subject

Engineering, Energy and Fuel Technology

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