Abstract
This aim of this work was to develop an integrated fault location and restoration approach for microgrids (MGs). The 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 a 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 was utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve fault location and restoration problems in MGs.
Highlights
Microgrids (MGs) can be regarded as a “set of load clusters, distributed generations (DGs), and energy storage systems” [1,2,3]
The bus impedance matrix is convenient to use in pre-fault power flow analysis and the calculation of post-fault bus voltage change caused by the fault current contributed by the upstream power gird and DGs
The multi-objective function, which is composed of load shedding, switch operations, and power loss with voltage drop and ampere capacity constraints, is proposed for service restoration
Summary
Microgrids (MGs) can be regarded as a “set of load clusters, distributed generations (DGs), and energy storage systems” [1,2,3]. A recent, possible solution for rapid and effective restoration is to operate these feeders as MGs because DGs composed of photovoltaics and wind power are interconnected in rural distribution feeders. Gush et al [18] proposed an intelligent fault classification and location identification method for microgrids using discrete orthonormal Stockwell transform (DOST)-based optimized multi-kernel extreme learning machine (MKELM). Wang et al [20] proposed a parallel restoration strategy that considers the characteristics of DGs for MG black starts These state-of-the-art studies presented power restoration approaches in modern active distribution networks and revealed that MGs are distinct from traditional passive radial-type networks. An integrated fault location and restoration approach for MGs is proposed to effectively solve the FDIR problem in MGs. This paper is divided into six sections.
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