本論文研究以負載預測為基礎之微電網故障定位，藉由類神經網路技術計算出標的系統每日負載量與發電量再利用故障時上游主電網與微電網內各分散式電源所提供的故障電流造成母線電壓變動量，並結合粒子群演算法進行故障定位，以提高系統整體運轉可靠度。
首先參考原子能委員會核能研究所所建構之微電網系統，應用電力系統模擬軟體ETAP PowerStation建立系統模型、微電網故障定位所需之資料，包含系統結構、負載資料以及發電量等資料；其次，在MATLAB®環境下建立微電網故障相關矩陣，包含系統阻抗矩陣、電壓差矩陣與故障電流矩陣。模擬結果顯示所提方法可完成以負載預測為基礎之故障定位於放射狀微電網系統中。

This thesis aims to study the microgrid fault locating based on load forecasting. The neural network technology is used to calculate the system load and renewable energy of each day. In order to improve the overall operation of the system reliability. This paper used the Particle Swarm Optimization (PSO) algorithm and the bus voltage variation by fault current which is Taiwan Power Company (TPC) and distributed generation provided to locate the fault.
First, simulate the micro-grid system which is constructed by the Institute of Nuclear Energy Research Atomic Energy Council by using ETAP PowerStation software and the information including system structure, history load data and renewable energy data in order to execute fault location. Secondly, using MATLAB® software to build the matrix including system impedance matrix and bus voltage matrix and calculating the fault current when the fault occur. The simulation results by Particle Swarm Optimization show that the proposed method can be used to locate the fault based on load forecasting on the radial micro-grid.

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