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Research on Fault Location Method in Distribution Network with DG Based on PADEA

机译:基于PADEA的DG配电网故障定位方法研究。

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For better solving the problem of multi-terminal fault location in the distribution network, this paper proposes a new fault location method based on Strong Tracking Filter (STF) and Parameter Adaptive Differential Evolution Algorithm (PADEA), which can be applied to asynchronous sampling systems. STF is adopted for real-time fundamental wave amplitudes' extraction of voltage and current. STF can achieve the fast track of power parameters' mutation, and also make the construction of the Distributed Generators' (DG) impedance model more accurate. On the basis of the establishment of impedance model and fault feature analysis, the fault feature value is defined by using only the amplitude of signals measured at the measurement points without introducing the phase angle, which can avoid the introduction of the sampling error radically. PADEA is adopted in the precise fault location part. The use of PADEA can improve the simulation efficiency and result accuracy. Simulation results in MATLAB/Simulink show that the method proposed in this paper has advantages of high accuracy and strong robustness.
机译:为了更好地解决配电网中多端故障的定位问题,提出了一种基于强跟踪滤波器(STF)和参数自适应差分进化算法(PADEA)的故障定位新方法,可以应用于异步采样系统。 。 STF用于实时提取电压和电流的基波幅度。 STF可以快速跟踪功率参数的突变,也可以使分布式发电机(DG)阻抗模型的构建更加准确。在建立阻抗模型和故障特征分析的基础上,仅利用在测量点处测得的信号幅度来定义故障特征值,而无需引入相位角,从而可以从根本上避免采样误差的引入。精确故障定位部分采用了PADEA。使用PADEA可以提高仿真效率和结果准确性。在MATLAB / Simulink中的仿真结果表明,本文提出的方法具有精度高,鲁棒性强的优点。

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