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Single phase fault diagnosis and location in active distribution network using synchronized voltage measurement

机译:使用同步电压测量的有源配电网中的单相故障诊断和定位

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To ensure the safe operation of the active distribution network (ADN), accurate fault diagnosis and location are crucial to improve the reliability indices and reduce the outage time. This paper proposes a characteristic model-based method for the single phase to earth fault in the ADN system. Firstly, the characteristic model of the fault factor extracts the phasor distribution characteristics of the voltage and current along the distribution feeder line and estimates the current contribution of DG units to the fault point. Based on the minimum entropy theory, the solution of nonlinear characteristic model is transformed to a single-objective optimization of the characteristic entropy and the diagnosis criteria is formulated. Then, the two-stage fault location scheme for single phase fault is proposed. The fault diagnosis stage estimates the suspicious fault section to reduce the search area and the fault location stage locates the exact fault distance. The Fibonacci search algorithm is utilized for fault location to the optimize iterative and minimize the respond time. The proposed scheme is general for all DG types and overcomes the requirement of its individual model parameters. The proposed method is validated in the IEEE 34 bus distribution test system using the phasor measurement unit (PMU). Test results of the model-based diagnosis and location method can reveal accurate fault location and rapid response at different fault impedance and small time delay comparing with the radial basis function neural network (RBF), and wavelet neural network (WNN) method.
机译:为了确保有源配电网(ADN)的安全运行,准确的故障诊断和定位对于提高可靠性指标和减少中断时间至关重要。本文针对ADN系统中的单相接地故障提出了一种基于特征模型的方法。首先,故障因子的特征模型提取沿配电馈线的电压和电流的相量分布特征,并估算DG单元对故障点的电流贡献。基于最小熵理论,将非线性特征模型的解转化为特征熵的单目标优化,并提出了诊断标准。然后,提出了单相故障的两阶段故障定位方案。故障诊断阶段估计可疑故障区域以减少搜索区域,而故障定位阶段确定确切的故障距离。 Fibonacci搜索算法用于故障定位,以优化迭代并最小化响应时间。所提出的方案对于所有DG类型都是通用的,并且克服了其单独模型参数的要求。所提出的方法在使用相量测量单元(PMU)的IEEE 34总线分配测试系统中得到了验证。与径向基函数神经网络(RBF)和小波神经网络(WNN)方法相比,基于模型的诊断和定位方法的测试结果可以显示出在不同故障阻抗和较小时延下的准确故障定位和快速响应。

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