微型电脑应用

为了进一步促进配电网的高效、稳健运行，提出一种基于智能电表的配电网高阻抗故障的检测与定位方法。构建配电网高阻抗故障参数驱动模型，结合阻尼能量特征分析方法重建配电网高阻抗故障的数据，并分析故障分布特征量。采用智能电表进行配电网高阻抗的直流调制参数，通过直流额定有功功率补偿方法进行配电网高阻抗故障特征提取。结合机器学习算法对提取的配电网高阻抗故障特征量进行自适应学习的特征分类，在智能电表中实现配电网高阻抗故障特征融合和属性分类识别。最后通过优化的学习算法和故障特征聚类分析算法实现配电网高阻抗故障的优化检测和智能定位。仿真结果表明该方法对配电网高阻抗故障检测定位的精度较高，证明了该方法的有效性。

In order to further promote the efficient and steady operation of distribution network, this paper proposes a detection and location method of high impedance fault of distribution network based on smart electricity meter. A distribution network high impedance fault parameter driving model is constructed. Combined with the damping energy characteristic analysis method, the data of distribution network high impedance fault are reconstructed, and the characteristic quantity of fault distribution is analyzed. DC modulation parameters of high impedance of distribution network are carried out by intelligent electricity meters, and fault characteristics of high impedance of distribution network are extracted by DC rated active power compensation method. Combined with the machine learning algorithm, the high impedance fault features extracted from the distribution network are classified by adaptive learning, and the high impedance fault features fusion and attribute classification recognition are realized in the smart electricity meter. Then the optimized detection and intelligent location of high impedance faults in distribution network are realized by the optimized learning algorithm and fault feature clustering analysis algorithm. The simulation results show that this method has high accuracy in fault detection and location of high impedance in distribution network, which proves the effectiveness of this method.