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基于电流积分与时序卷积网络-支持向量机的直流配电网故障定位OA

Fault location of DC distribution network based on current integration and temporal convolutional network-support vector machine

中文摘要英文摘要

本文提出一种结合电流积分变化趋势和时序卷积网络(TCN)-支持向量机(SVM)的直流配电网故障定位方法,以区分故障类型并实现直流配电网故障准确定位,为实现直流配电网保护奠定基础.首先计算故障电流的积分序列,并用变分模态分解(VMD)算法分解积分序列,将分解所得高频固有模态函数的特征量作为 TCN与 SVM组合模型的输入特征向量,实现故障线路定位和故障类型判定.仿真结果表明,该方法能快速定位故障线路,准确识别不同故障,并且有较好的适应性和具备一定的抗干扰能力.

This paper proposes a DC distribution network fault location method combining current integral variation trend and temporal convolutional network(TCN)-support vector machine(SVM),to distinguish and locate DC distribution network faults,and lay the foundation for DC distribution network protection.Firstly,the integral sequence of fault current is calculated,and the integral sequence is decomposed by variational mode decomposition(VMD)algorithm.The eigenvalues of the decomposed high frequency intrinsic mode function are used as the input eigenvectors of the combination model of TCN and SVM,and the fault lines are located and the fault types are determined.The simulation results show that the scheme can not only locate the fault line quickly and identify different faults accurately,but also has good adaptability and certain anti-interference ability.

祝光思涵;洪翠

福州大学电气工程与自动化学院,福州 350108福州大学电气工程与自动化学院,福州 350108

直流配电网故障定位电流积分趋势变分模态分解(VMD)时序卷积网络(TCN)

DC distribution network fault locationcurrent integration trendvariational mode decomposition(VMD)temporal convolutional network(TCN)

《电气技术》 2025 (2)

1-13,13

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