首页|基于最优特征训练支持向量机的MMC-HVDC输电线路故障检测方法

基于最优特征训练支持向量机的MMC-HVDC输电线路故障检测方法

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准确辨识 MMC-HVDC系统的线路故障类型对于快速恢复故障线路正常运行有着重要的意义,但高阻接地故障一直是识别的难点.为此,提出了一种基于支持向量机的故障类型检测方法.该方法利用经验模态分解提取故障电压信号中的若干个高频模态量,通过粒子群优化算法寻找各个模态量的最优权值后重构波形信号作为特征样本训练分类模型.仿真结果验证,利用优化样本训练出的分类模型可以在低采样频率和较少故障波形采样点的情况下完成对不同类型故障的准确识别.
Method to Detect MMC-HVDC Transmission Line Fault Utilizing Optimal Sample-trained Support Vector Machine
Accurately identifying line fault types in MMC-HVDC systems is of great significance for quickly restoring the normal operation of faulted lines,but high-resistance grounding faults have always been difficult to identify.In this paper,a fault type detection method based on support vector machine is proposed.The method uses empirical modal decomposi-tion to extract a number of high-frequency modal quantities in the fault voltage signal,and uses the particle swarm optimi-sation algorithm to find the optimal weights of each modal quantity and then reconstructs the waveform signal as the opti-mised feature quantity to train the classification model.Simulation results verify that the classification model trained with the optimised samples can accurately identify different types of faults with low sampling frequency and fewer fault wave-form sampling points.

DC transmissionhigh-resistance ground faultempirical mode decompositionsupport vector machinepar-ticle swarm optimisation

肖思捷、于琼、房悦、黄文龙、刘科、鲁成、牟建学

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国网山东省电力公司淄博供电公司,山东 淄博 255000

直流输电 高阻接地故障 经验模态分解 支持向量机 粒子群优化

2024

电工技术
重庆西南信息有限公司(原科技部西南信息中心)

电工技术

影响因子:0.177
ISSN:1002-1388
年,卷(期):2024.(19)