高压直流输电系统在我国发展迅速，并在西电东送工程和全国联网中起着重要作用。由于高压直流输电线路距离长、跨越的地形复杂、气候条件多变，不易排查故障点，因此开展高压直流输电线路故障测距的研究，对于可靠、准确地故障测距，及时修复故障线路，减小停电损失，保证供电可靠性和提高电力系统的安全稳定运行具有十分重要的意义。

首先，对于线路衰减系数难以求取和二次波头捕捉困难的问题，本文选择用首波头幅值比特征量和故障距离之间的非线性关系作为测距的依据。根据故障点产生的高频故障暂态信号沿线路向两端传播的过程中会发生衰减这一特性，探讨了故障电压线模分量两端首波头幅值比与故障距离的关系。依据故障电压线模分量和零模分量衰减程度不同的特性，分析了线路一端线模量和零模量首波头幅值比与故障距离的关系。鉴于最小二乘支持向量机（Least Squares Support Vector Machines，LSSVM）具有良好的非线性逼近拟合能力，利用LSSVM模型对故障特征量与故障距离之间的非线性关系进行泛化，进而实现故障测距。

其次，针对LSSVM的学习性能受惩罚系数和核参数影响的问题，提出了一种改进的鹈鹕优化算法（Improved Pelican Optimization Algorithm，IPOA），对 LSSVM的关键参数进行寻优。引入Tent混沌映射和精英反向学习使初始种群变得更加均匀，引入差分进化算法增强全局寻优能力，并采用典型的测试函数对改进鹈鹕算法进行性能测试。结果表明，IPOA算法的收敛性能得到有效提高。

最后，为验证基于IPOA- LSSVM模型的故障测距性能，在PSCAD/EMTDC平台搭建了高压直流输电线路仿真模型。对仿真模型设置故障点，提取故障电压，对故障电压模分量进行不同尺度下的小波分解得到不同频率下的首波头幅值比作为故障输入量，故障距离作为输出量进行仿真验证。仿真结果表明，该模型对于双端数据量测距和单端数据量测距具有较高的精度，且不受故障电阻的影响，具有较好的抗噪性能。

The west-east power transmission project and national networking both benefit from the fast development of HVDC transmission systems in our nation. It is challenging to patrol and troubleshoot when a defect arises because of the enormous distance of HVDC transmission lines, the complicated terrain across them, and the unpredictable environment. As a result, research on HVDC transmission line fault location is crucial for a number of reasons, including precise and dependable fault location, prompt fault line repair, decreased power loss, guaranteed power supply reliability, and enhanced power system stability and safety.

First, as the attenuation coefficient of the line is imprecise and it is challenging to capture the second wave head, the nonlinear relationship between the amplitude ratio characteristic of the head and the fault distance is selected as the foundation for distance measurement. The characteristic that the high-frequency fault transient signal generated at the fault point will attenuate when propagating along the route to both ends is used to discuss the relationship between the amplitude ratio of the head at both ends of the fault mode component and the fault distance. The relationship between the amplitude ratio of the terminal line modulus and the zero modulus head and the fault distance is examined based on the varied attenuation degree of the fault voltage line mode component and the zero mode component. The nonlinear relationship between fault characteristics and fault distance is generalized by using the LSSVM model, and fault localization is realized because of the good nonlinear approximation fitting ability of LSSVM.

Second, in light of the impact of the kernel parameters and penalty coefficient on LSSVM learning performance, the Improved Pelican Optimization Algorithm (IPOA) was put forth to optimize the LSSVM's critical parameters. The differential evolution method is introduced to improve the global optimization capability, tent chaotic mapping and elite reverse learning are used to make the initial population more uniform, and standard test functions are used to evaluate the effectiveness of the modified Pelican algorithm. The outcomes demonstrate that the IPOA algorithm's convergence performance has been successfully enhanced.

Lastly, an HVDC transmission line simulation model is constructed using the PSCAD/EMTDC platform in order to validate the fault location performance based on the IPOA-LSSVM model. The simulation model's fault point is set, the fault voltage is extracted, and the fault voltage module's wavelet transform is applied at various scales to obtain the amplitude ratio of the first wave head at various frequencies as the fault input. The simulation is then verified using the fault distance as the output. According to the simulation results, fault resistance has no effect on the model's excellent accuracy for both single- and double-ended data range. Furthermore, it has good anti-noise performance.

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