一种改进的混合三端输电线路故障定位算法研究

针对混合输电线路故障多、定位精度差等问题，提出一种基于改进的自适应噪声完备集合经验模态分解(CEEMDAN)和Teager-Kaiser能量算子相结合的混合三端输电线路故障测距方法。首先对输电线路故障电压信号使用新型相模变换进行解耦，以消除线路间的电磁耦合现象；然后利用改进的CEEMDAN和Teager-Kaiser能量算子对解耦后的故障电压信号进行分解，提取了故障初始行波到达检测点的时间，再根据故障时暂态电压行波零模分量和线模分量的故障特性，提出一种基于零模和线模时间差的故障分支判别方法；最后为解决无法得到行波波速精确值的问题，采用基于零模线模时间差的行波测距方法得到了精确的故障定位数据。基于仿真模型，对比改进CEEMDAN算法和传统CEEMDAN算法，验证所提改进算法的有效性。仿真结果表明，所提算法不受故障类型、过渡电阻、波速、时间不同步的影响，具有较高的测量精度。改进CEEMDAN算法能够有效改善故障信号经传统CEEMDAN算法分解后带来的随机性问题，具有较高的工程应用价值。

Research on an Improved Fault Location Algorithm for Hybrid Three-terminal Transmission Line

Aiming at the problems of multiple faults and poor location accuracy of hybrid transmission lines a fault location method based on improved complete ensemble empirical mode decomposition with adaptive noise CEEMDAN and Teager-Kaiser energy operator was proposed. Firstly the fault voltage signals of transmission lines were decoupled by a new phase-mode transformation to eliminate the electromagnetic coupling phenomenon between lines. Then the decoupled fault voltage signal was decomposed by using the improved CEEMDAN and Teager-Kaiser energy operator and the time when the initial traveling wave of the fault reached the detection point was extracted. According to the fault characteristics of the zero-mode and line-mode components of the transient voltage traveling wave a fault branch discrimination method based on the time difference between zero-mode and line-mode was proposed. Finally in order to solve the problem that the accurate value of the traveling wave velocity can not be obtained the accurate fault location data were obtained by using the traveling wave ranging method based on the time difference between zero-mode and line-mode. Based on the simulation model the improved CEEMDAN algorithm was compared with the traditional CEEMDAN algorithm and the effectiveness of the proposed improved algorithm was verified. The simulation results show that the proposed algorithm is not affected by fault type transition resistance wave velocity and time synchronization and has a high measurement accuracy. The improved CEEMDAN algorithm can effectively improve the randomness problem caused by the decomposition of fault signals by the traditional CEEMDAN algorithm which has high engineering application value.