A Fault Section Identification Method for Cable-Wire Hybrid Transmission Lines Based on Cluster Analysis of Principal Components of Current Transients
A transmission line, cluster analysis technology, applied in the direction of the fault location, etc., can solve the problems of complex traveling wave waveform, discontinuous wave impedance, increasing difficulty of traveling wave head, etc., and achieve the effect of simple principle and high precision
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Embodiment 1
[0027] Example 1: 220kV line with static synchronous series compensator such as figure 1 shown. Its line parameters are as follows: cable section MJ is 10km long, and overhead line section JN is 25km long. Fault location: traverse every 1km in the cable section, the initial fault angle is -90°, the transition resistance is 20Ω, and the sampling rate is 1MHz.
[0028] (1) Construct the principal component clustering analysis space according to steps 1 to 3 in the instruction manual. The M terminal obtains the principal component clustering analysis space formed by clustering the fault current sample data with initial fault angles of 90° and -90° respectively, such as 3 shown;
[0029] (2) Put the fault sample into the principal component clustering space that responds to the initial fault angle according to step 4 in the specification, and obtain its clustering space in the first principal component (PC 1 ) projection value q on the axis 1 , for its graphical representation...
Embodiment 2
[0031] Example 2: 220kV line with static synchronous series compensator such as figure 1 shown. Its line parameters are as follows: cable section MJ is 10km long, and overhead line section JN is 25km long. Fault location: traverse every 1km in the cable section, the initial fault angle is 90°, the transition resistance is 50Ω, and the sampling rate is 1MHz.
[0032] (1) Construct the principal component clustering analysis space according to steps 1 to 3 in the instruction manual. The M terminal obtains the principal component clustering analysis space formed by clustering the fault current sample data with initial fault angles of 90° and -90° respectively, such as 3 shown;
[0033] (2) Put the fault sample into the principal component clustering space that responds to the initial fault angle according to step 4 in the specification, and obtain its clustering space in the first principal component (PC 1 ) projection value q on the axis 1 , for its graphical representation ...
Embodiment 3
[0035] Example 3: 220kV line with static synchronous series compensator such as figure 1 shown. Its line parameters are as follows: cable section MJ is 10km long, and overhead line section JN is 25km long. Fault location: traverse every 1km on the overhead line section, the initial fault angle is 30°, the transition resistance is 50Ω, and the sampling rate is 1MHz.
[0036] (1) Construct the principal component clustering analysis space according to steps 1 to 3 in the instruction manual. The M terminal obtains the principal component clustering analysis space formed by clustering the fault current sample data with initial fault angles of 90° and -90° respectively, such as 3 shown;
[0037] (2) Put the fault sample into the principal component clustering space that responds to the initial fault angle according to step 4 in the specification, and obtain its clustering space in the first principal component (PC1 ) projection value q on the axis 1 , for its graphical represent...
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