A Fault Line Selection Method of Distribution Network Based on Time-Frequency Eigenvector of Transient Zero-Sequence Current
A distribution network fault, time-frequency characteristic technology, applied in the direction of the fault location, can solve the difficult problem of line selection, small high-frequency components, random and other problems
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Embodiment 1
[0036] Example 1: The single-phase ground fault simulation model of 110kV / 35kV distribution network is as follows figure 1 As shown, it has 6 feeders, and the neutral point of the Z-shaped transformer is grounded through the series resistance of the arc suppression coil. Overhead feeder L 1 =15km,L 3 =18km,L5 = 30km, wire-cable hybrid feeder L 4 =17km, the overhead feeder is 12km, the cable is 5km, and the cable feeder is L 2 = 6km, L 6 = 8km. Among them, the overhead feeder is JS1 pole type, LGJ-70 type conductor, the span is 80m, and the cable feeder is YJV23-35 / 95 type cable. G in the power grid is an infinite power source; T is the main transformer, the transformation ratio is 110kV / 35kV, and the connection group is Y N / d11;T Z Is a zigzag transformer; L is the arc suppression coil; R is the damping resistance of the arc suppression coil. The feeder adopts three types of lines: overhead line, overhead line-cable hybrid line and cable line. The load uses a constan...
Embodiment 2
[0044] Example 2: Using the model in Example 1, the distance from the feeder L 2 An AG single-phase ground fault occurs 3 kilometers from the beginning, the ground resistance is 50Ω, the initial phase angle of the fault is 90°, and the sampling frequency is 10kHz. Faulty feeder L 2 And sound feeder L 4 The zero-sequence current waveform of Figure 4 shown.
[0045] Using the same method as in Example 1, select the transient zero-sequence current data 5 ms after the fault and use db10 to decompose and reconstruct the three-layer wavelet packet to obtain the time-frequency eigenvector L of each feeder TF and time-frequency feature similarity matrix P xy , forming a comprehensive correlation coefficient matrix P ij :
[0046] P ij = 1 0.0014 0.2585 0.0222 ...
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