An artificial intelligence identification method for the external fault of the commutation bus of the hvdc system
A technology for commutating busbars and external faults, applied in measurement devices, instruments, biological neural network models, etc., can solve the problems of stable operation of AC and DC systems, system operation collapse, hazards, etc., achieves good model classification capabilities, and is easy to implement , the effect of high reliability
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[0035] combine figure 1 , which illustrates the flow chart of the artificial intelligence identification method for the external faults of the HVDC system converter bus. identify.
[0036] 1. The structure of BP neural network
[0037] 1. Select the fault signal
[0038] When the HVDC AC system fails, electrical quantities such as commutation voltage, DC voltage, DC current and firing angle will all change suddenly. In this embodiment, the DC voltage U on the inverter side is selected d as a fault signal. Select respectively the DC voltage signal U d , as training samples for the neural network. Among them, the different operating modes of the system mainly include: A, the bipolar wiring operation mode of the system; B, the single pole ground circuit operation mode of the system; C, the full voltage operation mode; D, the step-down operation mode (20%, 30% step-down) ; E, bipolar unbalanced operation mode. N1, F1~F4 faults mainly refer to: N1-normal state; F1-single-ph...
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