Power system transient voltage stability assessment method based on convolution neural network CNN
A technology of transient voltage stabilization and convolutional neural network, which is applied in the direction of neural learning methods, biological neural network models, neural architectures, etc., can solve problems that cannot satisfy accuracy and rapidity at the same time, and achieve short test time and high accuracy Efficiency, fast and accurate judgment
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[0042] In conjunction with the accompanying drawings, the technical solutions involved in the present invention are further explained:
[0043] Step 1. Build a New England 10-machine 39-node system in PSD-BPA. This standard system has 10 generators, 39 buses and 46 AC lines. The reference power is 100MVA, and the reference voltage is 345kV.
[0044]Step 2, use Matlab to generate BPA power flow files and transient fault files in batches, call BPA power flow and transient state programs through MATLAB, and perform calculation and simulation. By setting different operating modes and fault locations, a large amount of original data can be obtained, and 36-dimensional feature data.
[0045] In the above step 2, there are 4000 sets of training data and 2000 sets of test data. The simulation settings for generating these data are as follows:
[0046] The load model is set to 9 types (80%, 85%, 90%, 95%, 100%, 105%, 110%, 115%, 120%), and the output state of the generator is changed...
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