Power system dynamic security assessment comprehensive model and spatiotemporal visualization method
A dynamic safety and power system technology, applied in electrical components, circuit devices, AC network circuits, etc., can solve the problems of large amount of calculation, unintuitive presentation of dynamic safety evaluation results, and high computer time consumption, reducing the training burden and reducing the cost of training. Predicting performance online, overcoming limitations
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Embodiment 1
[0092] Embodiment 1: The first embodiment used in the present invention is based on the 23-node system provided by the power system simulation software PSS / E. The system includes 23 nodes, 6 generators, 10 transformers and other system components. This test includes all steps described in the method of the present invention, by testing on a computer equipped with an Intel Core i7 processor and 8GB memory, and obtained the test results. This test obtained 4510 initial sample sets, selected MIC value and ρ as the top 5% variables among all variables as features, used 80% of the sample set for training, and the remaining 20% for performance testing, after 5 times Cross-validation yielded stable results. Using R 2 and RMSE to evaluate the prediction performance, the calculation formula is as follows:
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[0095] In the formula: Y i for the actual TSM i value; Y i * is the predicted value of the regression model; for Y i The average value of ; m is ...
Embodiment 2
[0098] Embodiment 2: The second embodiment used in the present invention is based on the actual 1648-node system provided by the power system commercial simulation software PSS / E. The system contains 1648 nodes, 313 generators, 182 shunts, 2294 transmission lines and other system elements. The test hardware conditions are the same as in Example 1. This test has obtained 15375 initial sample sets, including 37439 variables involved in the present invention. The MIC value and ρ are selected as the top 0.1% variables in all variables. 80% of it is used for training, and the remaining 20% is used for performance testing. After 5-fold cross-validation, stable results are obtained. The test accuracy of the final model reaches R 2 = 0.9865, RMSE = 0.0197.
[0099] In order to verify whether the processing speed of the model can meet the seamless online dynamic security assessment, the processing speed tests were performed on both systems, and the results are as follows: Image 6...
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