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- W4360971978 abstract "The out-of-step protection of a synchronous generator or a group of synchronous generators is unreliable with significant renewable power penetration in the power system. This work presents an innovative out-of-step protection algorithm using wavelet transform and deep learning to protect synchronous generators and transmission lines. The specific patterns are generated from a stable power swing, an unstable power swing, and a three-phase fault using the wavelet transform technique. The data containing 27008 continuous samples of 48 different features trains a two-layer feed-forward network’s particular design. The proposed algorithm gives an automatic, setting free, and highly accurate classification against the three-phase fault, the stable power swing, and the unstable power swing through pattern recognition within half cycle. The solution given in Chaps. 3 , 4 , and this chapter has a minor setting procedure that can be fully eliminated using the ANN approach. The proposed algorithm uses the Kundur two-area system and the 29-bus electric network for testing under different swing center locations and renewable power penetrations. The hardware-in-loop (HIL) test shows a newly developed out-of-step algorithm’s hardware compatibility. The proposed algorithm is compared with a recently reported algorithm in the end. The comparison and test results on different large-scale systems clarify that the algorithm is simple, fast, accurate, and HIL tested and not affected by the changes in power system parameters." @default.
- W4360971978 created "2023-03-30" @default.
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- W4360971978 date "2023-01-01" @default.
- W4360971978 modified "2023-09-29" @default.
- W4360971978 title "Wavelet Transform and Deep Learning Machine Model-Based Out-of-Step Relay" @default.
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- W4360971978 doi "https://doi.org/10.1007/978-981-19-9546-0_6" @default.
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