A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model

Jigneshkumar Pramodbhai Desai; Vijay Hiralal Makwana

doi:10.1186/s41601-021-00221-y

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A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model

Contents | 更新时间：2026-06-25

- A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model
- A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model
- 现代电力系统保护与控制（英文） 2021年6卷第4期
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- DOI：10.1186/s41601-021-00221-y
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- 纸质出版：2021
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Jigneshkumar Pramodbhai Desai, Vijay Hiralal Makwana. A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model[J]. 现代电力系统保护与控制（英文）, 2021,6(4).

Jigneshkumar Pramodbhai Desai, Vijay Hiralal Makwana. A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model[J]. Protection and Control of Modern Power Systems, 2021, 6(4).
Jigneshkumar Pramodbhai Desai, Vijay Hiralal Makwana. A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model[J]. 现代电力系统保护与控制（英文）, 2021,6(4). DOI： 10.1186/s41601-021-00221-y.

Jigneshkumar Pramodbhai Desai, Vijay Hiralal Makwana. A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model[J]. Protection and Control of Modern Power Systems, 2021, 6(4). DOI： 10.1186/s41601-021-00221-y.

摘要

Abstract

Out-of-step protection of one or a group of synchronous generators is unreliable in a power system which has significant renewable power penetration. In this work

an innovative out-of-step protection algorithm using wavelet transform and deep learning is presented to protect synchronous generators and transmission lines. The specific patterns are generated from both stable and unstable power swing

and three-phase fault using the wavelet transform technique. Data containing 27

008 continuous samples of 48 different features is used to train a two-layer feed-forward network. The proposed algorithm gives an automatic

setting free and highly accurate classification for the three-phase fault

stable power swing

and unstable power swing through pattern recognition within a half cycle. The proposed algorithm uses the Kundur 2-area system and a 29-bus electric network for testing under different swing center locations and levels of renewable power penetration. Hardware-in-the-loop (HIL) tests show the hardware compatibility of the developed out-of-step algorithm. The proposed algorithm is also compared with recently reported algorithms. The comparison and test results on different large-scale systems show that the proposed algorithm is simple

fast

accurate

and HIL tested

and not affected by changes in power system parameters.

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