基于自适应变分模分解和奇异值分解的触电特征提取方法

IF 1.4 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Iet Science Measurement & Technology Pub Date : 2023-09-14 DOI:10.1049/smt2.12157
Hongzhang Zhu, Chuanping Wu, Yang Zhou, Yao Xie, Tiannian Zhou
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引用次数: 0

摘要

提出了一种将自适应变分模式分解(AVMD)和奇异值分解(SVD)相结合的特征提取方法,用于触电故障类型识别。AVMD算法用于将电击信号自适应地分解为固有模式分量,每个分量包含不同的频率信息。随后利用相关系数提取振幅大于或等于0.1的本征模分量(γk${\gamma}_k$≥0.1)。然后对γk${\gamma}_k$≥0.1本征模分量,基于其最大奇异值和奇异熵。这种方法有效地克服了传统VMD需要手动设置K值的局限性。此外,它通过SVD实现了对固有模式分量的降维和特征提取,提高了计算效率和故障识别精度。广泛的仿真表明,使用所提出的AVMD-SVD方法对动植物触电故障类型的识别率很高,识别率高达99.25%。性能比较分析进一步验证了AVMD-SVD方法优于类似的经验模式分解SVD特征提取技术。
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Electric shock feature extraction method based on adaptive variational mode decomposition and singular value decomposition

This paper proposes a feature extraction method combining adaptive variational mode decomposition (AVMD) and singular value decomposition (SVD) for electric shock fault-type identification. The AVMD algorithm is utilized to adaptively decompose the electric shock signal into intrinsic mode components, each containing distinct frequency information. Subsequently, the correlation coefficient is employed to extract the intrinsic mode component with amplitudes greater than or equal to 0.1 ( γ k ${\gamma }_k$ ≥ 0.1). Feature extraction is then performed using SVD on the γ k ${\gamma }_k$ ≥ 0.1 intrinsic mode component, based on its maximum singular value and singular entropy. This approach effectively overcomes the limitation of the traditional VMD that necessitates manual K value setting. Moreover, it achieves dimensionality reduction and feature extraction of the intrinsic mode components through SVD, resulting in enhanced computational efficiency and fault identification accuracy. Extensive simulations demonstrate the remarkable recognition rates of electric shock fault types in animals and plants using the proposed AVMD-SVD method, achieving a recognition rate as high as 99.25%. Comparative performance analysis further verifies the superiority of AVMD-SVD over similar empirical mode decomposition-SVD feature extraction techniques.

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来源期刊
Iet Science Measurement & Technology
Iet Science Measurement & Technology 工程技术-工程:电子与电气
CiteScore
4.30
自引率
7.10%
发文量
41
审稿时长
7.5 months
期刊介绍: IET Science, Measurement & Technology publishes papers in science, engineering and technology underpinning electronic and electrical engineering, nanotechnology and medical instrumentation.The emphasis of the journal is on theory, simulation methodologies and measurement techniques. The major themes of the journal are: - electromagnetism including electromagnetic theory, computational electromagnetics and EMC - properties and applications of dielectric, magnetic, magneto-optic, piezoelectric materials down to the nanometre scale - measurement and instrumentation including sensors, actuators, medical instrumentation, fundamentals of measurement including measurement standards, uncertainty, dissemination and calibration Applications are welcome for illustrative purposes but the novelty and originality should focus on the proposed new methods.
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