Open AccessA data‐driven Dir‐MUSIC method based on the MLP model
Author(s) -
Xu Wencong,
Hu Yue,
Li Jianxun
Publication year - 2022
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/smt2.12110
Subject(s) - computer science , noise (video) , matrix (chemical analysis) , stability (learning theory) , algorithm , process (computing) , multiple signal classification , signal (programming language) , antenna (radio) , function (biology) , artificial intelligence , machine learning , telecommunications , materials science , image (mathematics) , evolutionary biology , composite material , biology , programming language , operating system
The directional multiple signal classification (Dir‐MUSIC) algorithm based on antenna gain array manifold has been proposed to find the direction of the partial discharge source successfully in substations, however, a non‐linear optimisation problem in this algorithm is usually time‐consuming. To achieve real‐time feature, a data‐driven Dir‐MUSIC method based on the multilayer perception model is proposed to speed up the computing process and simultaneously guarantee the accuracy. Pre‐trained the multilayer perception model, the non‐linear optimisation problem can be treated as a function can be calculated directly. Input of the model is the noise matrix which can be uniquely calculated with a certain matrix measurement, and output is the estimated direction of the partial discharge source. Simulation results demonstrate that the proposed method has an excellent efficiency and relatively high accuracy, and the computing time can match the real‐time demand. Additionally, two effective improvements are proposed to raise the direction accuracy and stability. Specifically, mean error is reduced from 2.58° to 2.22° with the first improvement, and from 2.58° to 1.00° with the second improvement.