A data‐driven Dir‐MUSIC method based on the MLP model. Issue 6 (25th May 2022)

• Record Type: Journal Article
• Authors: Xu, Wencong; Hu, Yue; Li, Jianxun
• Is Part Of: IET science, measurement & technology. Volume 16:Issue 6(2022)
• Page Start: 367
• Page End: 376
• Publication Date: 2022-05-25
• Subjects: Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/smt2.12110
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract: 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.