Observer-based fault detection and diagnosis for the nonlinear stochastic distribution systems

Article type: Research Article

Authors: Yi, Qu^{a; b}

Affiliations: [a] Xianyang Vocational Technical College, Xianyang, Shaanxi, China | [b] College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China | E-mail: [email protected]

Correspondence: [*] Corresponding author: College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China. E-mail: [email protected].

Abstract: Our work describe a novel fault detection and diagnosis (FDD) problem for nonlinear stochastic distribution systems (SDS) with the help of the system output probability density functions (PDFs), and it can be obtained by rational square-root B-spline expansion. A new nonlinear FDD method based on observer is given by drawing into the adaptive tuning rule, in order that the residual signal can be sensitive to the system fault. And then, for the fault system, convergence and stability have been implemented by the fault detection and diagnosis. A simulation examples is shown to validates the efficiency of the proposed method and expecting results have been gained.

Keywords: Probability density functions, stochastic distribution systems, fault detection and diagnosis, the adaptive tuning rule

DOI: 10.3233/JCM-204487

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 21, no. 1, pp. 213-221, 2021