Optimization of a GIS sensor layout based on global detection probability distribution evaluation. Issue 4 (9th September 2021)

• Record Type: Journal Article
• Authors: Li, Peijiang; You, Ting
• Is Part Of: Cognitive computation and systems. Volume 3:Issue 4(2021)
• Page Start: 342
• Page End: 350
• Publication Date: 2021-09-09
• Subjects: Bayesian risk decision -- discharge detection -- gas insulated switchgear -- sensor layout optimization; decision theory -- genetic algorithms -- power engineering computing -- sensor fusion -- probability -- optimisation -- gas insulated switchgear -- signal detection; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs2.12033
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract: Gas‐insulated switchgear (GIS) is an important power equipment. The implementation of health monitoring is limited by the number of sensors, and the global detection results of the system should be highly credible to ensure the reliability of the power supply system. To solve this problem, this study proposes a sensor layout optimization method based on global detection probability performance evaluation. Starting from the cost function, the GIS discharge detection problem is transformed into a Bayesian risk decision problem, the binary state of 'with discharge' and 'without discharge' is adopted to simplify the cost function and reduce the computing workload, and the objective function representing the global detection performance of the system is obtained. The solution of layout optimization is realized by the improved genetic algorithm. 3‐sensor, 4‐sensor and 6‐sensor layouts, which are digitally simulated at different detection rates, and then the distribution diagram of the global detection rate is obtained. On this basis, the feasibility and effectiveness of the optimization method are verified through an experiment. The results show that, compared with other sensor layout optimization methods, this optimization method can obtain the correct probability distribution of the detection rate globally and realize the graphical quantization of the detection performance distribution of the system so as to ensure the system performance.