Fault Detection for Non-Gaussian Stochastic Distribution Systems Based on Randomized Algorithms

This article presents a new fault detection scheme for stochastic distribution systems with non-Gaussian variables based on a probabilistic framework. The available information is the output probability density function (pdf) rather than the measured outputs themselves. The square-root B-spline function is utilized to formulate the output pdfs. Probabilistic parameter models are developed to characterize system uncertainties and faults. An observer is designed to detect the multiplicative and additive faults. The randomized algorithm is adopted to design the threshold to achieve an optimal balance between the false alarm rate (FAR) and the fault detection rate (FDR). The effectiveness of the proposed method is demonstrated and compared against existing work by utilizing a continuous stirred tank reactor system.

Automated summary

This article presents a new fault detection scheme for stochastic distribution systems with non-Gaussian variables based on a probabilistic framework. The output probability density function (pdf) is utilized as the available information instead of the measured outputs. The proposed method utilizes the square-root B-spline function to formulate the output pdfs and develops probabilistic parameter models to capture system uncertainties and faults. An observer is designed to detect multiplicative and additive faults, and a randomized algorithm is adopted to design the threshold for achieving an optimal balance between the false alarm rate (FAR) and the fault detection rate (FDR).