Particle Filter Based Multi-sensor Fusion for Remaining Service Life Estimation of Energized LV-Aerial Bundled Cables

Taimoor Zafar ()
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Taimoor Zafar: Electrical Engineering Department, Bahria University, Karachi, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 2, 664-687

Abstract: Aerial Bundled Cables (ABC) consist of several wires that contain numerous layers of thermal insulation, which reduces the risk of theft. Nonetheless, there have been regular reports of rapid degeneration of such cables in coastal areas, resulting in multiple unplanned breakdowns. This study employs the data, collected from field-based nondestructive assessment techniques such as ultrasonic listening and thermal imaging. There is a pressing need for advanced tools to estimate the remaining lifespan of ABCs deployed along coastlines. This paper presents a novel approach using a particle filter-based fusion of multiple sensors framework for estimating the Remaining Useful Life (RUL) of in-service ABCs in a severe coastal atmosphere. The use of multi-sensormeasurement data improves the accuracy and reliability ofthe RUL estimation. This will allow electric power distribution companies to plan maintenance and replacement activities well in time. In the reported research work, the f-step prediction scheme under the framework of the Particle filter algorithm is implemented to predict the posterior density function of degradation growth in the cable insulation. The Particle Filter (PF) method performs effectively with nonlinear state transitions and measurement functions, even when addressing non-Gaussian or multidimensional noise variations. The technique also contains a step error calculation approach for determining forecast accuracy when measurement data is missing. The encouraging outcomes of this strategy illustrate its efficacy.

Keywords: Particle Filtering; Multisensory Fusion; Predictive Based Maintenance; Degradation Growth Rate; and Aerial Bundled Cables (ABCs) (search for similar items in EconPapers)
Date: 2024
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