Prognosis study of live aerial bundled cables in coastal areas using historical super-heterodyne ultrasonic listening data
| Autor: | Waleed Bin Yousuf, Tariq Khan, Anzar Alam, Taimoor Zafar, Muhammad Atayyab Shahid, Fuzail Hashmi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Electric power distribution
Aerial bundled cable Computer science business.industry 020209 energy 020208 electrical & electronic engineering Energy Engineering and Power Technology 02 engineering and technology Field (computer science) Reliability engineering Noise Nondestructive testing 0202 electrical engineering electronic engineering information engineering Ultrasonic sensor Electrical and Electronic Engineering Particle filter business Degradation (telecommunications) |
| Zdroj: | Electric Power Systems Research. 189:106591 |
| ISSN: | 0378-7796 |
| DOI: | 10.1016/j.epsr.2020.106591 |
| Popis: | Aerial Bundled Cables (ABCs) are multi-layer insulated bundled cables. XLPE insulation makes these cables less prone to pilferage. However, these cables are observed to rapidly degrade in a coastal environment, since multiple unexpected failures have been reported. The field acquired Ultrasonic listening nondestructive testing data is used in this work. The maintenance agency hence requires sophisticated techniques to predict degradation growth of ABCs installed in coastal areas. This paper presents the prognosis study of the energized ABCs subjected to harsh coastal climate, for the very first time. Prediction of degradation growth in the insulation of live aerial bundled cable (ABC) allows electric power distribution companies to plan maintenance and replacement activities well in time. Resultantly, the maintenance cost and probability of failure reduces. In the reported research work, the posterior density function of degradation growth in the cable insulation is predicted using the particle filter (PF) based f-step prediction algorithm. Nonlinear state-transitions and measurement functions are well handled by PF for non-Gaussian / multimodal noise distributions. The proposed algorithm is further complemented by a step error calculation method to analyze the prediction accuracy in the absence of measurement data. The promising results indicate the efficacy of the proposed technique. |
| Databáze: | OpenAIRE |
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