Details

Autor(en) / Beteiligte

• Coutinho, M.S.
• Lourenço Novo, L.R.G.S.
• de Melo, M.T.
• de Medeiros, L.H.A.
• Barbosa, D.C.P.
• Alves, M.M.
• Tarragô, V.L.
• dos Santos, R.G.M.
• Lott Neto, H.B.T.D.
• Gama, P.H.R.P.

Titel

Machine learning-based system for fault detection on anchor rods of cable-stayed power transmission towers

Ist Teil von

• Electric power systems research, 2021-05, Vol.194, p.107106, Article 107106

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •This paper presents a system for detecting faults and tears on buried anchor rods.•A high frequency connector was designed to match a portable VNA with the anchor rod.•A set of 14 anchor rods were buried in the test field to provide measurements.•A logistic regression obtained a mean accuracy of 98.26% in the classification task.•The proposed system has proved to be feasible and reliable for field applications. This paper presents a field application system for detecting structural faults on anchor rods of cable-stayed towers of power transmission lines, based on a nondestructive technique using frequency domain reflectometry analysis. A specific high frequency connector was designed to interface a portable vector network analyzer and the buried rods in the test field. The soil electric permittivity was modeled using a full-wave electromagnetic simulation software and measurements. A machine learning structure was developed for the measured S11-parameter signals from the distinct buried rods to binary classify them as normal or faulty. An accuracy greater than 98% was achieved by characterizing the system as reliable and feasible for a novel predictive maintenance process for ground-anchored metallic rods.