Open AccessThermal balance diagram modelling of surge arrester for thermal stability analysis considering ZnO varistor degradation effect
Author(s) -
Seyyedbarzegar Seyyed Meysam,
Mirzaie Mohammad
Publication year - 2016
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2015.0728
Subject(s) - surge arrester , varistor , diagram , adaptive neuro fuzzy inference system , transient (computer programming) , surge , mechanics , control theory (sociology) , materials science , engineering , voltage , computer science , electrical engineering , fuzzy logic , fuzzy control system , physics , database , control (management) , artificial intelligence , operating system
This study proposes a new method for modelling of metal‐oxide surge arresters (MOSAs) thermal balance diagram based on power loss and heat dispersion curve. Adaptive network‐based fuzzy inference system (ANFIS)‐based method was offered to compute surge arrester power loss characteristic. Thermal stability (TS) of surge arrester, which is analysed by thermal balance diagram, has a great influence on their proper functions. MOSA material variation is an important factor that has an influence on its TS. Degradation factor (DF) was introduced as a new index in this study to represent material variations of MOSA. Therefore, applied voltage, temperature and DF were considered as inputs in ANFIS model to obtain accurate power loss which is a very important factor in TS characteristic. Heat dispersion curve was obtained as a main feature in the thermal balance diagram from surge arrester cooling curve. Degrading effect was considered using voltage and current measured from MOSA which are degraded by utilisation in the network. In addition, effect of assembling process has been considered by using new different varistors in experimental measurements. This study shows that the thermal balance diagram of surge arrester is predictable with a good accuracy by using artificial model.