Evaluation of MOSA condition using leakage current method

Stojanović, Zoran; Stojković, Zlatan

doi:10.1016/j.ijepes.2013.03.027

Cited by 33 publications

(21 citation statements)

References 13 publications

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“…These can occur in cases of voltage harmonics, variation of operating voltage, the occurrence of errors during relevant measurements, etc. [19]. The aforementioned methods are used to estimate the indicators of MOSA condition.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy expert system for metal-oxide surge arrester condition monitoring

Dobrić

Žarković

2020

Electr Eng

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Metal-oxide surge arresters (MOSAs) play an important role in preventing outage situations and thus in preserving the reliability of electric power system. So far, many MOSA monitoring methods have been developed. These methods propose different indicators that are more or less reliable in MOSA monitoring and diagnostics. Another important aspect of these indicators is that each one of them can be determined with a certain estimation error. None of the indicators has been proved to be supreme. Nevertheless, under different conditions, the reliability of these indicators may vary. This paper presents an analysis methodology for reliability of MOSA indicators and proposes a fuzzy logic expert system that overcomes the problem of decision-making in the case when more than one indicator is available. The analysis and verification are done by computer simulations.

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Section: Introductionmentioning

confidence: 99%

Fuzzy expert system for metal-oxide surge arrester condition monitoring

Dobrić

Žarković

2020

Electr Eng

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“…Due to these non-linear V-I characteristics and excellent current/energy absorption capabilities, MOAs are widely used in the electrical power systems [1][2][3][4][5][6]. For an MOA, zinc oxide (ZnO) varistors are the core components, and the V-I characteristics of ZnO varistors show temperature dependence under power frequency-applied voltages [7][8][9][10][11]. What's more, when an AC voltage is applied to a MOA, the resistive current flowing through the ZnO varistors can cause power loss, which may lead to temperature rise or thermal failure of the MOA [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Electro-Thermal Modeling of Metal-Oxide Arrester under Power Frequency Applied Voltages

Xie

Fang

et al. 2018

Energies

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Metal-oxide arresters (MOAs) are used to absorb the electrical energy resulting from overvoltages in power systems. However, temperature rises caused by the absorbed energy can lead to the electrothermal failure of MOAs. Therefore, it is necessary to analyze the electric and thermal characteristics of MOAs. In this paper, in order to study the electric and thermal characteristics of MOAs under power frequency voltage, an improved electrothermal model of an MOA is presented. The proposed electrothermal model can be divided into an electric model and a thermal model. In the electric model, based on the conventional MOA electric circuit, the effect of temperature on the voltage-current (V-I) characteristics of an MOA has been obtained. Using temperature and applied voltage as input data, the current flows through the MOA can be calculated using the artificial neural network (ANN) method. In the thermal model, the thermal circuit of a MOA has been built. The varistor power loss obtained from the electric model is used as input data, and the temperature of the zinc oxide varistors can be calculated. Therefore, compared with the existing MOA models, the interaction of leakage current and temperature can be considered in the proposed model. Finally, experimental validations have been done, and the electrothermal characteristics of an MOA have been studied by simulation and experimental methods. The electrothermal model proposed in this paper can assist with the prediction of the electric and thermal characteristics of MOAs.

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“…Therefore, their condition monitoring has significant influence on the reliability of power system. According to the most recent studies, equipment monitoring is the best way to increase the reliability and equipment failure diagnostic [1,2].…”

Section: Introductionmentioning

confidence: 99%