2020
DOI: 10.1049/iet-smt.2020.0021
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Experimental-based models for predicting the flashover voltage of polluted SiR insulators using leakage current characteristics

Abstract: This study aims to predict the flashover voltage (FOV) of silicone rubber (SiR) insulators. Accordingly, it benefits from two methods, including an artificial neural network (ANN) model and a FOV gradient fitting model. The FOV and leakage current (LC) tests are carried out on one un-aged and three aged specimens under uniform and longitudinal non-uniform pollution circumstances. The proposed ANN model is designed based on equivalent salt deposit density (ESDD), pollution non-uniformity degree, aging time, LC … Show more

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Cited by 9 publications
(4 citation statements)
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“…To date, few studies have investigated the performance of aged polymeric insulators with respect to LC. In [16], the LC was used to predict flashover voltage. The above study is limited to LC performance as a parameter of the prediction model.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…To date, few studies have investigated the performance of aged polymeric insulators with respect to LC. In [16], the LC was used to predict flashover voltage. The above study is limited to LC performance as a parameter of the prediction model.…”
Section: Introductionmentioning
confidence: 99%
“…The shape factor of the insulator, its height, and diameter are important factors affecting flashover performance [23]. In [16], experiments were conducted to predict the flashover voltage of two different profiles of composite insulators under various pollution levels and hydrophobicity classes, using flashover voltage and LC tests, where the components of LC were used as input for artificial neural network (ANN) model. Authors in [24] stated that the analysis of the LC of insulators aided in the assessment of high-voltage insulator conditions.…”
Section: Introductionmentioning
confidence: 99%
“…For aged samples, an extensive analysis of leakage current has been carried out in [27][28][29][30][31][32]. The investigation has been conducted on non-uniform and also longitudinal conditions based on experimental tests.…”
Section: Introductionmentioning
confidence: 99%
“…Otros autores proponen evaluar el rendimiento del aislador en base al número de pulsos, el valor pico y la carga acumulada de la corriente de fuga [101,102]. Dadashizadeh ha propuesto un modelo de redes neuronales artificiales para predecir la tensión de descarga disruptiva a partir de ESDD, grado de heterogeneidad de contaminación, edad, corriente fundamental y distorsión armónica, obteniendo un error relativo <6.4% [103].…”
Section: Corriente De Fuga Como Medio De Diagnósticounclassified