Abstract
In order to monitor the insulation condition of XLPE cables which have been one of the most popular equipment used in power transition, a method is proposed in this paper to identify the insulation defects accurately by analysing the Weibull distribution of partial discharge (PD) signals. Experiments were applied on a 10kV 30m XLPE cable and three typical types of insulation defect models were designed and tested. The discharge pulse signals of each defect model were collected and their Weibull parameters were analysed and estimated with the ordinary least square estimation method. Several common pattern recognition methods such as support vector machine (SVM), artificial neural network (ANN) as well as classification and regression tree (CART) were used to test the accuracy of the extracted Weibull parameters. The results verified that it is effective to use the parameters of Weibull distribution for defect identification of XLPE cables and the features are suitable for common pattern recognition models.
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