This article proposes an approach for identification of partial discharge location in a power cable using a fuzzy inference system and probabilistic neural networks. For accurate determination of the partial discharge source, feature extraction of the measured signals is used in the proposed method. White Gaussian noise, which simulates the high noise environment, is added to the partial discharge signals when making measurements. The accurate ratios of the partial discharge occurrence prediction using conventional observations of partial discharge signals via oscilloscopes are much improved by the proposed method. According to the concept of power delivery, both the peak absolute value and average power of partial discharge signals are adopted as input variables of the fuzzy inference system and probabilistic neural networks. Finally, experimental results validate that the proposed approach can effectively determine the location of partial discharge sources in practical partial discharge measurement.

This study proposes an alternative methodology for predicting salt contamination in rotating blade of wind turbine under lightning strike using fuzzy c‐means (FCM) and k‐means (KM) clustering approaches. The salt contamination states of wind turbine blades are classified with four different levels of equivalent salt deposit density (ESDD) classes. The lightning strike experiments are set up for simulating the condition when the blades are struck by lightning with four rotational speeds under various ESDD classes. Then, the absolute peak value of the measured current signals in grounding line using the high‐frequency current transformer and the average power are used to represent the input vectors of FCM and KM to predict the class of the salt contamination. The experimental results validated that the proposed approach can effectively classify the measured current signal and accurately predict the ESDD class on lightning strike occurrence.