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Leakage current monitoring is a commonly employed tool for the investigation of HV insulators’ performance. Several techniques have been applied on in order to extract activity indicating information from leakage current waveforms. However, a fully representative value is yet to be defined. A recent approach to cope with this problem is to classify...
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Citations
... Several signal analysis and classification techniques have been applied on LC measurements with different values considered as inputs and aiming to different goals and a thorough review can be found in [8]. Recently, a new approach has been proposed for the classification of leakage current waveforms [13, 17, 18]. According to this approach, 20 different features are extracted from the leakage current waveform in order to be used for the classification . ...
... The features selected are commonly used in the literature [8] and equally represent the time and the frequency domain (ten features from each domain). Classification techniques are used to classify each waveform in two different classes depending on the duration of discharges [13, 17, 18]. At first, a linear classification was attempted employing a Euclidian classifier and a simple genetic algorithms (GAs) approach, and results were not that encouraging [17]; this was attributed to the non-linearity of the problem and the absence of an effective feature selection scheme. ...
... Results showed the superior performance of SVMs and of the feature set provided by the mRMR algorithm. Then, a new GAs approach was applied [18] with GAs used for both feature selection and classification and the accuracy percentage achieved was significantly higher compared to the previous GA approach [17] and slightly inferior to the SVM-mRMR approach [13]. Although the mRMR-SVM classification scheme offered the best results, there were still some drawbacks. ...
Several techniques have been applied on leakage current waveforms in order to extract information regarding electrical activity on high-voltage insulators. However, a fully representative value is yet to be defined. In this paper, a hybrid support vector fuzzy inference system is introduced as a classification tool. The system incorporates fuzzy logic, genetic algorithms and support vector machines. Apart from the classification accuracy achieved, the system also produces a set of fuzzy rules under which the classification is made, allowing a further insight of the process. A comparison is made to other classification tools previously applied on the same data set.
... In addition, it should be noted that recent research has shown that other classification algorithms perform better [12], however this should largely be attributed to the selection of the feature set. When GAs are applied for both feature selection and classification, accuracy can be significantly increased (88.48%) [30], however still be slightly inferior compared to Support Vector Machines (SVMs) (90.21%) [13]. An added problem that should be noted here is the absence of an insight to the classification process as both SVMs and GAs operate similarly to a black-box classifier. ...
A number of 387 discharge portraying waveforms recorded on 18 different 150 kV post insulators installed at two different Substations in Crete, Greece are considered in this paper. Twenty different features are extracted from each waveform and two feature selection algorithms (t-test and mRMR) are employed. Genetic algorithms are used to classify waveforms in two different classes related to the portrayed discharges. Five different data sets are employed (1. the original feature vector, 2. time domain features, 3. frequency domain features, 4. t-test selected features 5. mRMR selected features). Results are discussed and compared with previous classification implementations on this particular data group.
