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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 article, 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. [ABSTRACT FROM AUTHOR]

Leakage current (LC) monitoring is a widely employed tool for the investigation of surface electrical activity and the performance of high voltage insulators. Surface activity is correlated to the shape of LC waveforms. Although field monitoring is necessary in order to acquire an exact view of activity and insulators' performance, field waveforms are not often recorded due to the required long term monitoring and the accumulation of data. Instead, extracted values, such as the peak value, charge and number of pulses exceeding predefined thresholds, are recorded, with actual waveforms either being recorded occasionally or not at all. However, a fully representative extracted value is yet to be determined. In this paper, 1540 field waveforms are investigated to acquire a detailed image of the waveforms' shape in the field. Simple classification rules are employed to distinguish between basic groups. Discharge waveforms are further classified based on the duration of discharges. Twenty different features, from time and frequency domain, two feature extraction algorithms (student t-test and mRMR) and three classification algorithms (knn, Na?ve Bayes, Support Vector Machines) are employed for the classification. Results described in this paper can be used to maximize the efficiency of field LC monitoring. [ABSTRACT FROM PUBLISHER]

Leakage current (LC) monitoring is widely employed in order to investigate electrical surface activity on high voltage (HV) insulators. Long term field monitoring is required in order to obtain an actual view of the phenomena. Therefore any monitoring system is bound to encounter data size problems. A number of solutions have been proposed to address this problem by extracting different values from the measured waveforms as a performance indication. However, a parameter that is fully representative of the waveform is yet to be determined. Hence, measuring and storing of the actual LC waveform is still required on sites for research and monitoring purposes. In addition, field related noise can result to misinterpretation of the actual activity. In this paper, a number of 26089 waveforms, recorded in a 150 kV Substation on four different post insulators that are part of the grid, are investigated. The monitoring period exceeded six years. Different techniques are applied and evaluated on the data set, employing simple numeric criteria and more advanced techniques such as Fourier and Wavelet analysis. Results are presented towards addressing the problems of noise and data size. The study objective is the incorporation of the results in improved leakage current monitoring systems. [ABSTRACT FROM AUTHOR]

The use of composite polymeric insulators in overhead high voltage (HV) power transmission lines and in substations has been rapidly growing, as a result of their excellent operational performance, even under environmental pollution conditions. However, it has been observed that the insulation capacity of the composites deteriorates gradually for in-service insulators as a result of ageing, caused by several processes arising because of environmental, electrical and mechanical stresses. For these reasons, development of non-destructive diagnostic techniques for real-time, on-site inspection of the insulators' condition is required for ensuring high reliability of power transmission networks. To this end, laser-induced breakdown spectroscopy (LIBS) is found to offer an effective and reliable method for assessing the state of polymeric insulators, primarily silicone rubber (SIR) ones, that have been in service for up to 20 years on the HV power network of Crete, Greece. Determining the type of composite and furthermore the chemical integrity of the insulator is based on simple spectral indicators, which reflect the extent of chemical modifications induced on the insulator surface, as found in both standard and remote LIBS measurements performed in the laboratory. As a result, a stepwise diagnostic procedure was proposed and tested in an outdoors campaign, demonstrating that LIBS can become a field deployable technique for the efficient and reliable assessment of the performance of HV outdoor insulators in service. Unlabelled Image • Remote LIBS analysis of field-aged polymeric insulators has been performed on site. • A spectral indicator, R, is defined reflecting the insulator carbon-to‑silicon content. • R enables identification of the polymer composite type. • R permits assessment of the physical state of field-aged SIR insulators. • ATR-FTIR measurements support LIBS results. [ABSTRACT FROM AUTHOR]