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The fault-free performance of outdoor insulators is a matter of great importance for power utilities as a single insulator fault may lead to an extended outage. Several factors related to operation conditions affect the performance of outdoor insulators, with pollution being probably the most significant one [1]?[3]. Coastal areas are considered heavily polluted due to the deposition of sea salt on the insulators. When the deposited salt is diluted in water through humidity, drizzle, fog, or light rain, a conductive solution is created and leakage current flows on the surface. The activity may advance, under favorable conditions, to flashover. Therefore, the local weather and climate is an important factor for the determination of pollution severity as strong winds carry more salt and frequent rains may provide natural cleaning. [ABSTRACT FROM PUBLISHER]

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]