Abstract
Sulphur hexafluoride (SF6) gas insulated switchgear (GIS) is widely used in electrical power supply system and therefore needs regular preventive maintenance. Prediction and diagnosis analysis of faults in GIS using SF6 gas by-products was introduced previously by using 4 to 8 types of by product gases. As latest development on gas analyser, more by-product gases can be detected and used for condition monitoring of the GIS. The type, number, concentration and chemical stability of by-product gases of SF6 GIS are found to be closely correlated to the type of defect. However, the number of by-product gases used increases, the pattern for faults classification become more complex. Thus, further analysis on increasing number of by product gases using intelligent techniques such as pattern recognition is required. In this article, 12 significant by-products captured due to various sources of partial discharge fault in GIS were used. Random Forest (RF) was selected in this work as a multi-class classification technique. The analyses using RF pattern recognition with eight algorithms based on the presence and concentration of the gas by-products were carried out. The RF algorithm successfully recognises a given defect with an accuracy of 87.5% for all defects fault classification. The performance of the RF algorithm is 1.5 times better than the decision table algorithm which is the next best algorithm. This research illustrates the feasibility and applicability of an effective GIS diagnostic using gas by-products analyses, and in particular, using the RF pattern recognition.
Highlights
Partial discharge (PD) is the localised breakdown which occurs under high voltage stress in a small portion of solid, liquid or gas insulators
The above pattern recognition pre-process analyses reveal that the order of severity of a fault or discharge caused by all defects studied in this study can be listed in the following order: the electrode protrusion-fixed copper particle defect, the fixed copper particle defect, the electrode protrusion-free conducting particle defect, the electrode protrusion defect, the fixed aluminium particle defect, the electrode to dielectric voidfree conducting particle defect, the electrode to dielectric void defect, and the free conducting particle defects
By detecting more by product gases the safety operation of the gas insulated switchgear (GIS) can be ensure as the presence of carbon monoxide (CO), carbonyl sulphide (COS), SiF4 and hydrogen fluoride (HF) gases can be harmful to the GIS system due to their flammable and corrosive nature
Summary
Partial discharge (PD) is the localised breakdown which occurs under high voltage stress in a small portion of solid, liquid or gas insulators. A random forest is a flexible ensemble machine learning algorithm equipped for performing both classification and regression tasks where a group of models is joined to frame an effective model (bagging technique) It undertakes dimensional reduction approach (reduction of prediction variance), treats outlier values, treats missing values and other important steps of data analysis. PATTERN RECOGNITION USING WAIKATO ENVIRONMENT FOR KNOWLEDGE ANALYSIS (WEKA) WEKA is a machine learning workbench and data mining software written in Java under the GNU general public licence and was developed by the University of Waikato in New Zealand [33, 34] It is a collection of machine language algorithms with main features that comprise a comprehensive set of data pre-processing tools, learning algorithms, evaluation methods, graphical user interface (GUI), including data visualization and a comparing learning algorithm environment. There are other assessment measurements to validate a model [31]
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