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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.

In this paper, an optimal, reliable, and cost-effective framework for designing a renewable hybrid photovoltaic-wind-battery system is presented to minimize the total net present cost (TNPC) and to consider reliability constraint as loss of load probability (LPP) for the city of Ahvaz, Iran, considering the components outage rate (COR). The decision variables include the number of photovoltaic panels, wind turbines, batteries, and the angle of the photovoltaic panel optimized by the grey wolf optimizer (GWO) algorithm. The performance of the proposed method is compared with the particle swarm optimization (PSO) method. The results of a system designed in different combinations with and without considering COR are evaluated. The simulation results confirm that the GWO algorithm is superior to the PSO method by yielding lower TNPC (1.199 M$ for GWO and 1.201 M$ for PSO) and better LPP (0.653% for GWO and 0.655% for PSO) for optimal combination (photovoltaic-battery system). The results also showed that a photovoltaic-wind combination is not the most cost-effective and reliable for the Ahvaz region, and the implementation of hybrid systems based on wind power is not cost-effective in this region. In addition, the results showed that considering COR gives the designers of these systems a more accurate view of the cost and reliability. Moreover, considering COR increases the cost of load supply and undermines the load reliability.