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Wireless Power Transfer (WPT) technology has recently gained popularity in applications and research topics. It enables the transfer of electrical energy from a source to a load without connecting wires physically. The WPT system is commonly studied classically using integer order capacitors and inductors. Nonetheless, such integer order based systems have drawbacks, such as low output power, poor transmission efficiency and sensitivity to parameter variations. This paper proposes a fractional order resonant WPT circuit whereby both the transmitting and receiving ends are composed of a fractional capacitor and inductor to overcome such problems. In this paper, the overall performance is studied based on its output power and efficiency considering a series-parallel topology. The effect of fractional order in fractal elements will be analyzed to observe the optimal combination of components to achieve the maximum output power with higher efficiency. Through a comparative analysis of the results, several combinations of circuit parameters can provide a theoretical understanding for implementing an experimental system.

Extreme rainfall events often lead to excessive river flows and severe flooding for Pacific Island nations. Fiji, in particular, is often exposed to extreme rainfall events and associated flooding, with significant impacts on properties, infrastructure, agriculture, and the tourism sector. While these occurrences are often associated with tropical cyclones (TCs), the specific characteristics of TCs that produce extreme rainfall are not well understood. In particular, TC intensity does not appear to be a useful guide in predicting rainfall, since weaker TCs are capable of producing large rainfall compared to more intense systems. Therefore, other TC characteristics, in particular TC track morphology and background climate conditions, may provide more useful insights into what drives TC related extreme rainfall. This study aimed to address this problem by developing a decision tree to identify the most important predictors of TC related extreme rainfall (i.e., 95th percentile) for Fiji. TC attributes considered include; TC duration, the average moving speed of TCs, the minimum distance of TCs from land, seasonality, intensity (wind speed) and the geometry of TCs (i.e., geographical location, shape and length via cluster and sinuosity analyses of TC tracks). In addition, potential predictors based on the phases of Indo-Pacific climate modes were input to the decision tree to represent large scale background conditions. It was found that a TC's minimum distance from land was the most important influence on extreme rainfall, followed by TC cluster grouping, seasonality and duration. The application of this model could result in improved TC risk evaluations and could be used by forecasters and decision-makers on mitigating TC impacts over the Fiji Islands.