Nowadays, particle swarm optimisation (PSO) is one of the most commonly used optimisation techniques. However, PSO parameters significantly affect its computational behaviour. That is, while it exposes desirable computational behaviour with some settings, it does not behave so by some other settings, so the way for setting them is of high importance. This paper explains and discusses thoroughly about various existent strategies for setting PSO parameters, provides some hints for its parameter setting and presents some proposals for future research on this area. There exists no other paper in literature that discusses the setting process for all PSO parameters. Using the guidelines of this paper can be strongly useful for researchers in optimisation-related fields.

Natural ester oils are the current target of many industries and electrical utilities as electrically insulating fluids to replace the conventional mineral oils. However, as previously investigated, most natural ester oils are based on edible products, causing a negative impact on the food crisis. Accordingly, nonedible green nanofluids based on cottonseed oil have been targeted in the present study. Additive graphene nanoparticles (0.0015 wt%, 0.003 wt%, 0.006 wt%, and 0.01 wt%) along with surfactant sodium dodecylbenzene sulfonate (SDBS) were used (1:1) due to their promising impact on dielectric and thermal properties. Experimental methods introduced were including characterization of graphene and preparation of dielectric nanofluids (DNFs). The main concern for any nanofluid to be usable in transformer applications is its long-term stability. The effect of various ultrasonication periods (10, 20, 30 and 60-minute) on short-term stability of nanofluids was preliminary investigated by visual inspection, highest short-term stability was obtained at 30-min and 60-min. Considering short-term stability results, the two most stable samples were investigated and compared for long-term stability through ultraviolet-visible (UV-Vis) spectroscopy to find the suitable ultrasonication time. In addition, dielectric and thermal properties of these samples were investigated and compared. Physical mechanisms were discussed for the obtained enhancements considering the effect of ultrasonication period on the number of dispersed nanoparticle sheets per unit volume and the corresponding effect on dielectric and thermal properties.

One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN). As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited. The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN) model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations.