The transition from fossil fuel to a green economy has led to the rise of renewable energy sources. Wind energy stands out because it is free, clean, inexhaustible, has the capacity to generate greater power, and has lower energy costs. From local to global scales, the environmental effects of wind power are frequently positive, in contrast to the negative impacts associated with fossil fuel technologies. These include air pollution, climate change, health risks, high mortality rates, especially in infants, and greenhouse gas emissions. However, all energy sources have an impact on the environment and the economy including wind energy. With the rapid growth of wind energy over the last decade and the future potential of wind power generation, strategic assessment of these environmental and economic impacts, both positive and negative, and developing ways to mitigate these negative impacts are prerequisite operations to be carried out in the overall development of human-, economically- and ecologically-friendly wind energy.

In this paper, temperature-dependent magnetic properties of magnetite nanoparticles (MNPs) synthesized at room temperature via chemical coprecipitation were investigated. Monodispersed MNPs with an average size of 11.22 nm were synthesized at a stirring rate of 200 rpm and a pH value of 10. The phase composition, particle size/morphology, and magnetic properties of the synthesized nanoparticles were characterized by X-ray diffractometer (XRD), scanning transmission electron microscope (STEM) and superconducting quantum interference device (SQUID), respectively. The results show that the synthesized nanoparticles are magnetite with a spinel structure without any impurities. The synthesized MNPs exhibit a nearly spherical morphology with a uniform particle size distribution and an average particle size of 11 nm. Based on the analysis of the temperature-dependent magnetic properties at 5–300 K, the coercivity and remanence in the hysteresis loop for the MNPs decrease with the increase in temperature and disappear at room temperature (i.e., 300 K), thus showing a superparamagnetic behaviour. The superparamagnetic behaviour of the synthesized MNPs was analysed using a developed model based on the core-shell theory. The experimental results of magnetization are in a reasonable agreement with the calculated data by the developed model. The magnetic susceptibility of the synthesized MNPs increases and the coercivity, exchange bias, remanent and saturation magnetizations decrease with the increase of the temperature. It is indicated that the synthesized MNPs could be used as a promising candidate in various applications such as heat transfer, magnetic hyperthermia and magnetic separation.

Faults in electrical networks are among the key factors and sources of network disturbances. Control and automation strategies are among the key fault clearing techniques responsible for the safe operation of the system. Several researchers have revealed various constraints of control and automation strategies such as a slow dynamic response, the inability to switch the network on and off remotely, a high fault clearing time and loss minimization. For a system with wind energy technologies, if the power flow of a wind turbine is perturbed by a fault, the intermediate circuit voltage between the machine side converter and line side converter will rise to unacceptably high values due to the accumulation of energy in the DC link capacitor. To overcome the aforementioned issues, this paper used MATLAB simulations and experiments to analyze and validate the results. The results revealed that fault ride through capability with Supervisory Control and Data Acquisition (SCADA) viewer software, Active Servo software and wind sim packages are more adaptable to the variations of voltage sag, voltage swell and wind speed and avoid loss of synchronism and improve power quality. Furthermore, for protection purposes, a DC chopper and a crowbar should be incorporated into the management of excess energy during faults and a ferrite device included for the reduction of the electromagnetic field.

The aim of this study is to estimate the wind energy potential at four locations in Burundi: Bujumbura, Gisozi, Gitega and Mpota. For this endeavour, some commonly used statistical probability distribution functions (PDFs) (i.e., Burr, Gamma, Lognormal, Normal, Rayleigh and Weibull) are assessed in the modelling of around 20 years of daily wind speed data measured at the four locations. The parameters for each PDF are estimated using the Maximum likelihood method and goodness-of-fit tests are used to assess how well the PDFs fit the data. It is found that the Burr distribution fits all the data at 0.05 significance level. Finally, computed Burr parameters for monthly wind speed datasets are used to estimate the mean monthly wind power density (WPD) at each location. Results obtained show that Bujumbura has high potential for wind energy harvesting