It is widely known and accepted that the global climate is changing with unprecedented speed. Climate models project increasing temperatures and changes in precipitation regimes which will alter the frequency, magnitude, and geographic distribution of climate-related hazards including flood, drought and heat waves. In the mining industry, climate change impacts are an area of research around the world, mostly in relation to the mining industry in Australia and Canada, where mining policies and mitigation actions based on the results of this research were adopted and applied. In Poland, there is still a lack of research on how climate change, and especially extreme weather events, impacts mining activity. This impact may be of particular importance in Poland, where the mining industry is in the process of intensive transition. The paper presents an overview of hazardous events in mining in Poland that were related to extreme weather phenomena. The needs and recommended actions in the scope of mitigating the impact of future climate change on mining in all stages of its functioning were also indicated. The presented analyses and conclusions are the results of the first activities in the TEXMIN project: The impact of extreme weather events on mining activities, identifying the most important factors resulting from climate change impact on mining.
The active distribution network (ADN) represents the future development of distribution networks, whether the islanding phenomenon occurs or not determines the control strategy adopted by the ADN. The best wavelet packet has a better time-frequency characteristic than traditional wavelet analysis in the different signal processing, because it can extract better and more information from the signal effectively. Based on wavelet packet energy and the neural network, the islanding phenomenon of the ADN can be detected. Firstly, the wavelet packet is used to decompose current and voltage signals of the public coupling point between the distributed photovoltaic (PV) system and power grid, and calculate the energy value of each decomposed frequency band. Secondly, the network is trained using the constructed energy characteristic matrix as a neural network learning sample. At last, in order to achieve the function of identification for islanding detection, lots of samples are trained in the neural network. Based on the actual circumstance of PV operation in the ADN, the MATLAB/SIMULINK simulation model of the ADN is established. After the simulation, there are good output results, which show that the method has the characteristics of high identification accuracy and strong generalization ability.
In the description of small-signal transmittances of switch-mode power converters several characteristic frequencies are usually used, corresponding to poles and zeros of transmittances. The knowledge of these frequencies is important in the design of control circuits for converters and usually are assumed to be constant for a given power stage of a converter. The aim of the paper is to evaluate the influence of converter primary parameters and load conductance on characteristic frequencies. Analytical derivations and numerical calculations are performed for an ideal and non-ideal BUCK converter working in continuous or discontinuous conduction mode.