Nowadays, consumption of energy is gradually increasing, the conscious for protecting environment is improving and liberalization in energy market is proceeding. Furthermore, the distribution of traditional energy sources is not homogeneous. These reasons are increasing interest to create policies for benefiting from renewable sources better by developing newer technology and to fuel cell based alternative distributed generation systems. Energy generation systems like wind, photovoltaic (PV), micro hydroelectric are the promising and the most important renewable energy technologies. Moreover, fuel cell based systems will indicate a great potential for future applications of distributed generation because of their quick developing technology, high productivity, pollutant gases with no or low emission and their elastic structures. In this study, energy management of a hybrid microgrid that was renewable energy based (wind, photovoltaic, micro hydroelectric) was provided by a computer program reformed in Microsoft C Sharp(C#) programming language in order to supply electric energy to small locations like holiday camps which were far away from energy distributing systems and other locations. It was observed that demanded energy was met by the data taken from generation sources thanks to this developed program. The importance of the energy management was explained by analyzing one year results via graphics.
Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.
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