Fire monitoring systems have usually been based on a single sensor such as smoke or flame. These single sensor systems have been unable to distinguish between true and false presence of fire, such as a smoke from a cigarette which might cause the fire alarm to go off. Consuming energy all day long and being dependent on one sensor that might end with false alert is not efficient and environmentally friendly. We need a system that is efficient not only in sensing fire accurately, but we also need a solution which is smart. In order to improve upon the results of existing single sensor systems, our system uses a combination of three sensors to increase the efficiency. The result from the sensor is then analyzed by a specified rule-set using an AI-based fuzzy logic algorithm; defined in the purposed research, our system detects the presence of fire. Our system is designed to make smart decisions based on the situation; it provides feature updated alerts and hardware controls such as enabling a mechanism to start ventilation if the fire is causing suffocation, and also providing water support to minimize the damage. The purposed system keeps updating the management about the current severity of the environment by continually sensing any change in the environment during fire. The purposed system proved to provide accurate results in the entire 15 test performed around different intensities of a fire situation. The simulation work for the SMDD is done using MATLAB and the result of the experiments is satisfactory.
Maximum power-point tracking (MPPT) is applied to enable effective operation of photovoltaic (PV) systems under different external conditions. MPPT is based on a control system that aims at maintaining the PV system operation in the most effective conditions of maximum power output. This paper demonstrates the effective application of a novel adaptive control approach developed to be used in the field of power electronics. The application to MPPT is developed by using a non-inverted Buck-Boost converter applied to the PV system. The novel control methodology is based on the application of the Lyapunov stability concepts. The strength of this novel control technique is confirmed by the accurate comparison among the results obtained by using the proposed solution and some controllers proposed in the literature.
In this paper, a novel model-reference adaptive control methodology is proposed for the regulation of two power converter topologies. The main controller objective is the asymptotic tracking of the reference trajectory provided as input. The tracking is achieved through the adaptive mechanism based on Torelli Control Box approach. The control methodology explicitly guarantees convergence and asymptotic stability of the system. The designed controller has been simulated on buck and boost power converters and its performance has been analyzed by subjecting the converters to varying load and voltage conditions. Under all the test conditions, the controller proposed performs better than a backstepping-based controller taken as a benchmark for both converters.
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