In this paper, using artificial neural network (ANN) for tracking of maximum power point is discussed. Error back propagation method is used in order to train neural network. Neural network has advantages of fast and precisely tracking of maximum power point. In this method neural network is used to specify the reference voltage of maximum power point under different atmospheric conditions. By properly controling of dc-dc boost converter, tracking of maximum power point is feasible. To verify theory analysis, simulation result is obtained by using MATLAB/SIMULINK.
Increasing the application of power in electronic devices has increased the harmonics in power systems. Numerous methods like the synchronous reference frame (SRF) and the p-q-based method have been suggested to overcome the effects of these harmonics. The conventional SRF method provides acceptable results in harmonic compensation of high-order harmonics (higher than the fourth order), but the transient response time will be drastically increased in the presence of low-order harmonics due to the existence of a conventional low-pass filter. Furthermore, if the load terminal voltages are distorted, then the conventional SRF method will become unable to implement load current compensation. This research has used wavelet transform to overcome these difficulties. The proposed method is not only faster than conventional SRF, but it can also compensate the load currents if the load terminal voltages are also distorted. The simulation and experimental results are performed using MATLAB/Simulink and digital signal processor TMS320F28335 to verify the proposed method.
One of the most important issues in power systems is the energy. Due to the fossil fuel downfall, global warming and greenhouse effect which are important environmental effects of fossil fuel burning to produce energy and energy price variation, energy saving, and recycling in industry especially in electrical vehicles and electrical trains are very important and vital. In this paper, the main aim is using a battery and a supercapacitor for train kinetic energy recovery in the braking mode and grid demand reduction in the acceleration mode. In this regard, a new DC-DC converter is proposed. An algorithm control that manages energy flow between the battery and super-capaciror (SC) in the proposed converter is designed. The control method is based on the sliding mode. Because of immeasurable internal and initial voltage of the supercapacitor, an observer extracts the internal voltage of the supercapacitor. In addition, acceleration and braking conditions of electrical trains and energy storage systems are simulated in MATLAB Simulink, and effectiveness of the proposed converter, energy management algorithm, and control system in different phases has been proven.
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