A. Zare; M. Simab; M. Nafar
Abstract
Due to the growing demand in the electricity sector and the shift to the operation of renewable sources, the use of solar arrays has been at the forefront of consumers' interests. In the meantime, since the production capacity of each solar cell is limited, in order to increase the production capacity ...
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Due to the growing demand in the electricity sector and the shift to the operation of renewable sources, the use of solar arrays has been at the forefront of consumers' interests. In the meantime, since the production capacity of each solar cell is limited, in order to increase the production capacity of photovoltaic (PV) arrays, several cells are arranged in parallel or in series to form a panel in order to obtain the expected power. Short circuit (SC) and open circuit (OC) faults in the solar PV systems are the main factors that reduce the amount of solar power generation, which has different types. Partial shadow, cable rot, un-achieved maximum power point tracking (MPPT) and ground faults are some of these malfunctions that should be detected and located as soon as possible. Therefore, effective fault detection strategy is very essential to maintain the proper performance of PV systems to minimize network interruptions. The detection method must also be able to detect, locate and differentiate between SC and OC modules in irradiated PV arrays and non-uniform temperature distributions. In this paper, based on artificial intelligence (AI) and neural networks (NN), neutrons can be utilized, as they have been trained in machine learning process, to detect various types of faults in PV networks. The proposed technique is faster than other artificial neural networks (ANN) methods, since it uses an additional hidden layer that can also increase processing accuracy. The output results prove the superiority of this claim.
Systems with Low Energy Consumption
Seyed E. Hoseini; M. Simab; B. Bahmani-Firouzi
Abstract
The argument of power systems planning in home microgrids has become one of the burning topics in optimization studies today among the researchers. Since the installation and use of high-capacity energy sources in power systems have many limitations and constraints, so part of the perspective of power ...
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The argument of power systems planning in home microgrids has become one of the burning topics in optimization studies today among the researchers. Since the installation and use of high-capacity energy sources in power systems have many limitations and constraints, so part of the perspective of power systems studies tends to operate residential microgrids. For this purpose, in this paper, operation planning is based on a residential microgrid consisting of combined heat and power (CHP), heat storage tank and boiler, and when possible, surplus electricity is sold to the upstream network to generate revenue. One of the innovations of this paper is the use of the exergy function to complete the optimization and, in practice, combine energy with economics. Other objective functions of this paper are to discuss the reduction of carbon dioxide in the air and the cost of operation. Energy management and planning in this home microgrid is tested with different capacities and types of CHPs, so that the home operator can choose the best mode to use. The multi-stage decision based dynamic programing (MSD-DP) optimization approach is used to minimize the operation costs of proposed framework. The most important innovation of this paper is the use of exergy function for energy management in a residential complex where CHP can also be used to generate electricity and heat simultaneously. Therefore, determining the capacity of CHP and the possibility of exchanging electricity with the upstream network can be mentioned as other innovations of this research.