Sadegh Shajari; Reza Keypour
Abstract
Load sharing, as an important challenge in microgrids (MGs), is realized commonly via a droop control method. Conventional droop control methods are not applicable in unpredictable renewable energy sources (RESs) like photovoltaic (PV) and wind turbines (WT) because their output power depends on the ...
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Load sharing, as an important challenge in microgrids (MGs), is realized commonly via a droop control method. Conventional droop control methods are not applicable in unpredictable renewable energy sources (RESs) like photovoltaic (PV) and wind turbines (WT) because their output power depends on the weather conditions and can be extracted only if these free sources are available. This paper, considers two operating modes for these types of sources as Maximum Power Point Tracking (MPPT) and DC-link Voltage Control (DCLVC). These power sources usually operate in the MPPT mode unless the load of the MG drops to a lower level compared to the maximum power generation by RESs, in which case the sources switch to the DCLVC operating mode. This study proposed a method based on enhanced droop control, which helps RESs to choose its control mode locally without communication and share the demand of the AC MG with other dispatchable sources besides supplying its maximum power. The proposed method focused on supplying MG load from RESs as much as possible and simplicity in implementation. MG frequency helps the proposed controller to select its operation mode. Enhanced control for DC link voltage control is offered for inverter based RESs. The validity of the proposed method is approved by simulations in the MATLAB/SIMULINK environment.
Optimization
Javad Farzaneh; Reza Keypour; Ali Karsaz
Abstract
It is highly expected that partially shaded condition (PSC) occurs due to the moving clouds in a large photovoltaic (PV) generation system (PGS). Several peaks can be seen in the P-V curve of a PGS under such PSC which decreases the efficiency of conventional maximum power point tracking (MPPT) methods. ...
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It is highly expected that partially shaded condition (PSC) occurs due to the moving clouds in a large photovoltaic (PV) generation system (PGS). Several peaks can be seen in the P-V curve of a PGS under such PSC which decreases the efficiency of conventional maximum power point tracking (MPPT) methods. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is proposed based on particle swarm optimization (PSO) for MPPT of PV modules. After tuning the parameters of the fuzzy system, including membership function parameters and consequent part parameters, to obtain maximum power point (MPP), a DC/DC boost converter connects the PV array to a resistive load. ANFIS reference model is used to control duty cycle of the DC/DC boost converter, so that maximum power is transferred to the resistive load. Comparing the proposed method with PSO alone method and firefly algorithm (FA) alone shows its efficacy and high speed tracking of MPP under PSC. Due to the fact that these optimization algorithms have online applications, the convergence time of the algorithms is very important. The simulation results show that the convergence time for the proposed ANFIS-based method is lower than 0.15 second, while it is nearly three second for PSO and FA methods.
