Integration of solar PV as a distributed generator (DG) require efficient and coordinated control measures for the proper synchronization. In this paper, a hybrid control algorithm for single stage solar photovoltaic (PV)
In this paper, the electrical parameters of a hybrid power system made of hybrid renewable energy sources (HRES) generation are primarily discussed. The main components of HRES with energy storage (ES) systems
Knez M. and Jereb B.: ''Solar power plants Desai H.P. and Patel H.K.: ''Maximum power point algorithm in PV generation: an overview''. Proc. 7th Int. Conf. on Power Electronics and Drive
Under partial shading conditions, photovoltaic (PV) arrays exhibit multiple local maximum power points and a single global maximum power point in the P-V characteristics
However, Agarwal et al. [16] presented a grid connected solar PV power generation system using a artificial neural network (ANN)-based control algorithm which uses Leaky Least Mean Fourth
Renewable Energy technologies are becoming suitable options for fast and reliable universal electricity access for all. Solar photovoltaic, being one of the RE technologies, produces variable output power (due to variations
One of the most available energy sources in the world is solar energy, while in the category of renewable and nonrenewable energies is in the first group. Power generation
However, Agarwal et al. [] presented a grid connected solar PV power generation system using a artificial neural network (ANN)-based control algorithm which uses Leaky Least Mean Fourth control algorithm. This control
To achieve efficient photovoltaic power generation prediction in smart city energy management, Sun et al. studied and designed a model based on a multi-scale short-term memory recurrent neural network that can predict
A grid coupled solar photovoltaic power generating system is developed using current synchronous detection controller along with power quality refinement features in paper
An efficient maximum power point tracking (MPPT) method plays an important role to improve the efficiency of a photovoltaic (PV) generation system. This study provides an extensive review of the current status of MPPT
For a grid-connected PV system, inverters are the crucial part required to convert dc power from solar arrays to ac power transported into the power grid. The control performance and stability of inverters severely affect
Numerous control algorithms are widely reported in the literature for grid integration of solar PV systems with active power feeding, reactive power compensation and harmonics elimination abilities [ – ]. These techniques can be broadly divided in three categories: as conventional, intelligent and adaptive techniques.
In photovoltaic systems, one of the most used MPPT algorithms is the P&O algorithm. Its basic idea is to gradually alter the PV system's operating point while closely observing how the power output changes in response. The operating point is changed to improve power output after reaching the maximum power point 32.
In this paper, a hybrid control algorithm for single stage solar photovoltaic (PV) system integrated with low voltage (LV)/medium voltage (MV) grid is proposed. The hybrid algorithm utilizes I cos ϕ technique and quasi-Newton back-propagation (QNBP) neural network (NN).
An efficient maximum power point tracking (MPPT) method plays an important role to improve the efficiency of a photovoltaic (PV) generation system. This study provides an extensive review of the current status of MPPT methods for PV systems which are classified into eight categories.
Integration of solar PV as a distributed generator (DG) require efficient and coordinated control measures for the proper synchronization. In this paper, a hybrid control algorithm for single stage solar photovoltaic (PV) system integrated with low voltage (LV)/medium voltage (MV) grid is proposed.
Power electronics combined with intelligent control help PV systems to be observable, controllable, and adjustable. However, the degree of intelligence of PV systems is still at a low level. The potential of intelligent control to improve PV system power quality and stability has yet to be explored.
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