Improved double integral sliding mode MPPT controller based parameter estimation for a stand-alone photovoltaic system

The use of solar cells despite being free of contamination and unlimited in terms of the amount of energy is considered as a costly way to generate energy. Two main factors may be enumerated as follows. First of all, the amount of sunlight and ambient temperature affected the amount of energy received from sunlight by solar panels, as long as the amount of sunlight changes overnight in line with changing weather conditions and the second one is the low efficiency of the energy conversion. The main reason for the low electrical efficiency is the nonlinear variation of the output voltage and current along with the change of the amount of radiation, the change of the temperature of the operating environment and the change of the electric charge, respectively. To address this concern, the maximum point of the photovoltaic system can be tracked through an appropriate algorithm and pushes the system point to the optimal point. In a word, the key goal of the investigation presented here is to provide an approach that in the high speed and precision of convergence to the maximum power point is well considered. So far, a large number of available methods have been used to increase the efficiency of solar cells. Some of these are associated with problems in the tracking process or they respond slowly. It should be noted that a set of them are depended on the types and structures of solar cells and also their implementation is very complex and costly. Therefore, this study has focused on intelligence-based techniques such as artificial neural networks to solve all the problems mentioned. The investigated outcomes verify the effectiveness of the approach performance proposed.

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“…According to Fig. 1, the solar cell's characteristic equation is expressed by the following equation [11].…”

Section: The Pv Modulementioning

confidence: 99%

“…Innovation of the proposed method is to use a search algorithm that receives the power and current of the PV array, and general maximum is estimated in its output. This algorithm improves the capability of existing one-step methods and increases their efficiency [11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Maximum power point tracking of the solar power plants in shadow mode through artificial neural network

Zandi

Mazinan

2019

Complex Intell. Syst.

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“…Currently, the use of renewable energies is increasing due to the need to reduce the negative effects on the environment produced by the use of fossil fuels, such as the greenhouse effect and climate change. Among the renewable energies, photovoltaic (PV) solar energy stands out for being clean, inexhaustible, easy to obtain, and low maintenance [1]. PV energy has received a lot of attention since it gives the possibility of generating electricity both in isolated conditions and connected to network systems.…”

Section: Introductionmentioning

confidence: 99%

“…However, this controller uses hysteresis modulation which produces variable switching frequency. Other works such as [1,17,18] have also used SM to regulate the MPPT voltage, showing robust responses to disturbances in the load and to variations in environmental conditions. Nevertheless, the project of these controllers needs complicated mathematical analysis.…”

Section: Introductionmentioning

confidence: 99%

Weighted-PSO Applied to Tune Sliding Mode Plus PI Controller Applied to a Boost Converter in a PV System

et al. 2019

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In this paper, a sliding mode plus proportional-integral (PI) controller for a boost converter in a photovoltaic system is proposed. The proposed controller is characterized by being easy to implement and by operating with constant switching frequency. The parameters of the proposed controller are calculated using the weighted particle swarm optimization technique, ensuring low percentage of overshoot and short setting time. The use of this optimization technique allows one to ensure the stability of the controller. A linear lead-leg controller is considered in order to compare the performance of the proposed controller. Finally, experimental results using a solar kit are presented to verify the performance of the proposed controller.

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“…Consequently, nonlinear controllers will be further approving. Sliding mode control (SMC) is well known as one of the most robust nonlinear controllers . It is also used in solar systems to adjust the voltage of PV.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of an improved sliding mode controller for maximum power point tracking in a SEPIC based on PV system

Shahdadi

Barakati

Khajeh

2019

Engineering Reports

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In this paper, a standalone photovoltaic (PV) system with an improved sliding mode control (SMC) based on the maximum power point tracking algorithm is presented. The system contains a solar panel, a single‐ended primary‐inductor converter, a control part, and a load. Because of the nonlinear behavior of the switching and solar panels, designing a nonlinear controller for the system is crucial. The principal purpose of this study is to propose a new approach to resist the PV system against uncertain conditions. The stability and robustness of the system are investigated in a normal situation, under uncertainty conditions, as well as under changes in environmental conditions. Both simulation and laboratory experimental results show that the controller has a precise and fast response in comparison with a more conventional controller.

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Improved double integral sliding mode MPPT controller based parameter estimation for a stand-alone photovoltaic system

Cited by 66 publications

References 24 publications

Maximum power point tracking of the solar power plants in shadow mode through artificial neural network

Maximum power point tracking of the solar power plants in shadow mode through artificial neural network

Weighted-PSO Applied to Tune Sliding Mode Plus PI Controller Applied to a Boost Converter in a PV System

Design and implementation of an improved sliding mode controller for maximum power point tracking in a SEPIC based on PV system

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