Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance

Abdalla, Omer; Rezk, Hegazy; Ahmed, Emad M.

doi:10.1016/j.solener.2019.01.056

Cited by 111 publications

(61 citation statements)

References 51 publications

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“…Shading tends to be detrimental upon the performance of photovoltaic modules [39,40]. The cause of this owes to what is commonly referred to as 'string design', whereby one shaded module within the system or 'string' underperforms.…”

Section: Partial Shadingmentioning

confidence: 99%

Environmental Impacts on the Performance of Solar Photovoltaic Systems

et al. 2020

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This study scrutinizes the reliability and validity of existing analyses that focus on the impact of various environmental factors on a photovoltaic (PV) system’s performance. For the first time, four environmental factors (the accumulation of dust, water droplets, birds’ droppings, and partial shading conditions) affecting system performance are investigated, simultaneously, in one study. The results obtained from this investigation demonstrate that the accumulation of dust, shading, and bird fouling has a significant effect on PV current and voltage, and consequently, the harvested PV energy. ‘Shading’ had the strongest influence on the efficiency of the PV modules. It was found that increasing the area of shading on a PV module surface by a quarter, half, and three quarters resulted in a power reduction of 33.7%, 45.1%, and 92.6%, respectively. However, results pertaining to the impact of water droplets on the PV panel had an inverse effect, decreasing the temperature of the PV panel, which led to an increase in the potential difference and improved the power output by at least 5.6%. Moreover, dust accumulation reduced the power output by 8.80% and the efficiency by 11.86%, while birds fouling the PV module surface was found to reduce the PV system performance by about 7.4%.

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Section: Partial Shadingmentioning

confidence: 99%

Environmental Impacts on the Performance of Solar Photovoltaic Systems

et al. 2020

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“…This subsection presents a comparative study between the results obtained from the proposed GMPPT and the method proposed in Abdalla et al 23 To implement the simulations in the partially shaded conditions, the irradiation profile is 1000, 250, 750, 800, and 120 W/m 2 . It should be noted that a new PV array with different characteristic is used for this …”

Section: A Comparative Studymentioning

confidence: 99%

State‐dependent Riccati equation–based MRAC and fuzzy sliding mode control for maximum power point tracking in partially shaded conditions in PV systems

Rahideh

Ketabi

Niasar

2019

Int Trans Electr Energ Syst

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Summary This study suggests an adaptive and optimal control method to achieve the maximum power point tracking (MPPT) in the photovoltaic (PV) systems. For the maximum power transmission under varying the environmental conditions and partially shaded conditions, MPPT technologies are utilized in PV systems. For the improvement of functioning MPPT, a new four‐level control structure which decreases difficulty in the control process and efficiently deals with the uncertainties in the PV systems is introduced. The first one is a novel mechanism to estimate the reference voltage at a global maximum power point (GMPP), and an optimal and intelligent GMPP tracker (GMPPT) is employed as the last one. An extended state‐dependent Riccati equation (ESDRE) approach and fuzzy sliding mode control (FMSC) are selected as a GMPPT. The FMSC generates the duty cycle for the boost converter, and the ESDRE removes the underdamped and oscillatory response of PV systems. To verify the proposed method, several irradiation profiles that create several peaks (five peaks) in the P‐V curve are used. The simulation results show that the proposed method causes PV systems to track the GMPP immediately so that no oscillation around the GMPP is observed. Therefore, maximum efficiency can be derived from the PV system.

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“…Many papers have proposed optimization algorithms such as artificial bee colony, chicken swarm, PSO and win‐driven optimization as an MPPT controller. The accurate tracking of maximum power point (MPP) and slow convergence to the MPP can be mentioned as the main advantage and disadvantage of these methods, respectively.…”

Section: Introductionmentioning

confidence: 99%

A self‐constructing Lyapunov neural network controller to track global maximum power point in PV systems

Tavakoli

Forouzanfar

2020

Int Trans Electr Energ Syst

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This study suggests an intelligent control method for achieving the maximum power point tracking (MPPT) in photovoltaic (PV) systems. MPPT technologies in PV systems are used to transfer maximum power under various environmental conditions. To improve the performance of the MPPT, the study develops a two‐level adaptive control structure that can facilitate system control and efficiently handle uncertainties and perturbations in the PV systems and in the environment. The first control level is a ripple correlation control (RCC), and the second is a model reference adaptive control (MRAC). The paper emphasizes mainly on designing an MRAC algorithm that improves the underdamped dynamic response of the PV system. The original state‐space equation of the PV system is time‐varying and nonlinear, and its step response contains oscillatory transients that damp slowly. Using a self‐constructing Lyapunov neural network (SCLNN), an adaptive law of the controller is derived for the MRAC system to remove the underdamped modes in the PV systems. Also, a self‐constructing mechanism for generating blocks in the recursive unit of the SCLNN is introduced. Since the size of the SCLNN is optimal and minimal, the computation time, which is an important factor for real‐time implementation, is greatly reduced. It is shown that the proposed control algorithm enables the system to converge to the maximum power point in milliseconds.

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Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance

Cited by 111 publications

References 51 publications

Environmental Impacts on the Performance of Solar Photovoltaic Systems

Environmental Impacts on the Performance of Solar Photovoltaic Systems

State‐dependent Riccati equation–based MRAC and fuzzy sliding mode control for maximum power point tracking in partially shaded conditions in PV systems

A self‐constructing Lyapunov neural network controller to track global maximum power point in PV systems

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