Simulation and experimental design of adaptive-based maximum power point tracking methods for photovoltaic systems

Srinivasarao, P.; Peddakapu, K.; Mohamed, Mohd Rusllim; Deepika, K. K.; Sudhakar, K.

doi:10.1016/j.compeleceng.2020.106910

Cited by 20 publications

(12 citation statements)

References 21 publications

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“…The efficiency is measured between 99.45 and 99.72%, and it takes 0.048 s to reach MPP. However, frequent optimization is required for this controller, and it comes at a very high cost [14]. An improved approach, i.e., steady output and fast tracking-MPPT (SOFT) is derived from the INC and P&O algorithms.…”

Section: Status Quo On Mppt Algorithms In Pv Systemsmentioning

confidence: 99%

“…Thus, an increase in di pv is a result of an increase in the level of solar irradiation, since increased irradiation produces an increased current. Thereby, the size of each subsequent step increases, which is accomplished by using (14). However, if the solar irradiance level drops, the current also drops, resulting in a smaller step size, and therefore, ( 15) is used instead.…”

Section: Vspomentioning

confidence: 99%

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Novel Lyapunov-based rapid and ripple-free MPPT using a robust model reference adaptive controller for solar PV system

Manna

Akella

Singh

2023

Prot Control Mod Power Syst

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The technological, economic, and environmental benefits of photovoltaic (PV) systems have led to their widespread adoption in recent years as a source of electricity generation. However, precisely identifying a PV system's maximum power point (MPP) under normal and shaded weather conditions is crucial to conserving the maximum generated power. One of the biggest concerns with a PV system is the existence of partial shading, which produces multiple peaks in the P–V characteristic curve. In these circumstances, classical maximum power point tracking (MPPT) approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point (GMPP). To overcome such obstacles, a new Lyapunov-based Robust Model Reference Adaptive Controller (LRMRAC) is designed and implemented to reach GMPP rapidly and ripple-free. The proposed controller also achieves MPP accurately under slow, abrupt and rapid changes in radiation, temperature and load profile. Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods, i.e., ANFIS, INC, VSPO, and P&O. MPP and GMPP are accomplished in less than 3.8 ms and 10 ms, respectively. Based on the results presented, the LRMRAC controller appears to be a promising technique for MPPT in a PV system.

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Section: Status Quo On Mppt Algorithms In Pv Systemsmentioning

confidence: 99%

Section: Vspomentioning

confidence: 99%

Novel Lyapunov-based rapid and ripple-free MPPT using a robust model reference adaptive controller for solar PV system

Manna

Akella

Singh

2023

Prot Control Mod Power Syst

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“…The suggested MPPT's efficiency is measured at 99.45% and 99.72%, with MPP capture time clocked at 0.048 s under a range of radiation and temperature conditions. 27 An effective SMC for MPPT in PV systems is described under varying temperatures and irradiance. Hysteresis QI is employed to wipe out classic SMC's chattering difficulties.…”

Section: Hybrid Mppt Approaches Include the Integration Of Classicalmentioning

confidence: 99%

A novel robust maximum power extraction framework for sustainable PV system using incremental conductance based MRAC technique

Singh

Akella

Manna

2023

Env Prog and Sustain Energy

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This study proposes a maximum power point tracking (MPPT) approach with two components: an Incremental Conductance (INC) MPPT for reference voltage regulation and a Model Reference Adaptive Controller (MRAC) for adjusting the duty cycle of the DC‐DC converter switch. A robustness test is performed on the system considering real‐world situations that involve an abrupt change in atmospheric conditions with load uncertainties. The probabilistic load distribution analysis is accomplished through levels of uncertainty (i.e., Probabilistic DOWN and UP) to guarantee the operation of the proposed INC‐MRAC controller while generating unexpected disturbances in the system. Using MATLAB/Simulink, the performances of the novel INC‐MRAC MPPT are comparatively analyzed with PO, INC, VSSPO, and ANN under realistic case studies in seven states. After comparative analysis, it is evident that the proposed MPPT offers less tracking time, i.e., 3.8 ms, to track maximum power point (MPP) with negligible steady‐state oscillation and ripples. It is about 3, 7, 9, and 10 times faster than ANN, VSSPO, INC and PO, respectively. Moreover, the tracking efficiency of the proposed controller is up to 99.63% as well as overall efficiency of the system is more than 98%. The tracking power loss and error rate in finding MPP for the proposed controller is the lowest among all state‐of‐the‐art MPPT approaches. Finally, the effectiveness of the proposed INC‐MRAC approach is experimentally validated by employing a real‐time simulator OPAL‐RT (OP4510). The study helps in environmental protection and participation in sustainable development.

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“…A photovoltaic module [35], a DC-DC BOOST converter [28][29][30][31][32][33][34][35], an MPPT controller [35], and a load make up a PV system [35][36][37]. The PVS system is depicted in diagram form in Figure 1.…”

Section: System Descriptionmentioning

confidence: 99%

“…Figure 3, displays the electrical circuit of a boost converter [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]: This type of converter can be used as an adapter Sourceload when the load needs a higher voltage than that of the photovoltaic system generator [9-31-39]. Through changing the duty cycle D, it is possible to adapt the source to the load [16].…”

Section: Dc/dc Converter [39]mentioning

confidence: 99%

Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

Maarouf

Ksentini

Azzag

et al. 2022

The Scientific Bulletin of Electrical Engineering Faculty

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Photovoltaic (PVS) generators’ nonlinear electrical characteristics allow for greater performance and efficiency when they are forced to operate at their peak power (MPP). This article suggests an adaptive method for maximizing power point tracking that makes use of artificial neural network (ANN) techniques (MPPT). A step-up converter powered by a separate solar generator is under the control of an ANN controller built on a neural network training database (PVS). The results show that ANN-MPPT has good control performance and is near to the maximum power point of PVS when compared to conventional MPPT methods like perturb and observe and incremental conductance.

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Simulation and experimental design of adaptive-based maximum power point tracking methods for photovoltaic systems

Cited by 20 publications

References 21 publications

Novel Lyapunov-based rapid and ripple-free MPPT using a robust model reference adaptive controller for solar PV system

Novel Lyapunov-based rapid and ripple-free MPPT using a robust model reference adaptive controller for solar PV system

A novel robust maximum power extraction framework for sustainable PV system using incremental conductance based MRAC technique

Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

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