A Novel MRAC-MPPT Scheme to Enhance Speed and Accuracy in PV Systems

Manna, Saibal; Akella, Ashok Kumar; Singh, Deepak Kumar

doi:10.1007/s40998-022-00542-0

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…All of the simulation settings for the recommended strategy are detailed in Table 1. Error for five distinct methods were calculated using the following formula 35

\mathrm{Mean}\ \mathrm{absolute}\ \mathrm{error}\ \left(\mathrm{MAE}\right)=\frac{\sum \mid Estimated\ Value- Actual\ Value\mid }{n}

…”

Section: Resultsmentioning

confidence: 99%

Hybrid two‐stage adaptive maximum power point tracking for stand‐alone, grid integration, and partial shaded PV system

Manna,

Singh,

Akella

2023

Adaptive Control & Signal

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SummaryIn a PV array, the power‐voltage characteristic (P‐V) and output power are completely dependent on atmospheric conditions. Consequently, the P‐V curve's maximum power point (MPP) likewise changes significantly. This abrupt oscillation in the MPP may be caused by the PV panel being uniformly or partially shaded by trees, buildings, raindrops, and clouds. However, in both circumstances, the maximum power point tracking (MPPT) turns into a highly nonlinear issue with a time‐bounded solution. The unpredictable weather circumstances change P‐V characteristics after every short time interval. Therefore, this research presents a hybrid two‐stage adaptive MPPT to overcome the environmental uncertainties. The first stage is MPPT control block for determining reference voltage for each MPP and the second stage is modified model reference adaptive controller (MMRAC) block fine‐tunes converter duty cycle to maintain MPP of PV panel. The probabilistic evaluation is executed through four distinct levels of uncertainty, including stand‐alone, partially‐shaded, grid integration, and OPAL‐RT to examine the system robustness in diverse scenario. The possibility of the tracking failure caused by dynamic irradiance change is also verified. Moreover, efficiency, tracking ability, power loss, ripple, and error rate are compared with the state‐of‐the‐art strategies, that is, ANFIS, INC, P&O, and VSPO. The satisfactory performances of the new strategy under simultaneously varying irradiance, temperature, and load; partially shaded and grid integration shows the supremacy.

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“…All of the simulation settings for the recommended strategy are detailed in Table 1. Error for five distinct methods were calculated using the following formula 35

\mathrm{Mean}\ \mathrm{absolute}\ \mathrm{error}\ \left(\mathrm{MAE}\right)=\frac{\sum \mid Estimated\ Value- Actual\ Value\mid }{n}

…”

Section: Resultsmentioning

confidence: 99%

Hybrid two‐stage adaptive maximum power point tracking for stand‐alone, grid integration, and partial shaded PV system

Manna,

Singh,

Akella

2023

Adaptive Control & Signal

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“…As suggested in 71 , 72 , a small signal model of the studied system was considered (Fig. 4) in order to make it easier to analyze how the system responds to changes over time.…”

Section: Dynamics Modeling Of the Dc-dc Boost Convertermentioning

confidence: 99%

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

Deghfel,

Badoud,

Merahi

et al. 2024

Sci Rep

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Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller’s performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm’s performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method.

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“…The transfer function from v pv (s) to d(s) in small signal operation is now developed around an operational point, ignoring the battery load. At node (1), using the KCL method, the relationship between v pv (s) and d(s) is determined in the frequency domain as [27]:…”

Section: Pv System Small-signal Modeling With Power-conversion Stagementioning

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 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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A Novel MRAC-MPPT Scheme to Enhance Speed and Accuracy in PV Systems

Cited by 13 publications

References 24 publications

Hybrid two‐stage adaptive maximum power point tracking for stand‐alone, grid integration, and partial shaded PV system

Hybrid two‐stage adaptive maximum power point tracking for stand‐alone, grid integration, and partial shaded PV system

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

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

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