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

Abstract

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.

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

“…Traditional approaches are often utilised in a wide range of applications since they are simple and inexpensive to execute like perturb and observe (P&O), incremental conductance (INC), and so forth. [7][8][9][10] Even though P&O, and INC MPPT strategies are popular and work well when irradiance is uniform, they cannot track the MPP when irradiance changes quickly and there are partial shading conditions (PSC). These strategies also suffer from significant steady-state oscillations and poor convergence speed, which are their biggest problems.…”

Enhanced MPPT-Based Fractional-Order PID for PV Systems Using Aquila Optimizer

“…A combined modeling predictive supervising scheme with an IC algorithm is involved to exploit the PV module at the MPP with autonomous PV system parameters [35]. An MPPT-based IC technique for controlling the reference output voltage and an adaptive reference regulator for monitoring the duty cycle interrupter of a DC-DC controller is proposed in [36]. In [37], a qualified MPPT algorithm is implemented using a fuzzy logic scheme, and a conventional IC technique for PV systems is presented.…”