A Novel Hybrid Approach for Maximizing the Extracted Photovoltaic Power under Complex Partial Shading Conditions

Abstract

The convenient design of a maximum power point tracking (MPPT) controller is key to the success of photovoltaic (PV) system performance in order to maximize the extracted power, which is affected significantly by weather fluctuations, particularly partial shading condition (PSC). This paper proposes a novel hybrid MPPT approach based on a modified Perturb and Observe (P&O) assisted by the Extremum Seeking Control (ESC) strategy, combining the benefits of these simple algorithms and, meanwhile, eliminating their drawbacks. The proposed algorithm is able to track the maximum possible power under any level of weather fluctuation, with comprehensive enhancement on all aspects of high performance, boosting the PV array efficiency to 100%, reducing the convergence time to less than 100 ms, completely eradicating the oscillations around the achieved power, and maintaining the simplicity levels of both involved strategies. More importantly, this algorithm is applicable for any PV array configuration, which enhances the robustness and novelty of the algorithm. The performance is verified using MATLAB/Simulink. A boost converter is used for controlling DC to DC (direct current to direct current) power. The proposed algorithm’s performance is compared with the conventional P&O and incremental conductance (IC) algorithms under four different cases of weather conditions. The shortcomings of these algorithms are illustrated and the analysis confirms the effectiveness of the proposed algorithm accordingly.

Comparison of Maximum Power Point (PO & GMPP) Algorithm Under Varying Environmental Factors to Extract Maximum Peak Power from Photovoltaic System

“…This paper proposes a novel hybrid MPPT approach on the modified Perturb & Observe (PO) by the assistance of Extreme Seeking Control (ESC) strategy. By comparison of GMPP algorithm, The novel hybrid MPPT is able to track more maximum peak power under any level of the weather fluctuation with the enhancement comprehensive on all aspects of high performance, it even eradicates the oscillations around the power achieved in PV system (Mohammad et al 2020). The multimodal PV characteristics for PV array with variable shading is proposed in this paper, the sequential ESC-based global MPPT control & GMPP algorithms is implemented.…”

Evaluation of a PV-TEG Hybrid System Configuration for an Improved Energy Output: A Review

“…Solar cells' characteristics are modelled as in the current source (Mohammad et al, 2020;Premkumar et al, 2020). The solar cell is the main component for converting the light from the sun energy into electricity via PV effect; it, therefore, serves as a DC source.…”

“…where : ; is current through the diode, q is the electron charge, : < is the reverse saturation current, V is the circuit voltage, and K is the Boltzmann constant (Mohammad et al, 2020). Where Tab is the temperature coefficient, D is the diode diffused factor, ƐI is material bandgap, and A is the area of the solar cell.…”

“…where W X YZ[ W \ are the numbers of series and parallel cells, respectively, ] X is the series resistance, and : \^ is the photocurrent of the solar cell proportional irradiance (Mohammad et al, 2020). where : < is as in Equation 1, : Xc = : \^ at d e = 0, which varies with the open-circuit voltage (d <c ) obtained at Ig = 0 and irradiance of the solar cell (Mohammad et al, 2020) thus,…”

Real-Time Implementation of a New MPPT Control Method for a DC-DC Boost Converter Used in a PEM Fuel Cell Power System

“…Usually, the MPPT algorithms are used for PV and wind systems. Thus, different techniques have been designed including fractional open-circuit voltage (FOCV) [24,25], hill climbing (HC) [25], perturb and observe (P&O) [24,26,27], incremental conductance (INC) [24,27], golden section search (GSS) [28], newton's quadratic interpolation (NQI) [28], extremum seeking control (ESC) [29], sliding mode control (SMC) [27], model predictive control (MPC) [30], fuzzy logic control (FLC) [24,26], backstepping algorithm (BSA) [10], genetic algorithms (GAs) [24,31], particle swarm optimization (PSO) [27], cuckoo search (CS) [32], nature-inspired algorithms (NIA) [25], recurrent learning gradient algorithm (RLGA) [33], flower pollination algorithm (FPA) [34], etc. Although the great research occurred on the MPPT methods, only a few of these techniques have been designed for fuel cell systems.…”