This paper presents a model-based approach for the global maximum power point (GMPP) tracking of solar strings under partial shading conditions. In the proposed method, the GMPP voltage is estimated without any need to solve numerically the implicit and nonlinear equations of the photovoltaic (PV) string model. In contrast to the existing methods in which first the locations of all the local peaks on the P-V curve are estimated and next the place of the GMPP is selected among them, the suggested method estimates directly the GMPP without any need for the evaluation of the other local peaks. The obtained GMPP voltage is then given as a reference value to the input voltage controller of a DC-DC boost converter to regulate the output voltage of the solar string at the GMPP voltage in various irradiation conditions. Furthermore, the values of the temperature and irradiation level of each PV module within the PV string are estimated, and therefore, the proposed method does not need to thermometers and pyranometers. This makes it as a reliable and low-cost GMPP tracking method. The theoretical aspects on which the proposed GMPP algorithm is established are also discussed. The comparison of the numerical results of the suggested GMPP tracking scheme with the existing methods at different environmental conditions shows the satisfactory operation of the proposed technique from the speed and accuracy point of views.

Partial Shading Conditions (PSCs) in Photovoltaic (PV) system represent an inevasible situation that curtails the PV array output by exhibiting multiple peaks in its Power-Voltage (P-V) curve. The multiple peaks consist of a single Global Maximum Power Point (GMPP) and many Local Maximum Power Points (LMPPs). The presence of multiple peaks makes tracking of maximum power point quite difficult and demands an efficient controller to track the global peak of the P-V curve. In the present work, a novel intelligent Asymmetrical Fuzzy Logic Control (AFLC) based Maximum Power Point Tracking (MPPT) algorithm was proposed for tracking GMPP. The fuzzy membership functions of the proposed algorithm were optimized using a heuristic approach. The algorithm was designed, developed, and analyzed using MATLAB/simulink. Furthermore, to establish the superiority of the proposed AFLC algorithm, it was compared with conventional Perturb and Observe (P&O) algorithm and intelligent Fuzzy Logic Control (FLC) based algorithm for GMPP tracking and shading losses under Standard Test Condition (STC) and partially shaded conditions.

The main challenge of photovoltaic (PV) systems is to extract the maximum power from the array, especially when it is partially shaded and subjected to variable weather conditions (sunshine and temperature). To address this challenge, this manuscript proposes a new method based on the Neuro-Fuzzy- Particle Swarm Optimization (NF-PSO) combination. The NF method is used here because it allows an automatic generation of fuzzy rules, and we inject the PSO meta-heuristic at the input of the Neuro-fuzzy to find an optimal gain allowing not only to convert the real input values into fuzzy quantities and to readjust the dynamics of the fuzzy rules by reducing the power losses (oscillations), this combination also provides a simple and robust MPPT scheme to manage efficiently the partial shading, and its convergence to the global maximum power point (GMPP) is independent of the initial conditions of the search process. To confirm the NF-PSO as a viable MPPT option a comprehensive evaluation is performed against two other methods, namely the cuckoo algorithm and the original Neuro-Fuzzy. The simulation results of the system confirmed the better performance of this method in terms of speed with a response time of 0.044s, efficiency with 99.94%, and especially in terms of oscillation reduction with practically a negligible oscillation rate compared to the NF and the Cuckoo algorithm.

This study aims to report on the application of Linear Quadratic Integral (LQI) based global maximum power point tracker (GMPPT) method for transferring the available maximum power from photovoltaic (PV) systems to load in unshaded and shaded conditions. 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 two-level control structure which decreases difficulty in the control process and efficiently deals with the uncertainties in the PV systems is introduced. In the proposed approach, the reference voltage at the global maximum power point (GMPP) is estimated by a new scanning algorithm. The difference between the reference voltage and the voltage of the PV array is then used by LQI controller to generate the duty cycle for a boost converter. The design process of the proposed approach is explained as step by step. The benefits of the approach are quicker tracking capability, transferring maximum deliverable power and simple implementation. To verify the proposed method, several irradiation profiles that create several 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.

Maximum Power Point Tracking (MPPT) is an important concept for both uniform solar irradiance and Partial Shading Conditions (PSCs). The paper presents an Improved Salp Swarm Algorithm (ISSA) for MPPT under PSCs. The proposed method benefits a fast convergence speed in tracking the Maximum Power Point (MPP), in addition to overcoming the problems of conventional MPPT methods, such as failure to detect the Global MPP (GMPP) under PSCs, getting trapped in the local optima, and oscillations around the MPP. The proposed method is compared with original algorithms such as Perturbation and Observation (P&O) method (which is widely employed in MPPT applications), Differential Evolutionary (DE) algorithm, Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The obtained results show that the proposed method can detect and track the MPP in a very short time, and its accuracy outperforms the other methods in terms of detecting the GMPP. The proposed ISSA algorithm has a higher speed and the convergence rate than the other traditional algorithms.

The present paper proposes an adaptive control method for maximum power point tracking (MPPT) in photovoltaic (PV) systems. 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 the extended state-dependent Riccati equation (ESDRE) approach, an optimal law of the controller is derived for the MRAC system to remove the underdamped modes in the PV systems. An algorithm of scanning the P-V curve of the PV array is proposed to seek the global maximum power point (GMPP) in the partial shading conditions (PSCs). It is shown that the proposed control algorithm enables the system to converge to the maximum power point in partial shading conditions in milliseconds.