Global Maximum Power Point Tracking Method Research Articles

A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions

In this paper, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking time and power loss involved in the tracking. In addition, it does not require costly illuminance or temperature sensors and can be realized using a low-cost digital signal controller. According to the simulation results, the proposed method has the best performance among all methods in terms of tracking speed, tracking accuracy, and tracking loss for all the three tested shading patterns (SPs). The simulated results of 252 SPs show that the performance indexes (PIs) of the proposed method are the best among all the compared methods, which are: the average tracking time 0.18 seconds, average power loss 0.01 W. In addition, the proposed method can correctly predict the GMPP positions of all 252 SPs. Furthermore, the PIs of the proposed method are also the best among all the compared methods according to the experimental results, which are: the tracking speed 0.21 seconds, tracking accuracy 99.66%, and tracking loss 12.58 W, all the above are average values.

An enhanced scanning technique for flexible power point tracking under partial shading condition

Flexible power point tracking (FPPT) terminology has recently emergedto support additional functionality for grid-connected systems. Despite having a well-developed operation under uniform irradiance conditions, the FPPT algorithm's operation under partial shading conditions(PSC) reveals substantial challenges. The presented article introduces a global FPPT (GFPPT) algorithm that can operate under the PSC with a fast-trackingspeed. The proposed GFPPT algorithm employs the conventional search-skip-judge global maximum power point trackingmethod and the essential strategic scheme. The method conducts on the left side of the power-voltage curve to avoid the less robust nature ofthe right-sideoperation. Simulation and experimental tests are employed to validate the adequacy of the proposed approach. The simulation and experimental study adopted an indirect control approach using a proportional-integral controller and a buck-boost dc-dc converter. The uniform irradiance condition and PSC patterns with various peaks are chosen, and the presented GFPPT method is applied underdistinct criteria. The proposed method's effectiveness has been adequately validated based on the simulation and experimental outcomes.

GMPPT approach for photovoltaic systems under partial shading conditions using a genetic algorithm

In this paper a new global maximum power point tracking (GMPPT) approach is proposed for a photovoltaic (PV) module using a genetic algorithm (GA) system under partial shading conditions. The partial shading condition is one of the adverse phenomena that the PV system experiences, it is difficult to track true peaks because of existence of multiple peaks in a PV array (partially shaded based). The conventional GMPPT algorithms met in the literature, the initial value of reference is approximated arbitrarily for various conditions of irradiation and temperature and they can’t distinguish between the local and global peaks if partial shading occurs, what reduces the performances of the tracking of the point of optimal operation of PV systems. So, to improve these performances, we proposed a nonlinear based GMPPT method to estimate the initial optimal operating point. To compare with conventional GAs methods, the global peak (GP) tracking process is accomplished after follow the far fewer power perturbation steps. This approach made a possible to largely improve the GP tracking in the PV system, improve the accuracy of the GMPPT algorithms, and accelerate the convergence speed. This GMPPT method is checked for different shading profiles through the simulation and verification.

Efficient Global Maximum Power Point Tracking Strategy for PV Systems

Nowadays, research is focused on optimizing the efficiency of photovoltaic power generation, especially under partial shading or other disruptive events leading to mismatch losses. During most of these events, multiple local maximum power points may occur in the power-voltage characteristics of the PV array, and conventional Maximum Power Point Tracking (MPPT) methods may therefore fail to operate the PV array at the global maximum power point. On the other hand, scanning the entire voltage range requires much time, which results in PV power losses. In terms of reducing the scanning voltage range and improving the tracking performance, this paper proposes a novel Global MPPT (GMPPT) strategy based on a nonlinear controller. The characteristics of a PV array, under such disruptive events, are first analyzed and then the working principle of the proposed strategy elements is described. A dynamic model of the dc-dc converter, interfacing the PV array with the load, is considered in order to ensure better control accuracy and rapidity. The proposed scanning process and the design of the nonlinear controller are then described in detail. In order to validate the designed control strategy, a numerical simulation is carried out using a PV array powered battery charging system. Tracking performances, achieved using the proposed GMPPT method, are then compared with those of a previously proposed one. Simulation results have shown that the proposed strategy is significantly faster and more accurate, thus confirming its viability and efficiency.

A hybrid global MPPT searching method for fast variable shading conditions

Global maximum power point tracking (GMPPT) in case of rapidly variable irregular shading conditions is a complex task, which is exacerbated in case of inexpensive solutions where sensors for acquiring environmental conditions are excluded, that is there is not any information about irradiance and temperature distribution along the photovoltaic system. In such a context, the paper deals with the feasibility study and implementation of a novel easy and cost-effective hybrid two-stages GMPPT algorithm. The first stage synergically combines two different methods to predict the optimal operating condition: an artificial neural network based algorithm and a segmentation-based approach. A traditional hill-climbing method is used in the second stage to finely track the maximum power point. The theoretical aspects of the proposed solution are firstly described, then a wide simulative and experimental tests campaign have been executed to validate the effectiveness of the GMPPT method, confirming that a very simple artificial neural network structure combined with a standard segmentation based method leads to excellent performance for applications where the environmental conditions vary very frequently.

An Enhanced 0.8$\text{V}_\text{OC}$-Model-Based Global Maximum Power Point Tracking Method for Photovoltaic Systems

Under partial shading conditions (PSC), the power-voltage ( P–V ) characteristic curve of a photovoltaic string exhibits multiple peaks, posing a big challenge to the problem of global maximum power point tracking (GMPPT). The traditional 0.8 $\text{V}_\text{OC}$ -model-based GMPPT method locates the global maximum power point (GMPP) locus by comparing the power at each local power peak. However, a considerable amount of time is required for iteratively scanning each 0.8 $\text{V}_\text{OC}$ vicinity. To address this problem, an improved 0.8 $\text{V}_\text{OC}$ -model-based GMPPT method is proposed in this article. A shading vector is introduced to characterize the PSC. The proposed GMPPT method estimates the 0.8 $\text{V}_\text{OC}$ region with the GMPP directly from the measured shading vector by the k -nearest neighbors approach and saves the time consumed in the comparison process used in the conventional method. Simulation and experimental results demonstrate that the proposed method is capable of tracking the GMPP efficiently and accurately under various shading patterns.

Control strategy of grid-connected inverter droop based on GMPPT method

The conductance increment method (INC) can be quickly and accurately stabilized at the maximum power point (MPP) but easily falls into the local extreme point (LMPP) in the shadow environment. Considering the particle swarm optimization (PSO) can quickly achieve global optimal search but the number of iterations is too large when local convergence, leading to fluctuation and energy loss. Therefore, a global maximum power point tracking (GMPPT) is proposed, which combined the PSO and INC and improved them. In this paper, the research object is the single-phase two-stage PV grid-connected system. The GMPPT needs a stable DC voltage (Udc) to track accurately and the grid-connected inverter needs a stable Udc in order to ensure the quality of grid power. Therefore, the goal of the inverter control is to achieve the combination of GMPPT and grid-connected control. Based on the characteristics of PV power generation, a PV grid-connected droop control strategy based on GMPPT is proposed, which realizes the stability of Udc and the output of MPP and meets grid-connected requirements. Finally, the simulation model of PV grid-connected system based on Matlab/Simulink software is used to verify the effectiveness of the proposed control strategy.

Global MPPT Based on Machine-Learning for PV Arrays Operating under Partial Shading Conditions

A global maximum power point tracking (GMPPT) process must be applied for detecting the position of the GMPP operating point in the minimum possible search time in order to maximize the energy production of a photovoltaic (PV) system when its PV array operates under partial shading conditions. This paper presents a novel GMPPT method which is based on the application of a machine-learning algorithm. Compared to the existing GMPPT techniques, the proposed method has the advantage that it does not require knowledge of the operational characteristics of the PV modules comprising the PV system, or the PV array structure. Additionally, due to its inherent learning capability, it is capable of detecting the GMPP in significantly fewer search steps and, therefore, it is suitable for employment in PV applications, where the shading pattern may change quickly (e.g., wearable PV systems, building-integrated PV systems etc.). The numerical results presented in the paper demonstrate that the time required for detecting the global MPP, when unknown partial shading patterns are applied, is reduced by 80.5%–98.3% by executing the proposed Q-learning-based GMPPT algorithm, compared to the convergence time required by a GMPPT process based on the particle swarm optimization (PSO) algorithm.

Enhanced leader adaptive velocity particle swarm optimisation based global maximum power point tracking technique for a PV string under partially shaded conditions

Under partially shaded conditions (PSC), obtaining the global maximum on the multi-peak power-voltage ( P – V ) curve by particle swarm optimisation (PSO) method requires proper tuning of parameters to avoid premature convergence problems. In this study, a new fast tracking global maximum power point tracking (GMPPT) technique for a photovoltaic (PV) string under PSC is proposed. The proposed GMPPT technique has the advantages of two PSO techniques: leader PSO (LPSO) and adaptive velocity PSO (AVPSO). The proposed enhanced leader adaptive velocity PSO (ELAVPSO) GMPPT technique overcomes the limitations of conventional PSO such as premature convergence and difficulty in parameter tuning. Furthermore, a new shading detection scheme is proposed, which accurately finds the type of shading from P – V curve scanning. With the proposed scheme, ELAVPSO GMPPT method is used only to find the global maximum power point during PSC and perturb & observe algorithm is used for maximum power point tracking during uniform irradiance. Under PSC, the limits of the search space of the proposed GMPPT technique can be identified at the time of curve scanning. The proposed algorithm is simulated and experimentally validated by using a boost direct current–direct current (DC–DC) converter prototype.

Fast and precise global maximum power point tracking techniques for photovoltaic system

The multiple power peaks obtained in the power–voltage (P-V) curve of a photovoltaic string under partially shaded condition results in a complicated maximum power point tracking (MPPT) process. Under this condition, the conventional MPPT methods are not acknowledged as they result in false and slow tracking. In this study three novel global MPPT (GMPPT) methods have been proposed and validated. These are named as large and small duty step (LSDS), large and mutable duty step (LMDS) and fast and intelligent GMPPT (FI-GMPPT). The LSDS method sweeps almost the entire P-V curve using a combination of LSDS. Small duty steps are used in predefined areas near all local maximum power points of the P-V curve. LMDS is a further improved method, which uses a combination of LMDSs. The FI-GMPPT is an advance true GMPPT method which limits the area to be swept during the search process. This results in a further reduction in sweep time. In this method, the unnecessary area is skipped during the sweep process. The improved performance of the projected methods has been demonstrated and validated using MATLAB/SIMULINK and hardware implementation.

Global maximum power point tracking method based on sorting particle swarm optimizer

ABSTRACT This paper presents a novel global maximum power point tracking (MPPT) method based on the sorting particle swarm optimizer (PSO). At first, the output characteristics of photovoltaic (PV) array under the partial shading conditions are analyzed. As an artificial intelligence algorithm, the sorting PSO is proposed for global MPPT of PV array under partial shading conditions. The principle of the proposed algorithm is illustrated in detail. Second, a stand-alone PV system based on Z-source inverter (ZSI) is simulated to validate the proposed global MPPT method. Then, the experiments are implemented based on the established ZSI. Compared with the conventional PSO for MPPT, simulation and experimental results both reveal that the proposed global MPPT method based on the sorting PSO can reduce the fluctuation of voltage of PV array during MPPT. Simulation results reveal that 61.53% and 51.81% energy consumption during MPPT is reduced under two different typical partial shading conditions. The proposed MPPT method can also track the global maximum power point more rapidly. Approximately 21.21–39.96% time consumptions during MPPT are saved.

Control strategy of grid-connected photovoltaic generation system based on GMPPT method

There are multiple local maximum power points when photovoltaic (PV) array runs under partial shading condition (PSC).However, the traditional maximum power point tracking (MPPT) algorithm might be easily trapped in local maximum power points (MPPs) and cannot find the global maximum power point (GMPP). To solve such problem, a global maximum power point tracking method (GMPPT) is improved, combined with traditional MPPT method and particle swarm optimization (PSO) algorithm. Under different operating conditions of PV cells, different tracking algorithms are used. When the environment changes, the improved PSO algorithm is adopted to realize the global optimal search, and the variable step incremental conductance (INC) method is adopted to achieve MPPT in optimal local location. Based on the simulation model of the PV grid system built in Matlab/Simulink, comparative analysis of the tracking effect of MPPT by the proposed control algorithm and the traditional MPPT method under the uniform solar condition and PSC, validate the correctness, feasibility and effectiveness of the proposed control strategy.

Fireworks Enriched P&O Algorithm for GMPPT and Detection of Partial Shading in PV Systems

Global maximum power point tracking (GMPPT) strategies for photovoltaic systems are effective under nonuniform irradiation conditions but cause unnecessary power loss due to wide search under uniform irradiance. This paper proposes a scheme for detecting the type of irradiation, viz., uniform or nonuniform, using the trend observed in the string maximum power point (MPP) voltage and current. This partial shading detection scheme is implemented in a GMPPT control strategy combining the conventional perturb and observe (P&O) algorithm and the Fireworks Algorithm (FWA). Under uniform irradiation conditions, the P&O algorithm is used owing to the lower magnitude of power oscillations during tracking as compared to the FWA. On the other hand, under partial shading conditions, the FWA is employed due to its global search and fast tracking capability. The proposed GMPPT method is tested experimentally to verify its effectiveness under different shading patterns.

A Simulated Annealing Global Maximum Power Point Tracking Approach for PV Modules Under Partial Shading Conditions

This paper proposes a simulated annealing (SA)-based global maximum power point tracking (GMPPT) technique designed for photovoltaic (PV) systems which experience partial shading conditions (PSC). The proposed technique is compared with the common perturb and observe MPPT technique and the particle swarm optimization method for GMPPT. The performance is assessed by considering the time taken to converge and the number of sample cases where the technique converges to the GMPP. Simulation results indicate the improved performance of the SA-based GMPPT algorithm, with arbitrarily selected parameters, in tracking to the global maxima in a multiple module PV system which experiences PSC. Experimental validation of the technique is presented based on PV modules that experience nonuniform environmental conditions. Additionally, studies regarding the influence of the key parameters of the SA-based algorithm are described. Simulation and experimental results verify the effectiveness of the proposed GMPPT method.

Global MPPT Scheme for Photovoltaic String Inverters Based on Restricted Voltage Window Search Algorithm

String inverter photovoltaic (PV) systems with bypass diodes require improved global maximum power point tracking (GMPPT) algorithms to effectively reach the absolute maximum power operating point. Several GMPPT algorithms have been proposed to deal with this problem, but most of them require scanning wide voltage ranges of the PV array from nearly zero voltage to open-circuit voltage that increases the scanning time and, in turn, causes energy loss. This paper presents a novel GMPPT method which significantly restricts the voltage window search range and tracks the global power peak rapidly in all shading conditions. Simulation tests and experimental comparisons with another GMPPT algorithm are presented to highlight the features of the presented approach.

What's in your oven?

Global maximum power point tracking (GMPPT) in case of rapidly variable irregular shading conditions is a complex task, which is exacerbated in case of inexpensive solutions where sensors for acquiring environmental conditions are excluded, that is there is not any information about irradiance and temperature distribution along the photovoltaic system. In such a context, the paper deals with the feasibility study and implementation of a novel easy and cost-effective hybrid two-stages GMPPT algorithm. The first stage synergically combines two different methods to predict the optimal operating condition: an artificial neural network based algorithm and a segmentation-based approach. A traditional hill-climbing method is used in the second stage to finely track the maximum power point. The theoretical aspects of the proposed solution are firstly described, then a wide simulative and experimental tests campaign have been executed to validate the effectiveness of the GMPPT method, confirming that a very simple artificial neural network structure combined with a standard segmentation based method leads to excellent performance for applications where the environmental conditions vary very frequently.