Global Maximum Power Point Tracking Research Articles

An enhanced arithmetic optimization algorithm for global maximum power point tracking of photovoltaic systems under dynamic irradiance patterns

ABSTRACT The photovoltaic power system is highly dependent on the quantity of solar irradiance incident on it and its temperature. These parameters are dynamically changing with time due to climatic changes, shadows formed by clouds, trees, buildings, and so on, resulting in multiple maximum power points (MPP). Out of which one is global MPP (GMPP) and the rest are local MPP (LMPP). The GMPP varies dynamically along with the weather conditions and connected load. Many GMPP tracking (GMPPT) algorithms were developed which are inefficient and ineffective under dynamic irradiance conditions. This paper proposes a new enhanced arithmetic optimization algorithm based on the levy flight (AOA-LF) as a GMPPT method, which improves the tracking efficiency and tracking speed because of its good exploration and exploitation due to the long jump with a variable step size. The suggested AOA-LF GMPPT method is implemented in MATLAB/SIMULINK and tested with eight different dynamic irradiance patterns whose tracking curves are compared with the existing GMPPT algorithms like JAYA-LF, DFO, AOA, PSO, and P&O, which reveals that it is excellent in the tracking of GMPP with increment in efficiency as high as 19% when compared with P&O method and the least time to reach the GMPP with percentage decrement as high as 69% as compared to the PSO method. Also, the proposed AOA-LF GMPPT method is validated with the help of OPAL-RT OP4510 Real-Time Digital Simulator. Hence, the proposed algorithm shows superior performance with zero steady-state oscillations and also enhanced exploration and exploitation.

A novel method based on tuna swarm algorithm under complex partial shading conditions in PV system

Under partial shading conditions (PSCs), the output characteristic curve of PV system fluctuates, hence contains multiple peaks. The higher efficiency of the Global Maximum Power Point(GMPP) tracking technique becomes a must to the Photovoltaic(PV) system, therefore, an algorithm based on Tuna Swarm Optimization (TSO) is proposed in this study. Then, it is applied to track the global MPPT of PV system under partial shading conditions, six shading scenarios have been considered. This method is on a PV system consisting of five panels that are formed in a MATLAB/Simulink platform. And then Particle Swarm Optimization(PSO) algorithm, Black Widow Spider(BWO), Squirrel Search Algorithm(SSA) and TSO are compared, the outcomes are analyzed in detail. The proposed algorithm reached maximum power of over 97% under the six PSCs. And the response time in all six scenarios were between 0.2s and 0.4s. This value is good compared to the other three algorithms. Finally, hardware verification demonstrate the performance and feasibility of the proposed MPPT. The proposed method reached maximum power over 91% in the Hardware-in-loop(HIL) and Rapid Control Prototype(RCP) platform.

MPPT and GMPPT Implementation for Buck-Boost Mode Control of Quasi-Z-Source Inverter

The focus is on the maximum power point tracking implementation for the buck-boost voltage mode control of a single-phase multilevel inverter based on a three-level neutral point clamped quasi-Z-source topology. To utilize shoot-through states only when boost function is needed and avoid it in the buck mode, two different control approaches are required. This article proposes merged control system which provides switching between different control algorithms in the buck and boost modes. In particular, a hysteresis band between the buck and boost modes is proposed which provides smooth and stable performance. It also includes features of the implementation of the global maximum power point tracking, which can scan the entire input voltage range in up to 10 s. Experimental results showed that proposed control system provides high and stable maximum power point tracking efficiency in different operation points. Finally, the key features are verified, the advantages, drawbacks, and an eventual potential for other applications are presented in conclusions.

Novel yellow saddle goatfish algorithm for improving performance and efficiency of PV system under partial shading conditions

This paper proposes a novel maximum power point tracking (MPPT) technique based on a new optimization algorithm called yellow saddle goatfish algorithm (YSGA). The photovoltaic system loses a significant amount of power under partial shading conditions (PSC). Because of the presence of bypass diodes, several maximums appear in the Power-Voltage characteristic, making tracking the global maximum power point (GMPP) extremely difficult. A yellow saddle goatfish-based MPPT algorithm is proposed and implemented to address this issue and improve the efficiency of the PV system. This algorithm employs a subgroup research methodology and a very interesting strategy of particle collaboration during GMPP tracking, providing a balance between the exploration and exploitation phases of the research process. To investigate its performance, tests were conducted using six different partial shading patterns and a fast irradiance variation. The simulation was carried out in MATLAB/Simulink using a series of MSX60 solar panels. The results were compared to well-known and efficient algorithms such as the Grey wolf optimization algorithm (GWO), the Bat algorithm (BAT), particle swarm optimization (PSO), the Seagull optimization algorithm (SOA), and the Perturb and observe (P&O) algorithm. The findings demonstrate the proposed method's high robustness, as well as its high dynamic and accuracy in tracking the GMPP. Furthermore, a quantitative and statistical analysis of 100 tests revealed that it outperformed the other algorithms in terms of efficiency, success rate, convergence time, and solution dispersion.

Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking

This paper presents a modified Levy particle swarm optimization (MLPSO) to improve the capability of maximum power point tracking (MPPT) under various partial shading conditions. This method is aimed primarily at resolving the tendency to trap at the local optimum particularly during shading conditions. By applying a Levy search to the particle swarm optimization (PSO), the randomness of the step size is not limited to a specific value, allowing for full exploration throughout the power-voltage (P-V) curve. Therefore, the problem such as immature convergence or being trapped at a local maximum power point can be avoided. The proposed method comes with great advantages in terms of consistent solutions over various environmental changes with a small number of particles. To verify the effectiveness of the proposed idea, the algorithm was tested on a boost converter of a photovoltaic (PV) energy system. Both simulation and experimental results showed that the proposed algorithm has a high efficiency and fast-tracking speed compared to the conventional HC and PSO algorithm under various shading conditions. Based on the results, it was found that the proposed algorithm successfully converges most rapidly to the global maximum power point (GMPP) and that the tracking of GMPP under complex partial shading is guaranteed. Furthermore, the average efficiency for all test conditions was 99% with a tracking speed of 1.5 s to 3.0 s and an average output steady-state oscillation of 0.89%.

Adapted flower pollination algorithm for a standalone solar photovoltaic system

This Extraction of the maximum electrical power from a solar photovoltaic (PV) system under numerous weather conditions is required to reduce its payback time period, per unit energy price, and to compensate for the high initial price of the solar PV system. This could only be achieved by continuously operating the solar PV system at its maximum power point (MPP) under several weather conditions. Unlike under uniform weather conditions (UWC), identification of the real MPP (Global MPP) under partial shading condition (PSC) in a reasonable time is a challenging task due to the formation of multiple local MPP in the power-voltage (P-V) characteristic curve of a solar PV array. The nature-inspired MPP tracking algorithms have been proved suitable for global MPP tracking (MPPT) under PSC. In this research paper, a renowned nature-inspired flower pollination algorithm (FPA) is deeply reviewed, modified, and integrated with the random walk filter to improve its performance in terms of tracking speed, and efficiency. A comparison of the proposed ‘Adaptive Flower Pollination Algorithm (AFPA)’ and conventional FPA algorithm has been made under zero, weak, and strong PSCs for a 4S solar PV array. The proposed algorithm has produced remarkable results in tracking speed, and efficiency, for the global MPP (GMPP) tracking under different PSCs. The simulation is performed in MATLAB/Simulink software.

A Technique for Tracking the Global Peak of PV Arrays During Partially Shaded Conditions Using the Detection of Current Source and Voltage Source Regions of I–V Curves

This article proposes a methodto track the global peak (GP) when the photovoltaic (PV) arrays receive uniform or partial irradiance. The proposed method employs the current–voltage ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> ) curve of the PV generator to track the maximum power point. It can distinguish whether the operating voltage is lying in the voltage source region (VSR) or the current source region (CSR) of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I–V</i> curve using current information at different operating points. It also uses the slope of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I–V</i> curve for discriminating CSR and VSR. This helps the algorithm track the movement of the operating point from VSR to CSR, which further helps in avoiding the false tracking of local peaks (LPs) during global maximum power point tracking. After all LPs are tracked, the LP with maximum power is set as a GP.The performance of the presented algorithm is verified using simulation in the MATLAB/Simulink environment and also supported with experimental results using hardware setup.

Comparative study of modern heuristic algorithms for global maximum power point tracking in photovoltaic systems under partial shading conditions

Under partial shading conditions (PSCs), photovoltaic (PV) generation systems exhibit multiple local and a single global maximum power point. Consequently, global maximum power point tracking (GMPPT) is required to improve the performance of PV systems in such scenarios. This paper comparatively studies and evaluates the tracking performance of modern heuristic-optimization-based GMPPT techniques. Monte Carlo method is used to statistically analyze different methods. Simulation and experimental results indicate that many of the algorithms have difficulties in balancing the explorative and exploitative searching behaviors. Therefore, we propose a variable vortex search (VVS), which is capable of improving the performance of GMPPT by using a variable step size and deterministic starting points. This paper will aid researchers and practical engineers to gain a thorough understanding on how to use modern heuristic algorithms for maximum power out of PV systems. Furthermore, it offers a comprehensive guidance on how to perform efficiently GMPPT in the PV systems under PSCs.

Global Flexible Power Point Tracking in Photovoltaic Systems Under Partial Shading Conditions

Flexible power point tracking (FPPT) aims to regulate the output power of photovoltaic (PV) systems to a predefined value to enable grid support functionalities, such as virtual-inertia provision. Even though partial shading is a common condition in PV systems, especially in small-scale residential PV systems, the current literature, only investigates FPPT control under uniform shading conditions. This article proposes a novel global FPPT (GFPPT) algorithm to provide two functionalities in PV systems under partial shading conditions: first, constant power control (CPC), in which the PV power is regulated to predefined reference value, and second, power reserve control (PRC), under which a constant power reserve is kept in the PV system under steady state to enable grid support under transients. The conventional global maximum power point tracking (GMPPT) algorithm is also modified to adapt with the proposed GFPPT algorithm. The aim of such modification is to obtain faster dynamics after the detection of any environmental changes. Experimental results under dynamically changing partial shading conditions verify the effectiveness of the proposed algorithm in providing CPC or PRC functionalities in PV systems.

Comparative analysis of MPPT techniques for enhancing a wind energy conversion system

One of the most reliable and advanced renewable energy sources is wind energy. It is critical to harness as much wind energy as possible and maintain wind turbines operating at full capacity. Maximum power point tracking (MPPT) is a cutting-edge study that incorporates a variety of approaches. Because each MPPT technique has its own set of advantages and disadvantages, developing an accurate maximum power point tracking methodology for a certain case necessitates understanding. As a result, they must be checked thoroughly. This research tries to examine many algorithms that can be used to improve the wind energy system’s global MPPT performance. The traditional “Perturb and Observe” tool, the optimization method based on the “particle swarm optimization algorithm,” the neural network, and the “fuzzy logics” as intelligent tools are these techniques. The main objective of this research is to define and evaluate four different flexible algorithms that achieve the fundamental objective of this optimization. The advantages, drawbacks, and thorough analysis of MPPT systems are highlighted in terms of initial investment, responsiveness, and capacity to create maximum energy output. All of this comparison was made through simulation software, which is the MATLAB Simulink tool. The conclusions are supported by a comprehensive discussion and presentation of the results for a variety of situations and tests that reflect real-world behavior in any wind system.

A fast and accurate sine-cosine MPPT algorithm under partial shading with implementation using arduino board

In this paper, we propose a fast and accurate algorithm for global maximum power point tracking (GMPP) based on the sine-cosine algorithm (SCA) of a photovoltaic system (PV) for various weather changes. The objective of this algorithm is to improve the tracking performance of the MPP controller, for optimal power extraction from a PV system under partial shading conditions (PSC). The performance of the proposed SCA algorithm was tested and compared to other algorithms such as perturb and observe (P&O) and the salp swarm algorithm (SSA) under static and dynamic PSC. The simulation results show the superiority and efficiency of the PV system controlled by the SCA algorithm, which outperforms other algorithms in fast and accurate GMPP tracking, fast response time, accelerated convergence speed, very low initial oscillation, very low MPP steady-state oscillation and PV system efficiency that reaches 99.94% under different weather PSC. Moreover, the proposed SCA algorithm is validated by following the V-cycle validation and verification process by integrating the generated embedded software on an onboard “Arduino MKR ZERO board”, which is used as a digital platform for implement the proposed SCA algorithm.

A Fast-Speed GMPPT Method for PV Array Under Gaussian Laser Beam Condition in Wireless Power Transfer Application

The laser power transfer system is one of the most promising systems in the long-range wireless power transfer field. However, the low efficiency at photovoltaic (PV) receiver limits the performance of an implemented system due to the Gaussian laser beam. Since the irradiance profile of the Gaussian laser beam is not uniform, the PV array exhibits complex output characteristics with multiple peaks, leading that the conventional maximum power point tracking (MPPT) method is not acceptable either on tracking accuracy or on tracking speed. In this article, a simple and rapid voltage-location global MPPT (VL-GMPPT) method is proposed for PV array under Gaussian laser beam condition (GLBC) in terms of reducing the voltage searching range. In order to do so, the mathematical model of PV array's global maximum power point (GMPP) is developed under GLBC. Then, the VL-GMPPT method incorporates voltage-location mechanism to directly move the operating point to the vicinity of GMPP and employs IncCond method to accurately find the GMPP. The experimental and simulation results are shown to verify the proposed GMPPT method. It was found that the proposed method can reduce 90% of tracking time that is consumed by the conventional global searching method.

Reference-Voltage-Line-Aided Power Incremental Algorithm for Photovoltaic GMPPT and Partial Shading Detection

Conventional global maximum power point tracking (GMPPT) algorithms show limited tracking speed and accuracy due to the unnecessary peak search considering the complexity of partial shading conditions (PSCs). This paper proposes a novel power incremental (PI) algorithm for photovoltaic GMPPT and partial shading detection with the aid of the reference voltage line (RVL), representing the voltage of possible peaks based on the derived analytical expressions. The proposed analytical solution can detect the occurrence of PSCs easily and never overlook GMPP. Furthermore, both the tracking speed and the efficiency can be improved due to the quick allocation of GMPP and the reduced search area. The proposed RVL-PI-GMPPT algorithm is straightforward without sophisticated searching iterations, which can reduce the computational burden. Combined with the low-cost feature without additional temperature or irradiance sensors, the proposed algorithm is very suitable for the practical engineering design. Both the simulations and experiments under various PSC patterns validated the effectiveness of the proposed algorithm. Conducted fair comparisons with other popular GMPPT methods, the average percentage change of tracking time by implementing the proposed RVL-PI-GMPPT algorithm under three-peaks PSC patterns is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bf 108.9\%$</tex-math></inline-formula> compared with the PSO method and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bf 209.9\%$</tex-math></inline-formula> compared with the 0.8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V_{oc}$</tex-math></inline-formula> model method, respectively.

A Bayesian Multiobjective Approach Based on GMPPT for PV Arrays

This paper considers the problem of tracking the global maximum power point (GMPP) in partially shaded conditions (PSCs) as a multiobjective optimization problem and solves it using a novel multiobjective optimization algorithm on the basis of Bayesian optimization formulation. Bayesian optimization is a metamodel-based global optimization method that is able to balance exploration and exploitation. The Pareto solutions are obtained by using a multiobjective Bayesian optimization algorithm. Also, a new acquisition function is proposed to improve the diversity and convergence of the Pareto solutions. Two objective functions are introduced to remove the large tracking errors and oscillations of the operating point around the GMPP. The suggested method is implemented online for GMPP tracking so that the suggested method monitors any change in environmental conditions and generates the optimal duty cycle for the DC-DC converter for the GMPP tracking (GMPPT) by the PV array. Several multipeak PSC scenarios are implemented and simulated to show efficiency of the suggested approach. The MATLAB/SIMULINK is employed to implement a photovoltaic (PV) system comprising a PV array, a boost converter, and the proposed multiobjective Bayesian optimization algorithm (MOBOA). The simulation results show a very satisfactory performance of the MOBOA in terms of transient state and steady-state oscillations and tracking speed.

A novel global maximum power point tracking algorithm based on Nelder-Mead simplex technique for complex partial shading conditions

In this study, a novel global maximum power point tracking (GMPPT) algorithm for photovoltaic generation system (PGS) operating under complex partial shading conditions is proposed. The presented GMPPT technique is based on Nelder-Mead (NM) simplex technique, which is commonly used to solve complicated optimization problems and has advantages such as simple implementation, derivative-free nature, fast convergence, and high accuracy. In this study, simulation results of 252 shading patterns with non-repeatable irradiance levels and 243 shading patterns under repeatable irradiance levels are provided to validate the effectiveness of the proposed GMPPT methods. According to the simulated and experimental results, the presented NM-based GMPPT approach has the highest success rate and tracking accuracy under all the tested shading patterns compared with other GMPPT methods.

A hybrid genetic algorithm and grey wolf optimizer technique for faster global peak detection in PV system under partial shading

In the present scenario, renewable energy is the fastest growing trend in the energy sector. Chief among the renewable power sources is solar photovoltaic (PV) technology. One of the major problems of solar PV systems is the occurrence of a partial shadow over the PV array, which generates multiple peaks in the power versus voltage (P-V) characteristics of the PV array. Under these conditions, conventional Maximum Power Point Tracking (MPPT) techniques tend to get stuck on local peaks and fail to track the Global Maximum Power Point (GMPP). In this paper, an improved hybrid optimization algorithm named as Genetic Algorithm-Grey Wolf Optimizer (GA-GWO) algorithm, which combines the abilities of the Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO) algorithms, has been proposed to track the global peak. The proposed system has been modeled and examined using MATLAB/Simulink software for various partial and uniform shading conditions. It has also been compared with previously existing GWO and GA algorithms. The proposed algorithm is found to offer faster and better tracking of GMPP than both GWO and GA algorithms.

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.

An algorithm to extract the maximum power from the PV-based generation systems under non-uniform weather

This paper presents a fast and simple algorithm to extract the maximum power under non-uniform weather from the photovoltaic (PV) based generation systems. The proposed algorithm’s three stages are the scanning stage, the tracking stage, the detecting and avoiding the hidden points stage. The hidden points are caused by a transition between the global maximum power point (GMPP) and a local maximum power point (LMPP) when the partial shading conditions (PSCs) are changed. This transition cannot be observed by monitoring only the power difference of the PV generation system. Simulation results with comparisons to other algorithms developed for global maximum power point tracking (GMPPT) under PSCs are provided to clarify and show the effectiveness of the proposed GMPPT algorithm. The average tracking speed of the proposed algorithm is two times faster than the compared MPPT algorithms, with about 2% more power generated with no additional cost. Moreover, the proposed GMPPT algorithm is implemented in real-time using National Instruments (NI) CompactRIO in field-programmable gate array (FPGA) mode to confirm the applicability of the proposed work.

Modified Current Control for Tracking Global Peak Under Fast Changing Partial Shading Conditions

The power voltage (P-V) characteristics of photovoltaic (PV) systems exhibit multiple power peaks under partially shaded conditions. Several global maximum power point tracking (GMPPT) algorithms in the literature recognize the irradiance change, only after the convergence of operating point to global peak, or use additional hardware to call GMPPT subroutine at definite time intervals to detect any insolation change, and thus track the global peak. However, during fast changing partial shading conditions, these methods are less effective, as they do not detect any irradiance change during the tracking phase of any shading pattern. This paper proposes a novel modified current control approach that uses current as a parameter to detect the insolation change during the tracking phase and track the global peak under fast changing partial shading conditions without any additional hardware. The proposed technique improves the tracking efficiency by as much as 39%, thus proving to be effective under fast changing partial shading conditions. The superior tracking performance of the proposed algorithm over the existing techniques in terms of its tracking efficiency, dynamic tracking capability, tracking speed, and convergence to the global peak is demonstrated with extensive simulations using MATLAB/Simulink and experimental results.

A Hybrid Particle Swarm Optimization and Single-Objective Slap Swarm Optimization Algorithm Based MPPT Strategy

Under inhomogeneous irradiance conditions, multiple peak characteristic of P-V curve reduces the efficiency of conventional trackers like incremental conductance control. A hybrid particle swarm optimization and single-objective slap swarm optimization algorithm (PSO-SOSSOA) based which is applied to maximum power point tracking (MPPT) strategy is presented. The PSO-SOSSOA adopts the particle swarm optimization algorithm in the early period of the algorithm operation and a modified single-objective slap swarm optimization algorithm (SOSSOA) in the remaining period. The simulation results reveal that the PSO-SOSSOA can successfully deal with global MPPT and exhibits performance advantages in terms of efficiency, search speed, and power oscillation compared with the SOSSOA.