Global Maximum Power Point Tracking Technique Research Articles

Novel Global MPPT Technique Based on Hybrid Cuckoo Search and Artificial Bee Colony under Partial-Shading Conditions

Under partial-shading conditions (PSCs), the output P-V curve of the photovoltaic array shows a multi-peak shape. This poses a challenge for traditional maximum power point tracking (MPPT) algorithms to accurately track the global maximum power point (GMPP). Single intelligent algorithms such as PSO and ABC have difficulty balancing tracking speed and tracking accuracy. Additionally, there is significant power oscillation during the tracking process. Therefore, this paper proposes a new hybrid method called the Cuckoo Search Algorithm and Artificial Bee Colony algorithm (CSA-ABC) for photovoltaic MPPT. The CSA-ABC algorithm combines the local random walk and the global levy flight mechanism of the cuckoo algorithm, by probability selection, to decide whether to group the population, and introduces adaptive weight factors and gravitational mechanisms between adjacent individuals, incorporating an algorithm restart mechanism to track new MPPs in response to changes in the external environment. The algorithm is implemented in MATLAB/Simulink using a photovoltaic power-generation system model. Simulation verification is performed under different PSC scenarios. The results show that the proposed MPPT algorithm is 6.2–78.6% faster than the PSO, CSA, and ABC algorithms and two other hybrid algorithms, with a smaller power oscillation during the tracking process and zero power oscillation during the steady process.

Fast indirect GMPPT method for PV systems under uniform and partial shading conditions

Under Partial Shading Conditions (PSCs), conventional MPPT techniques fail to locate the Global Maximum Power Point (GMPP) for PV generators, and when PSCs change suddenly and repetitively, several GMPP tracking techniques takes time to find or miss the target. To overcome these shortcomings, this paper proposes a new and fast technique that can identify and catch very quickly the GMPP. Due to the use of a PID controller, the PV system is improved in terms of response time and becomes very fast. On the other hand, the proposed algorithm is developed upon other known algorithms and enhanced in order to identify the occurrence of PSCs and to find the GMPP. The measured points during identification and searching process are reduced which increases the power efficiency of the PV system. The time required for the algorithm to catch the GMPP is minimized by 25% compared with other works. To examine the performance of the system a hard scenario, that contains several uniform and partial shading conditions, is used. The simulation is implemented in Matlab/Simulink. The obtained results show clearly the advantage of the proposed technique over others.

A global MPPT technique for PV systems under partial shading conditions

ABSTRACT Maximum Power Point Tracking (MPPT) algorithms are used to obtain maximum power from PV (Photovoltaic) systems in variable atmospheric conditions. Partial shading conditions (PSCs) have an important effect causing the maximum power points of PV systems to change. In this study, a new hybrid MPPT algorithm was proposed by combining Voltage Scanning (VSC) and Cuckoo Search Algorithm (CSA) methods to obtain maximum power from a PV system running under PSC. To test the proposed method, a 252.6 kW grid-connected PV system with 609 panels, including 7 serial panels and 87 parallel strings, was created in the MATLAB/Simulink environment. This system was tested under 10 different PSCs by using VSC, Particle Swarm Optimization (PSO), CSA, and the proposed MPPT methods. The average tracking efficiency and speed of the proposed method under these conditions were obtained as 99.74% and 0.3s, respectively. Under PSCs, the proposed method achieved the highest tracking efficiency of 99.92%, while the highest tracking speed that it achieved was 0.22s. On the other hand, the superiority of the proposed hybrid MPPT algorithm was clearly demonstrated by comparing it to the VSC, PSO, and CSA methods.

Hardware implementation of a novel hybrid MPPT technique for fast tracking of GMPP in solar PV system under PSCs

PurposeTo generate electricity, solar photovoltaic (PV) systems are among the best, most eco-friendly and most cost-effective solutions available. Extraction of maximum possible electricity from the solar PV system is complicated by a number of factors brought on by the ever-changing weather conditions under which it must operate. Many conventional and evolutionary algorithm-based maximum power point tracking (MPPT) techniques have the limitation of not being able to extract maximum power under partial shade and rapidly varying irradiance. Hence, the purpose of this paper is to propose a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid bridge link-honey comb (BL-HC) configured PV system to enhance the better maximum power during dynamic and steady state operations within less time.Design/methodology/approachIn this method, a hybridization of two algorithms is proposed to track the true with faster convergence under PSCs. Initially, the slime mould optimization (SMO) algorithm is initiated for exploration of optimum duty cycles and later P&O algorithm is initiated for exploitation of global duty cycle for the DC–DC converter to operate at GMPP and for fast convergence.FindingsThe effectiveness of the proposed HSMO MPPT is compared with adaptive coefficient particle swarm optimization (ACPSO), flower pollination algorithm and SMO MPPT techniques in terms of tracked GMPP, convergence time/tracking speed and efficacy under six complex partial shading conditions. From the results, it is noticed that the proposed algorithm tracks the true GMPP under most of the shading conditions with less tracking time when compared to other MPPT techniques.Originality/valueThis paper proposes a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid BL-HC configured PV system enhance the better maximum power under partial shading conditions (PSCs). This method operated in two stages as SMO for exploration and P&O for exploitation for faster convergence and to track true GMPP under PSCs. The proposed approach largely improves the performance of the MPP tracking of the PV systems. Initially, the proposed MPPT technique is simulated in MATLAB/Simulink environment. Furthermore, an experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution.

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.

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.

Modified honey badger algorithm based global MPPT for triple-junction solar photovoltaic system under partial shading condition and global optimization

In the recent era, Metaheuristic Algorithms (MHs) are developed to tackle many and different optimization problems. The merit of the MHs is not only its simplicity to understand but also its easiness of implementation which helps in tackling different and various real-world applications. Honey Badger Algorithm (HBA) is one of the recent MHs. Although the success of the HBA algorithm in solving complex problems with high dimensions, it suffers from several drawbacks such as; 1) being trapped in local optima issue, 2) low convergence, and 3) the imbalance between exploration and exploitation stages. Therefore, an efficient local search called Dimensional Learning Hunting (DLH) is injected to the HBA to tackle these drawbacks, the proposed method named mHBA. On the other side, the demand of the PV systems (PVSs)’ installations is rapidly increasing from a day to the next. Due to the intermittency of the weather, the PV array performs as a nonlinear component in terms of its output characteristics. Partial shading is an environmental phenomenon that prevents all or part of the light to be exposed to the PV cells. This phenomenon makes the cell's output power curve fluctuates and hence contains multiple peaks and has an essential impact on the entire output of the PV system. However, identifying the peak that has the global maximum output power is the main target of most of the conventional maximum power point tracking (MPPT) methods. In this context, the higher efficiency of the global MPPT technique becomes a must to operate the PV cells as close as possible to the global MPPT. Accordingly, the performance of the proposed mHBA is assessed based on the complex 2020 IEEE Congress on Evolutionary Computation (CEC′20) test suite. Then, it is applied to track the global MPPT of PV system-based triple-junction solar cells (TJSCs) under partial shading conditions, four shading scenarios have been considered. The results of the proposed mHBA are compared with common MHs including Gray wolf optimizer (GWO), Moth-flame optimizer (MFO), Whale optimization algorithm, Sine cosine algorithm (SCA), Salp swarm algorithm (SSA), Tunicate swarm algorithm (TSA), Sooty Tern Optimization Algorithm (STOA), and the original HBA. The findings of this research proved the effectiveness and robustness of the proposed mBHA for solving the global optimization problems of the MPPT as an engineering application.

A novel global MPPT technique using improved PS-FW algorithm for PV system under partial shading conditions

Under partial shading conditions (PSC), conventional Maximum Power Point Tracking (MPPT) algorithms are unable to reach the Maximum Power Point (MPP) due to non-linear characteristics of the curve. In the literature, some algorithms, such as Fireworks algorithm (FWA) and Particle Swarm Optimization (PSO), have been used to obtain the MPP. However, they provide less than optimal convergence rate and tracking accuracy of FWA which impair the MPPT performance. Therefore, a novel algorithm for Global Maximum Power Point Tracking (GMPPT) under PSC is proposed in this paper to track the single global MPP (GMPP). In particular, an application of the hybrid method of PSO and FWA (PS-FW) algorithm for GMPPT is proposed. The difficulty of balancing exploration and exploitation is alleviated within the PS-FW through the PSO velocity operator and the FWA mutation and explosion sparks operator. The population health is maintained through abandonment and supplement strategy and an adaptive modification to the operators to enforce convergence is also described. The proposed GMPPT algorithm performance is first verified within a simulation environment under four partial shading patterns. PS-FW is further validated using experimental demonstration. The measurement results show that our proposed GMPPT algorithm can achieve better performance than their singular algorithm counterparts. The proposed algorithm successfully obtains a minimum of 18.51% better tracking speed of the GMPP under simulation verification at one initial population setting. The PS-FW under experimental verification is able to achieve at least a minimum of 23.45% better tracking speed of the GMPP in two initial population settings.

A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions

The partial shading condition (PSC) makes it challenging for the PV system to harvest maximum power via maximum power point tracking (MPPT). Various MPPT algorithms based on bio-inspired optimization methods were proposed in the literature. The mechanism employed by these algorithms varies from one to another, making them perform differently when tracking the GMPP. This paper introduces a novel MPPT technique based on the improved squirrel search algorithm (ISSA). The performance of the proposed ISSA improved the tracking time by 50% in comparison with the conventional SSA algorithm. Similarly, the proposed method has also been compared with popular Genetic algorithm (GA), and particle swarm optimization (PSO). The results proved the ability of the proposed algorithm in tracking the GMPP with faster convergence and fewer power oscillations in comparison. The feasibility and effectiveness of the proposed ISSA based MPPT have been validated experimentally, and the results clearly demonstrate its capability in tracking the GMPP with an average efficiency of 99.48% and average tracking time of 0.66 s.

Global Hybrid Maximum Power Point Tracking for PV Modules Based on a Double-Diode Model

Photovoltaic systems under partial shading conditions (PSC) need to operate at the global maximum power point (GMPP) to extract the maximum possible power. Partial shading is characterized by the exhibition of a multi-peaked behavior in the voltage power (P-V) curve. Hence, there are several methods capable of extracting the GMPP from a multi-peaked P-V curve with various distinct characteristics. However, it is important that these techniques have fast dynamic responses to reduce power losses during the GMPP searching process. To fill this very important research gap by improving performance, an efficient hybrid global maximum power point tracking (GMPPT) technique combining a fast double-diode model-based technique and the classic Perturb and Observe (P&O) is proposed. The double-diode model used was modeled for a large number of series and parallel connected modules in a shaded photovoltaic array, which can be used as a valuable tool in the practical evaluation of a photovoltaic installation under PSC. The proposed method accurately tracks the global maximum power point at any operation point with good tracking speed and excellent efficiency. In addition, simulation and experimental results are presented to demonstrate its performance and comparative results with two other GMPPT techniques.

Stochastic Fractal Search Optimization Algorithm Based Global MPPT for Triple-Junction Photovoltaic Solar System

A significant growth in PV (photovoltaic) system installations have been observed during the last decade. The PV array has a nonlinear output characteristic because of weather intermittency. Partial shading is an environmental phenomenon that causes multiple peaks in the power curve and has a negative effect on the efficiency of the conventional maximum power point tracking (MPPT) methods. This tends to have a substantial effect on the overall performance of the PV system. Therefore, to enhance the performance of the PV system under shading conditions, the global MPPT technique is mandatory to force the PV system to operate close to the global maximum. In this paper, for the first time, a stochastic fractal search (SFS) optimization algorithm is applied to solve the dilemma of tracking the global power of PV system based triple-junction solar cells under shading conditions. SFS has been nominated because it can converge to the best solution at a fast rate. Moreover, balance between exploration and exploitation phases is one of its main advantages. Therefore, the SFS algorithm has been selected to extract the global maximum power point (MPP) under partial shading conditions. To prove the superiority of the proposed global MPPT–SFS based tracker, several shading scenarios have been considered. The idea of changing the shading scenario is to change the position of the global MPP. The obtained results are compared with common optimizers: Antlion Optimizer (ALO), Cuckoo Search (CS), Flower Pollination Algorithm (FPA), Firefly-Algorithm (FA), Invasive-Weed-Optimization (IWO), JAYA and Gravitational Search Algorithm (GSA). The results of comparison confirmed the effectiveness and robustness of the proposed global MPPT–SFS based tracker over ALO, CS, FPA, FA, IWO, JAYA, and GSA.

Global maximum power point tracking technique based on adaptive salp swarm algorithm and P&O techniques for a PV string under partially shaded conditions

ABSTRACT A Bio-inspired salp swarm algorithm (SSA) based soft computing technique is easy to implement with a single control parameter, which is dependent on maximum iteration count. But the maximum iteration count required for the on-line/hardware search process like maximum power point tracking (MPPT) of photovoltaic (PV) system is not known initially. Wrongly estimated maximum iteration count results in the unnecessary exploration of search space even after reaching the global peak (GP) region, which is the main cause for the slow-tracking of global maximum power point (GMPP) by conventional SSA. Hence, In this paper, a modified SSA based MPPT is proposed with an adaptive control parameter, and it is named Adaptive SSA (ASSA) based global maximum power point tracking (GMPPT) technique. Further, to achieve faster tracking of GMPP, ASSA is used only to identify the GP region, and in that region, variable step size P&O continues tracking. During irradiance changes, search space is limited for the reinitialization of ASSA. The proposed hybrid algorithm is simulated and experimentally validated using a boost DC-DC converter as an MPPT controller with direct duty ratio control. The proposed GMPPT technique results in faster-tracking of GMPP with the combined effect of adaptive parameter tuning, GP region identification with ASSA, and variable step size P&O. From the simulation and experimental results, it is evident that the proposed GMPPT technique is more than 1.3 times faster as compared to the conventional SSA based MPPT technique.

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.

A Fast GMPPT Algorithm Based on PV Characteristic for Partial Shading Conditions

Photovoltaic (PV) modules experience some partial shading conditions (PSC) due to some various factors. In that kind of a condition, a few maximum power points (MPPs) possibly appear on the power-voltage (P-V) curve, which increases the tracking difficulties. It is known that maximum power point tracking (MPPT) may not be realized by hill climbing (HC) based conventional MPPT algorithms under PSCs. In this context, this paper presents a novel micro converter based algorithm that was developed by using P-V characteristics of PV modules. Unlike voltage or duty ratio scanning techniques, this paper introduces a new deciding method to determine the correct global MPP (GMPP) region. For this, the proposed method uses some duty ratios that were calculated corresponding to each MPP region. Thus, the initialization of duty ratio is done properly, which results in high tracking speed and accurate tracking of the GMPP. The other advantages of the proposed algorithm are structural simplicity, less computational burden, and ease of implementation with a basic microcontroller. The simulation results show that this algorithm has fast tracking capability and it manages to track GMPP for PSCs correctly, since it includes an artificial scanning procedure. Single ended primary inductance converter (SEPIC) is built in order to validate the proposed global maximum power point tracking (GMPPT) algorithm. The performance of the proposed GMPPT technique is verified by experimental studies. The results show that the proposed GMPPT technique is fast by up to five times than an adaptive full scanning strategy and improved IC algorithm. Furthermore, the proposed algorithm can be commercially used in micro converters, since it is compatible with small number of bypass diodes in a module.

Combined Tracking of the Maximum Power and Maximum Efficiency Operating Points for Real-Time Maximization of the Energy Production of PV Systems

The power production of a photovoltaic (PV) system can be maximized by applying a global maximum power point tracking (MPPT) method for ensuring that the PV array operates at the global MPP where its power production is maximized and, simultaneously, by dynamically maximizing the efficiency of the power converter that interfaces the PV-generated energy to the energy storage devices or the electric grid. In this paper, a new control method has been presented for the real-time maximization of the power production of PV systems by modifying in real time the duty cycle and switching frequency of the power converter PWM control signal. The proposed method has the advantage that it can be applied in PV systems operating under partial shading conditions. Also, knowledge of either the operational characteristics and configuration of the PV modules within the PV array or the power converter operational characteristics, is not required for its application. The experimental results validated that by using the proposed method, the power production of the PV system is increased significantly compared to the case that only the power production of the PV array is maximized by using a global MPPT technique without tracking the optimal value of the power converter efficiency.

An Improved 0.8 VOC Model Based GMPPT Technique for Module Level Photovoltaic Power Optimizers

Photovoltaic modules may experience some mismatching conditions that affect their available power capacity, causing inefficient maximum power point tracking (MPPT). Furthermore, their power–voltage ( P–V ) characteristic curve becomes a multi-peak structure in such conditions owing to the presence of bypass diodes included since every part of the modules may receive different solar irradiance. By taking into account these facts, this paper introduces an improved global MPPT technique comprising 0.8 VOC model and the limited and adaptive scan approach. The proposed technique eliminates the requirement of some threshold value, leading to unreliable operation which the classical 0.8 VOC model based studies suffer from. Furthermore, the tracking time has been reduced substantially with the proposed technique by limiting scanning interval. Performance of the proposed technique has been verified by experimental studies and compared with classical 0.8 VOC model and a full scanning technique, which have been already presented in the literature. Experimental results show that the proposed technique is simple to be implemented and it has better performance than classical 0.8 VOC model, full scanning, and perturb and observe algorithms. Therefore, the proposed technique is feasible and it can be used technically in module level distributed MPPT (DMPPT) applications.

Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance

This paper proposes an accurate global maximum power point tracking (MPPT) technique based on Wind Driven Optimization (WDO) algorithm for photovoltaics system (PVS) under non-uniform solar irradiance. Under non-uniform solar irradiance or partial shading conditions (PSC), the power-voltage curve of the PVS has several maximum power points (MPPs), one of which is assumed to be the global peak. This condition complicates the optimal MPP tracking and decreases of the PVS efficiency. Conventional MPPT methods, such as Perturb & Observe, Hill Climbing and Incremental Inductance are likely to be trapped at one of the local MPPs. The major disadvantage of these methods is their incompetence to specify the global MPP under the above mentioned situation. To overcome this problem, global MPPT techniques based on modern optimization algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Harmony Search Algorithm (HSA), Bat Algorithm (Bat), Sine-Cosine Algorithm (SCA), Cuckoo Search (CS), and Genetic Algorithm (GA) are previously developed. The main contribution of this research is to introduce a novel global MPPT based on WDO algorithm. A comprehensive statistical evaluation of WDO, PSO, DE, HSA, Bat, SCA, CS and GA is carried out under different scenarios of shadowing conditions. Seven statistical metrics including the relative error, root mean square error, mean absolute error, standard deviation, success rate, convergence time and efficiency are used to evaluate the powerfulness and the feasibility of the proposed compared to the others. Therefore, the proposed MPPT based on WDO algorithm is considered to be the most effective and superior optimization tool compared to the corresponding ones.

A Hybrid Global Maximum Power Point Tracking Technique With Fast Convergence Speed for Partial-Shaded PV Systems

Photovoltaic (PV) systems exhibit multiple local and one global maximum power points (MPPs) in their P – V and I – V curves during partial shading conditions (PSC). Thus, to improve the efficiency of the system, a global maximum power point tracking (GMPPT) algorithm is necessary. This paper presents a hybrid GMPPT algorithm for constant voltage load applications using a single current sensor. The proposed method combines single current sensor hill climbing (SSHC) and artificial bee colony (ABC) algorithms to track the GMPP. The SSHC algorithm detects the event of PSC and tracks the MPP during uniform insolation conditions. The output current of the power electronic interface is measured effectively at selective duty cycles to identify the type of P – V curve pattern and, thus, initiate either SSHC or ABC. The search space for the ABC algorithm is reduced in the proposed technique to improve the convergence speed. The proposed GMPPT technique is simulated in MATLAB and validated through experimental prototypes for various PSCs. The proposed algorithm tracks the GMPP with excellent efficiency and fast speed.

A novel power-increment based GMPPT algorithm for PV arrays under partial shading conditions

Due to dust, structural interfering from surrounding buildings or trees, partial shading conditions (PSCs) are frequently occurred in photovoltaic (PV) arrays, which affects the generated power and system reliability significantly. Under PSCs, PV arrays exhibit multiple local maximum power points (LMPPs), which make the conventional maximum power point tracking (MPPT) algorithms difficult to quickly allocate the optimal operating point with the maximum output power. In order to solve this issue, a novel power-increment based global MPPT (GMPPT) algorithm is proposed by combining the voltage line, the load line, and the power line altogether in determining the tracking direction and the step size. The proposed algorithm retains the advantages of the conventional power incremental based GMPPT technique. Moreover, it can realize a successful convergence to the GMPP under any pattern of PSC, which is difficult to accomplish for some GMPPT algorithms. It simplifies the control implementation since it is not necessary to know exactly the internal connection of the PV array for the practical implementation of the algorithm. Furthermore, the proposed algorithm shows improved tracking speed and higher accuracy than other GMPPT techniques. It directly regulates the duty-cycle of the power interface rather than the output power command. Thus, the circuit design becomes easier. Finally, various partial shading scenarios are evaluated experimentally in order to validate the effectiveness of the proposed algorithm.