Hybrid Maximum Power Point Tracking Technique Research Articles

Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications

Solar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their cleanliness, high performance, and high dependability. The efficacy of photovoltaic systems is impacted by several elements, including geographical location, positioning, shadowing effects, and local climate conditions. In order to fulfil the demands of loads, an interleaved boost converter is utilized, which has a reduced number of filters with less stress on the devices. Solar powered systems employ several maximum power point tracking (MPPT) methodologies. However, when there is partial shading, many power peaks arise, which complicates the identification of the overall peak. Although MPPT approaches are designed to measure and maintain the global maximum power point (GMPP), there are still significant oscillations observed around the GMPP with subpar settling time, tracking efficiency, and conversion efficiency. In this work, novel hybrid MPPT technique called artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) method and water cycle optimization based perturb and observe (WCO-PO) have been proposed. Artificial neural network (ANN) has been used to determine the best scaling factor in ANN-ASSPO MPPT. Performance is enhanced in ANN-ASSPO MPPT by using the optimum scaling factor, particularly in situations when the irradiance is rapidly changing/partial shading conditions. Similarly, in WCO-PO MPPT water cycle optimization is used to determine the peak power when the PV panel is subjected to partial shading conditions. The performances of proposed hybrid MPPT ANN-ASSPO and WCO-PO techniques have been compared in terms of power generated, output voltage, average settling time and conversion efficiency. The MATLAB/Simulink tool is employed to carry out the experiment for this study.

Grey wolf optimization and incremental conductance based hybrid MPPT technique for solar powered induction motor driven water pump

The use of Solar Powered Water Pumps (SPWP) has emerged as a significant advancement in irrigation systems, offering a viable alternative to electricity and diesel-based pumping methods. The appeal of SPWPs to farmers lies in their low maintenance costs and the incentives provided by government agencies to support sustainable and cost-effective agricultural practices. However, a critical challenge faced by solar photovoltaic (PV) systems is their susceptibility to power loss under partial shading conditions, which can persist for extended periods, ultimately reducing system efficiency. To address this issue, this paper proposes the integration of Maximum Power Point Tracking (MPPT) controllers with efficient algorithms designed to identify the peak power during shading events. In this study, a hybrid approach combining Grey Wolf Optimization (GWO) and Incremental Conductance (INC) is employed to maximize the power output of SPWPs driven by an induction motor under partial shading conditions. In order to achieve faster convergence to the global peak, GWO handles the first stages of MPPT and then INC algorithm is employed at the end of the MPPT process. This method reduces the computations of GWO and streamlines the search space. The paper evaluates the performance of the induction motor in terms of speed settling time and torque ripple. To validate the effectiveness of the GWO-INC hybrid approach, simulations are conducted using the MATLAB Simulink platform. The outcomes are then compared with results obtained from various well-known approaches, including Particle Swarm Optimization – Perturb and Observe (PSO-PO), PSO-INC, and GWO-PO, illustrating the superiority of the GWO-INC hybrid approach in enhancing the efficiency and performance of solar water pumps during shading. The GWO-INC excels with 99.6% accuracy in uniform shading and 99.8% in partial shading. It achieves convergence in a mere 0.55 seconds under uniform shading conditions and only 0.42 seconds when partial shading is present. Moreover, it significantly reduces torque oscillations, with a torque ripple of 8.26% in cases of uniform shading and 10.56% in partial shading.

Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review

To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: a combination of conventional algorithms, a combination of soft computing algorithms, and a combination of conventional and soft computing algorithms are discussed in detail. Particularly, about 90 hybrid MPPT techniques are presented, and their key specifications, such as accuracy, speed, cost, complexity, etc., are summarized. Along with these specifications, numerous other parameters, such as the PV panel’s location, season, tilt, orientation, etc., are also discussed, which makes its selection easier according to the requirements. This research work is organized in such a manner that it provides a valuable path for energy engineers and researchers to select an appropriate MPPT technique based on the projects’ limitations and objectives.

New Hybrid MPPT Technique Including Artificial Intelligence and Traditional Techniques for Extracting the Global Maximum Power from Partially Shaded PV Systems

This research aimed to increase the power captured from photovoltaic (PV) systems by continuously adjusting the PV systems to work at the maximum power point under climate changes such as solar irradiance change and temperature change and by tracking the global maximum power under partial shading conditions (PSCs). Under the effect of partial shading (PS), the PV curve has many local maximum peaks (LMPs) and one global maximum peak (GMP) which is dynamic because it changes with time when the shading pattern (SP) changes. The traditional maximum power point tracking (MPPT) methods are unable to track the Dynamic GMP and may fall into one of the LMPs. Many modern MPPT methods have been introduced that can track the Dynamic GMP, but their effectiveness can be improved. In this respect, this work introduces a new optimal MPPT technique to enhance the performance of the maximum power point tracking of solar cells under environmental changes and partial shading conditions. The proposed technique combines three well-known and important MPPT techniques, which are the Artificial Neural Network (ANN), Variable Step Perturb and Observe (VSP&O), and Fuzzy Logic Controller (FLC). Artificial Neural Network gives a voltage near the optimum voltage, Variable Step Perturb and Observe updates the voltage to get close to the optimum voltage, and Fuzzy Logic Controller updates the step size of the (P&O) technique. The proposed hybrid ANN-VSP&O-FLC technique showed its ability to track the Dynamic GMP accurately and quickly under the variation in the shading patterns with time and its ability to follow maximum power efficiently and quickly under climate changes. The proposed hybrid ANN-VSP&O-FLC technique also showed very low distortions in waveforms and very low oscillations around the steady state. The proposed hybrid ANN-VSP&O-FLC technique was compared to the most recent and effective MPPT techniques in terms of steady-state behavior, tracking speed, tracking efficiency, and distortions in waveforms, and the comparison showed that it is superior to them, with lower distortions in waveforms, a faster tracking speed (less than 0.1 s), higher tracking efficiency (greater than 99.65%), and lower oscillations around the steady state (less than 2 Watts).

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.

PV Integrated UPQC with Intelligent Control Techniques for Power Quality Enhancement

The configuration and control of a Unified Power Quality Conditioner (UPQC) coupled with Photovoltaic (PV) system is proposed in this work. By integrating PV to UPQC, the twin advantages of decarbonized clean energy generation in addition to enhanced Power Quality (PQ) is obtained. The series and shunt compensators, which together constitute the UPQC are sequentially interfaced to the common dc-link. In addition to infusing active PV generated power, the UPQC shunt compensator diminishes the load side power quality concerns. The role of a series compensator is to ensure that both the load and source voltages are in-phase perfectly. The PV system is integrated to the UPQC through a DC/DC Interleaved Cuk converter and by regulating the duty cycle of the Interleaved Cuk converter, utmost possible power is derived from the PV using Crow Search Algorithm (CSA) assisted Perturb and Observation (P & O) Maximum Power Point Tracking (MPPT) technique. The designed hybrid MPPT technique is capable of operating at Maximum Power Point (MPP) under both Partial Shading Condition (PSC) and uniform insolation condition. A d-q theory-based control is employed with the assistance of Proportional Integral (PI) controller for controlling the working of UPQC. The dynamic working of the PV based UPQC is evaluated on the basis of simulation outcomes attained from MATLAB.

An improved genetic algorithm based fractional open circuit voltage MPPT for solar PV systems

To extract the maximum power from solar PV, maximum power point tracking (MPPT) controllers are needed to operate the PV arrays at their maximum power point under varying environmental conditions. Fractional Open Circuit Voltage (FOCV) is a simple, cost-effective, and easy to implement MPPT technique. However, it suffers from the discontinuous power supply and low tracking efficiency. To overcome these drawbacks, a new hybrid MPPT technique based on the Genetic Algorithm (GA) and FOCV is proposed. The proposed technique is based on a single decision variable, reducing the complexity and convergence time of the algorithm. MATLAB/Simulink is used to test the robustness of the proposed technique under uniform and non-uniform irradiance conditions. The performance is compared to the Perturb & Observe, Incremental Conductance, and other hybrid MPPT techniques. Furthermore, the efficacy of the proposed technique is also assessed against a commercial PV system’s power output over one day. The results demonstrate that the proposed GA-FOCV technique improves the efficiency of the conventional FOCV method by almost 3%, exhibiting an average tracking efficiency of 99.96% and tracking speed of around 0.07 s with minimal steady-state oscillations. Additionally, the proposed technique can also efficiently track the global MPP under partial shading conditions and offers faster tracking speed, higher efficiency, and fewer oscillations than other hybrid MPPT techniques.

Hardware validation of hybrid MPPT technique via Novel ML controller and P&O method

The proposed paper deals with the hardware validation of Hybrid Maximum power point tracking (MPPT) technique via Novel ML (Monotonous Learning) Controller & Perturb & Observe (P&O) Method. MPPT methods find its inability in handling periodic variation against tiny change in irradiance and temperature. Further, it also shows its inability in enhancing its dynamic responsiveness when irradiance varies quickly. To overcome these problems, this paper proposed a Novel ML controller incorporated with P&O method along with its hardware validation to confirm the suitability of this Novel controller in practical environment. Novel ML Controller can manage periodic fluctuations whenever there is a little irradiance to eliminate mistakes and steady state oscillations. To improve dynamic responsiveness when irradiance fluctuates fast, we are using P&O approach without dead time. The presented paper also discusses the design, stability analysis, hardware validation of proposed Novel ML Controller along with MATLAB simulation.

Maximum power point tracking based on self-adaptive neural network combined with incremental conductance

ABSTRACT This article proposes a hybrid method that combines the self-adaptive neural network (SANN) and incremental conductance (IC) algorithm for maximum power point tracking (MPPT) in a photovoltaic (PV) system. The main objective of the proposed algorithm is to extract the maximum power of solar panels quickly and faster than the recent artificial neural network (ANN)-IC MPPT algorithm. Compared to the previous ANN-IC algorithm, a minimum number of data sets were used to obtain the GMP without compromising accuracy. Furthermore, comparison demonstrates that the suggested SANN-IC provides high efficiency, reliability, rapid tracking, and less response time tracking of GMP than w.r.t. all state-of-the-art techniques and ANN-IC method. The simulation is done with the help of MATLAB/SIMULINK. This is the first article about the performance of the hybrid MPPT technique tested by a sun simulator. The benefit of this hybrid method is that it reduces the search space of ANN, and hence, convergence speed is significantly increased. Training time and computing power are optimized by altering the number of epochs and requiring less memory. The experimental result showed the best convergence time and less oscillation than the ANN-IC method. Hence, the overall performance of SANN-IC in a steady-state condition with a partial shading condition meets the motive of the work.

A Novel Hybrid MPPT Technique Based on Harris Hawk Optimization (HHO) and Perturb and Observer (P&O) under Partial and Complex Partial Shading Conditions

Photovoltaic (PV) systems have been used extensively worldwide over the past few years due to the mitigation of fossils fuels; it is the best source because of its eco-friendly nature. In PV systems, the main research area concerns its performance under partial shading (PS) and complex partial shading (CPS) conditions. PV sources perform perfectly under ideal conditions, but under practical conditions, their performance depends upon many factors, including shading conditions, temperature, irradiance, and the angle of inclination, which can bring a photovoltaic or solar system into a PS or CPS condition. In these conditions, many power peaks appear, and it is hard to find the global peak among many local peaks. The ability to track the maximum power peak and maintain it to avoid fluctuations depends on the maximum power point tracking (MPPT) technique used in a photovoltaic system. This article is based on the implementation of a hybrid algorithm, combining Harris hawk’s optimization (HHO), a new technique which is based on natural inspiration, and a conventional perturb and observe (P&O) technique. The hybrid technique was tested under different weather conditions in MATLAB Simulink and showed less computational time, a fast convergence speed, and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the hybrid technique was made with bio-inspired particle swarm optimization (PSO), adaptive cuckoo search optimization (ACS), the dragonfly algorithm (DFO), and the water cycle algorithm (WCA). The hybrid technique achieves 99.8% efficiency on average and performs very well among the rest of the competing techniques.

A Fast-Tracking Hybrid MPPT Based on Surface-Based Polynomial Fitting and P&O Methods for Solar PV under Partial Shaded Conditions

The efficiency of photovoltaic (PV) systems depends directly on solar irradiation, so drastic variations in solar exposure will undoubtedly move its maximum power point (MPP). Furthermore, the presence of partial shading conditions (PSCs) generates local maximum power points (LMPPs) and one global maximum power point (GMPP) in the P-V characteristic curve. Therefore, a proper maximum power point tracking (MPPT) technique is crucial to increase PV system efficiency. There are classical, intelligent, optimal, and hybrid MPPT techniques; this paper presents a novel hybrid MPPT technique that combines Surface-Based Polynomial Fitting (SPF) and Perturbation and Observation (P&O) for solar PV generation under PSCs. The development of the experimental PV system has two stages: (i) Modeling the PV array with the DC-DC boost converter using a real-time and high-speed simulator (PLECS RT Box), (ii) and implementing the proposed GMPPT algorithm with the double-loop controller of the DC-DC boost converter in a commercial low-priced digital signal controller (DSC). According to the simulation and the experimental results, the suggested hybrid algorithm is effective at tracking the GMPP under both uniform and nonuniform irradiance conditions in six scenarios: (i) system start-up, (ii) uniform irradiance variations, (iii) sharp change of the (PSCs), (iv) multiple peaks in the P-V characteristic, (v) dark cloud passing, and (vi) light cloud passing. Finally, the experimental results—through the standard errors and the mean power tracked and tracking factor scores—proved that the proposed hybrid SPF-P&O MPPT technique reaches the convergence to GMPP faster than benchmark approaches when dealing with PSCs.

Hybrid MPPT Technique PSO-P&O Applied to Photovoltaic Systems Under Uniform and Partial Shading Conditions

The PV array must always operate at the Global Maximum Power Point (GMPP) in order to make the most of the energy generated. In this context, some classical techniques achieve this goal satisfactorily under uniform shading conditions. However, environmental conditions vary during the day and these methods fail to extract the highest power available under partial shading conditions. The present paper aims to propose a hybrid Maximum Power Point Tracking (MPPT) technique that uses Particle Swarm Optimization (PSO) and Perturb & Observe (P&O) methods, applied to photovoltaic systems under uniform and partial shading conditions. The photovoltaic system was modeled in the PSIM software. The proposed MPPT technique is compared with the classical P&O, standard PSO and a hybrid P&O-PSO techniques. The simulation results showed that the proposed hybrid algorithm can track GMPP under uniform and partial shading conditions and tracking time is 50% shorter than standard PSO technique. Furthermore, the proposed method manages to extract 0.3% more electricity from the photovoltaic system compared to the P&O-PSO hybrid

A Fast Converging Hybrid MPPT Algorithm Based on ABC and P&O Techniques for a Partially Shaded PV System

Among all the conventional maximum power point tracking (MPPT) techniques for a photovoltaic (PV) system that have been proposed, incremental conductance (INC) and perturb and observe (P&O) are the most popular because of their simplicity and ease of implementation. However, under partial shading conditions (PSCs), these MPPT algorithms fail to track the global maximum power point (GMPP) and instead converge into local maximum power points (LMPPs), resulting in considerable PV power loss. This paper presents a new hybrid MPPT technique combining the artificial bee colony (ABC) and P&O algorithms named ABC-P&O. The P&O technique is used to track the MPP under uniform irradiance, and only during irradiance variations is the ABC algorithm employed. The effectiveness of the proposed hybrid algorithm at tracking the GMPP, under both uniform and nonuniform irradiance conditions, was assessed by hardware-in-the-loop (HIL) tests employed by a dc–dc boost converter. Then, the ABC-P&O strategy was applied to obtain the voltage reference for the outer PI control loop, which provided the current reference to the discrete-time sliding-mode current control. The ABC-P&O algorithm has a reasonable computational cost, allowing the use of a commercial, low-priced digital signal controller (DSC) with outer voltage and inner current control loops. Many challenging tests validated that the proposed ABC-P&O technique converges fast to the GMPP with high efficiency and superior performance under different PSCs.

High Performance MPPT based on TS Fuzzy–integral backstepping control for PV system under rapid varying irradiance—Experimental validation

With the recent focus marked on energy efficiency and solar energy development, much research is being dedicated to the development of enhanced maximum power point tracking (MPPT) algorithms for photovoltaic applications. However, the main criteria with regard to tracking performances and circuit implementation are still considered as a major challenge under rapid varying weather conditions. In this paper, a T-S Fuzzy–integral backstepping-based hybrid MPPT technique is proposed for rapid, accurate and efficient tracking. The proposed technique enables reliable and stable operation under fast dynamic environmental changes. Besides, it is simple as it does not require extra atmospheric sensors. The theoretical analysis addressed in this study is verified through simulations via Matlab/Simulink and experimental outdoor tests. A comparison with different other MPPT techniques is provided to highlight the performances of the developed MPPT method.

Hybrid Maximum Power Point Tracking Method Based on Iterative Learning Control and Perturb & Observe Method

Maximum power point tracking (MPPT) is used to utilize intermittent solar power fully in the photovoltaic (PV) systems. Tracking the MPP fast, and accurately with changes in the solar irradiance, and the temperature is the goal of MPPT techniques. In this paper, a hybrid MPPT method based on iterative learning control (ILC), and perturb, and observe (P&O) algorithm is proposed. ILC can deal with the periodic variations to eliminate the steady-state oscillations, and errors, when the operation point is close to the MPP or a small irradiance variation occurs. In the proposed hybrid MPPT technique, a high frequency power P&O method without deadtime is used to improve the dynamic response when the irradiance changes rapidly. This paper presents the theoretical background of the hybrid MPPT algorithm, design, and stability analysis. Simulation, and hardware validation results substantiate the effectiveness of the proposed method.

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

Photovoltaic (PV) systems are more efficient when operated in its maximum power point (MPP). Even with the existence of high number of MPP tracking (MPPT) techniques, it is possible to improve the MPPT efficiency. One way to achieve this improvement is to use model-based MPPT techniques, since they can estimate the MPP with reasonable accuracy, accelerating the MPP search process. Based on this motivation, the main objective of this article is to propose an efficient double-diode model-based + perturb and observe (P&O) heuristic-based hybrid MPPT technique. The proposition has the following three unique features: the double-diode model used in the MPPT technique has the capability of extrapolating the I-V curves of the PV modules for any environmental condition, even for temperature and irradiance values not used during the model building process; a parameter estimation technique that uses a combination of analytical equations and pattern search optimization algorithm is also presented for this unique model; and an irradiance estimation algorithm is also proposed, avoiding the irradiance sensor usually needed in model-based and hybrid MPPT techniques. Experimental results demonstrate that the integration of the model, the irradiance estimator, and reduced step P&O algorithm is an effective hybrid MPPT solution for PV systems.

Review of Recent Trends in Optimization Techniques for Hybrid Renewable Energy System

Due to non-linear characteristics of renewable energy sources, a Maximum Power Point Tracking (MPPT) technique is adopted to maximize the output power. In this research article, on the basis of state of art review of renewable energy systems such as solar Photo-voltaic, Wind Turbine, Fuel Cell and Hybrid Renewable Energy System. The investigators have gone through the various MPPT techniques for different renewable energy systems out of which artificial intelligence based hybrid MPPT technique gives better performance as compared to other MPPT techniques.

Investigation of MPPT Techniques Under Uniform and Non-Uniform Solar Irradiation Condition–A Retrospection

A significant growth in solar photovoltaic (PV) installation has observed during the last decade in standalone and grid-connected power generation systems. The solar PV system has a non-linear output characteristic because of weather intermittency, which tends to have a substantial effect on overall PV system output. Hence, to optimize the output of a PV system, different maximum power point tracking (MPPT) techniques have been used. But, the confusion lies while selecting an appropriate MPPT, as every method has its own merits and demerits. Therefore, a proper review of these techniques is essential. A “ Google Scholar ” survey of the last five years (2015-2020) was conducted. It has found that overall seventy-one review articles are published on different MPPT techniques; out of those seventy-one, only four are on uniform solar irradiance, seven on non-uniform and none on hybrid optimization MPPT techniques. Most of them have discussed the limited number of MPPT techniques, and none of them has discussed the online and offline under uniform and hybrid MPPT techniques under non-uniform solar irradiance conditions all together in one. Unfortunately, very few attempts have made in this regard. Therefore, a comprehensive review paper on this topic is need of time, in which almost all the well-known MPPT techniques should be encapsulated in one paper. This article focuses on classifications of online, offline, and hybrid optimization MPPT algorithms, under the uniform and non-uniform irradiance conditions. It summarizes various MPPT methods along with their mathematical expression, operating principle, and block diagram/flow charts. This research will provide a valuable pathway to researchers, energy engineers, and strategists for future research and implementation in the field of maximum power point tracking optimization.

Evolutionary and Metaphor-Metaheuristic MPPT Techniques for Enhancing The Operation of Solar PV Under Partial Shading Condition

Photovoltaic (PV) systems are the most popular electric power generation systems for domestic as well as industrial applications because of its sustainability and excess availability in nature. Under Partial Shading Condition (PSC), the nonlinear characteristics of solar PV consist of multiple local Maximum Power Points (MPPs) and one global MPP. Hence, it is difficult to operate the operating point of the solar PV closer to true MPP. There are different tracking methods available for MPP Tracking in the literature. The classical Maximum Power Point Tracking (MPPT) methods are not suitable under partial shading conditions because the operating point of PV may settle down to any one of the local MPP. Also, these methods failed to mitigate the oscillations across the operating point of solar PV. The recently developed evolutionary based MPPT techniques are becoming more prominent in order to avoid the drawbacks of classical MPP techniques. In this article, different types of MPPT techniques such as evolutionary, metaphor-metaheuristic and hybrid MPPT techniques are discussed in detail along with their advantages and disadvantages. The comparative analysis is carried out by considering the parameters such as tracking speed, oscillation across MPP, settling time, implementation complexity and efficiency.

Efficiency of Hybrid MPPT Techniques Based on ANN and PSO for Photovoltaic Systems under Partially Shading Conditions

Hybrid Maximum Power Point Tracking (MPPT) algorithms have been investigated as an alternative to improve the performance of conventional MPPT, such as Perturb and Observe (P&O), Incremental Conductance (InC) and Hill Climbing (HC) in Photovoltaic (PV) systems under Partially Shading Co+ndition (PSC). In the present article, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) hybridized with P&O algorithm for MPPT are compared in terms of power efficiency. This paper not only compare hybrid MPPT methods against conventional MPPT, but also provide a comparison between two different hybrid techniques. In order to evaluate the performance of such hybrid methods, a PV system was computationally modelled and different PSC scenarios were implemented in MATLAB®/Simulink, as well as the hybrid methods ANN + P&O and PSO + P&O. The hybrid methods ANN + P&O and PSO + P&O successfully improve the efficiency of P&O algorithm, respectively achieving 98.93% and 92.96% on average in the PSC scenarios tested, whereas P&O achieved 88.27% on average in such scenarios.