Maximum Power Point Tracking Of Photovoltaic System Research Articles

A Deep Recurrent Reinforcement Learning Approach for Enhanced MPPT in PV Systems

Under partial shading condition (PSC), for efficient usage, solar photovoltaic (PV) systems are required to operate at global maximum power point (GMPP). A maximum power point tracking (MPPT) controller is usually employed for this task. In this work, for MPPT under PSC, a recurrent deep reinforcement learning (DRL) approach has been developed and investigated. The optimized neural network, augmented by long short-term memory (LSTM), is trained using the proximal policy optimization (PPO) DRL algorithm. The MPPT performance of the proposed scheme was assessed using the GMPP attainment accuracy (%), on several realistic static and dynamic test cases. The presented investigations revealed the proposed controller's average MPPT accuracy of 97.79% for the considered random static PSCs. Similarly, average MPPT accuracies of 95.69% under changing irradiance, 95.70% under change in temperature and 95.77% under changes in both irradiance and temperature were recorded. These accuracies are found to be significantly superior to recent reported works. The resulting performance enhancement is attributed to the inclusion of LSTM, which allowed the agent to retain information about past states and actions, enabling it to make informed decisions. Therefore, based on the presented investigations, it is concluded that the proposed PPO-LSTM approach is an excellent MPPT alternative for PV systems.

Innovative metaheuristic algorithm with comparative analysis of MPPT for 5.5 kW floating photovoltaic system

Multiple localized maxima in power voltage features are triggered by non-uniform irradiance, which has a detrimental influence on photovoltaic (PV) system operation. Numerous control approaches for PV maximum power point tracking (MPPT) have lately been investigated. This paper proposes a metaheuristic algorithm to reach PV maximum power more efficiently based on the hybridization of Improved Grey Wolf Optimization (IGWO) and BAT algorithms, which are chosen for their reliability and fast MPPT for PV systems. However, they both encounter issues including delayed convergence and increased oscillations while tracking. To overcome these limitations, a newly Improved Grey Wolf BAT Optimization (IGWBO) is applied to specify the duty cycle parameter that is used in MPPT to enhance the performance of FPV systems under a variety of non-uniform weather scenarios. The results of implementing IGWBO-FPV are compared to those of IGWO-FPV, GWO-FPV, BAT-FPV, and FPV without any control algorithm. The findings revealed that the proposed IGWBO-FPV approach outperforms the annual generated power of the IGWO-FPV, GWO-FPV, BAT-FPV, and FPV without control by 5.64%, 10.58%, 17.54%, and 27.28% respectively. The optimal IGWBO-FPV enviro-economic evaluation indicates that 42.83 tons of CO2/year can be prevented from being released into the environment, with an LCOE of 0.052 $/kWh.

Improved coot optimizer algorithm-based MPPT for PV systems under complex partial shading conditions and load variation

Solar power is considered one of the most common renewable energy sources. However, the effective harnessing of maximum solar energy in photovoltaic (PV) systems faces a significant challenge due to weather fluctuations. This challenge becomes particularly pronounced for PV systems aiming to achieve optimal power output during Maximum Power Point Tracking (MPPT), especially under partial shading conditions (PSCs). In the P-V curve of PV arrays, the PSCs cause multiple peaks, known as local peaks, and one global peak. The use of sophisticated MPPT optimization algorithms is necessary to identify the global peak, ensuring the highest point of power production and avoiding entrapment in the local peaks. The main drawbacks of these optimization algorithms are their inability to differentiate between the irradiance change and load variations, as well as their high convergence speed. This paper proposes an Improved Coot Optimization Algorithm (ICOA) based on MPPT to alleviate the issue of convergence speed. Furthermore, a novel approach has also been devised to enhance the speed response during load variation, which can be implemented with any DC-DC converter. A novel approach was adopted, with one tuning parameter implemented to simplify the algorithm. A variety of complex PSCs were tested with a SEPIC converter, and the sampling time was adjusted at 0.05 s. Based on the experimental results, the proposed ICOA has achieved the best performance, with an average tracking time of 0.58 s under different weather conditions and an efficiency of 99.94 %. Additionally, an assessment of the proposed technique against existing metaheuristic algorithms in this field is conducted, revealing the effectiveness of the proposed approach in terms of rapid tracking and high efficiency.

A New MPPT-Based Extended Grey Wolf Optimizer for Stand-Alone PV System: A Performance Evaluation versus Four Smart MPPT Techniques in Diverse Scenarios

Photovoltaic (PV) systems play a crucial role in clean energy systems. Effective maximum power point tracking (MPPT) techniques are essential to optimize their performance. However, conventional MPPT methods exhibit limitations and challenges in real-world scenarios characterized by rapidly changing environmental factors and various operating conditions. To address these challenges, this paper presents a performance evaluation of a novel extended grey wolf optimizer (EGWO). The EGWO has been meticulously designed in order to improve the efficiency of PV systems by rapidly tracking and maintaining the maximum power point (MPP). In this study, a comparison is made between the EGWO and other prominent MPPT techniques, including the grey wolf optimizer (GWO), equilibrium optimization algorithm (EOA), particle swarm optimization (PSO) and sin cos algorithm (SCA) techniques. To evaluate these MPPT methods, a model of a PV module integrated with a DC/DC boost converter is employed, and simulations are conducted using Simulink-MATLAB software under standard test conditions (STC) and various environmental conditions. In particular, the results demonstrate that the novel EGWO outperforms the GWO, EOA, PSO and SCA techniques and shows fast tracking speed, superior dynamic response, high robustness and minimal power fluctuations across both STC and variable conditions. Thus, a power fluctuation of 0.09 W could be achieved by using the proposed EGWO technique. Finally, according to these results, the proposed approach can offer an improvement in energy consumption. These findings underscore the potential benefits of employing the novel MPPT EGWO to enhance the efficiency and performance of MPPT in PV systems. Further exploration of this intelligent technique could lead to significant advancements in optimizing PV system performance, making it a promising option for real-world applications.

A flexible load adaptive control strategy for efficient photovoltaic hydrogen generation system

The adaptive adjustment of the input current in proton exchange membrane electrolyzers (PEMEL) can provide several benefits, including the enhancement of PEMEL efficiency and the extension of its operating lifespan within an optimal range. This study presents a novel configuration for a photovoltaic (PV) hydrogen generation system that allows for the direct integration of PV. Moreover, the utilization of the triple-phase shift (TPS) modulation technique is implemented in order to achieve maximum power point tracking (MPPT) control within dual active bridge (DAB) DC/DC converters. The proposed strategy offers a pre-control approach for active adjustment of flexible loads to mitigate the mismatch between power output and load caused by fluctuations in renewable energy sources. This mismatch can lead to excessive current ripple, which in turn can impact the safe functioning of the PEMEL. In tandem with the analysis of the factors that contribute to ripple and the properties of the Electrical Spring (ES), relevant multiple variables are selected as control signals for the control strategy. This is done to effectively regulate the magnitude of non-essential loads and consequently reduce the ripple in the PEMEL system. The use of the ES pre-control technique effectively addresses the issue of delayed response of the PEMEL to PV oscillations, which is attributed to the large time-lag characteristics of electrolyzers. The simulation results at steady-state conditions and sudden changes in lighting conditions demonstrate concurrently that the overall control strategy yields a reduction in current stress in the DAB to 30.09% and 51.59%, respectively, enables MPPT in PV systems, and decreases the input current ripple of PEMEL to 80.1% and 57.67%, showing stronger anti-disturbance ability.

Fast Tracking of Maximum Power in a Shaded Photovoltaic System Using Ali Baba and the Forty Thieves (AFT) Algorithm

Photovoltaic (PV) generation systems that are partially shaded have a non-linear operating curve that is highly dependent on temperature and irradiance conditions. Shading from surrounding objects like clouds, trees, and buildings creates partial shading conditions (PSC) that can cause hot spot formation on PV panels. To prevent this, bypass diodes are installed in parallel across each panel, resulting in a global maximum power point (GMPP) and multiple local maximum power points (LMPPs) on the power-voltage (P-V) curve. Traditional methods for maximum power point tracking (MPPT), such as perturb and observe (P&O) and incremental conductance (INC), converge for LMPPs on the P-V curve, but metaheuristic algorithms can track the GMPP effectively. This paper proposes a new, efficient, and robust GMPP tracking technique based on a nature-inspired algorithm called Ali Baba and the Forty Thieves (AFT). Utilizing the AFT algorithm for MPPT in PV systems has several novel features and advantages, including its adaptability, exploration-exploitation balance, simplicity, efficiency, and innovative approach. These characteristics make AFT a promising choice for enhancing the efficiency of PV systems under varied circumstances. The performance of the proposed method in tracking the GMPP is evaluated using a simulation model under MATLAB/Simulink environment, the achieved simulation results are compared to particle swarm optimization (PSO). The proposed method is also tested in real-time using the Hardware-in-the-loop (HIL) emulator to validate the achieved simulation results. The findings indicate that the proposed AFT-based GMPP tracking method performs better under complex partial irradiance conditions than PSO.

An Improved P&O Based MPPT for PV Systems with Reduced Steady-State Oscillation

This paper proposes a perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems. P&O is a simple technique that comes with serious problems, steady-state oscillation, and deviation from the maximum power point (MPP). This algorithm uses variable step size to decrease the convergence time, and it uses the average value of the last three duty cycle to find MPP fast and accurately. In addition, the probability of deviation has been reduced using the direction of the current when the irradiance changes. The irradiance change identifier, steady-state determiner, duty cycle selector, step size alternator, and boundary condition imposer are the extramodules attached to the structure of the original P&O algorithm to change the step size. This algorithm has been tested along with different types of P&O and INC algorithms in MATLAB software and practical experiments using a boost switching converter. The irradiance pattern used in the simulation includes step and slop, increasing and decreasing patterns, and the proposed algorithm obtained an overall efficiency of 98.54%.

Application of Lévy flight particle swarm optimisation in MPPT of photovoltaic system

ABSTRACT Due to the intermittent and unstable nature of PV systems, maximum power point tracking (MPPT) of PV systems is necessary in practice. At the same time, PV cells are also shaded by objects such as trees and houses in the environment causing local shadows, so the Lévy flight particle swarm optimisation (LFPSO) is proposed in this paper. The random walk process of Lévy flight is added to the particle swarm optimisation (PSO) to increase the diversity of the search, which can avoid falling into local optimal solutions. The experimental simulation results show that the algorithm proposed in this paper can still accurately track the global maximum power in the case of partial shading, which well avoids falling into the local maximum power. The performance is much higher than the traditional maximum power point tracking algorithm, which improves the efficiency of the PV system.

Systematic Literature Review and Benchmarking for Photovoltaic MPPT Techniques

There are a variety of maximum power point tracking (MPPT) algorithms for improving the energy efficiency of solar photovoltaic (PV) systems. The mode of implementation (digital or analog), design simplicity, sensor requirements, convergence speed, range of efficacy, and hardware costs are the primary distinctions between these algorithms. Selecting an appropriate algorithm is critical for users, as it influences the electrical efficiency of PV systems and lowers costs by reducing the number of solar panels required to achieve the desired output. This research is relevant since PV systems are an alternative and sustainable solution for energy production. The main aim of this paper is to review the current advances in MPPT algorithms. This paper first undertakes a systematic literature review (SLR) of various MPPT algorithms, highlighting their strengths and weaknesses; a detailed summary of the related reviews on this topic is then presented. Next, quantitative and qualitative comparisons of the most popular and efficient MPPT methods are performed. This comparison is based on simulation results to provide efficient benchmarking of MPPT algorithms. This benchmarking validates that intelligent MPPTs, such as artificial neural network (ANN), fuzzy logic control (FLC), and adaptive neuro-fuzzy inference system (ANFIS), outperform other approaches in tracking the MPPT of PV systems. Specifically, the ANN technique had the highest efficiency of 98.6%, while the ANFIS and FLC methods were close behind with efficiencies of 98.34% and 98.29%, respectively. Therefore, it is recommended that these intelligent MPPT techniques be considered for use in future photovoltaic systems to achieve optimal power output and maximize energy production.

Jellyfish Search Algorithm for MPPT in Photovoltaic Systems Under Partial Shading Conditions

In photovoltaic systems, maximum power point tracking (MPPT) methods are used to get the maximum power out of them. The presence of several peaks in a PV array’s power-voltage characteristics is due to partial shadowing conditions that increase the complexity of the tracking operation. In this paper, the Global Maximum Power Point (GMPP) is calculated using latest meta-heuristic optimization algorithm known as Jellyfish Search (JS). This unique MPPT approach is used to reduce PV module tracking time and improve the tracking efficiency. Using MATLAB/SIMULINK, the effectiveness of the suggested JS algorithm is assessed by contrasting it with the traditional P&O approach in terms of tracking speed and precision. The simulation findings indicate that the JS algorithm’s tracking ability is better than that of the conventional P&O method. In the experimental results, the JS algorithm reduces convergence time by 56.6% when compared to the PSO algorithm. Also, the proposed JS algorithm generates output power higher than the PSO MPPT algorithm using the duty cycle ratio value at the expected peaks.

Hybrid MPPT approach using Cuckoo Search and Grey Wolf Optimizer for PV systems under variant operating conditions

Photovoltaic (PV) systems are adversely affected by partial shading and non-uniform conditions. Meanwhile, the addition of a bypass shunt diode to each PV module prevents hotspots. It also produces numerous peaks in the PV array’s power-voltage characteristics, thereby trapping conventional maximum power point tracking (MPPT) methods in local peaks. Swarm optimization approaches can be used to address this issue. However, these strategies have an unreasonably long convergence time. The Grey Wolf Optimizer (GWO) is a fast and more dependable optimization algorithm. This renders it a good option for MPPT of PV systems operating in varying partial shading. The conventional GWO method involves a long conversion time, large steady-state oscillations, and a high failure rate. This work attempts to address these issues by combining Cuckoo Search (CS) with the GWO algorithm to improve the MPPT performance. The results of this approach are compared with those of conventional MPPT according to GWO and MPPT methods based on perturb and observe (P&O). A comparative analysis reveals that under non-uniform operating conditions, the hybrid GWO CS (GWOCS) approach presented in this article outperforms the GWO and P&O approaches.

A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA

In this study, an improved artificial intelligence algorithms augmented Internet of Things (IoT)-based maximum power point tracking (MPPT) for photovoltaic (PV) system has been proposed. This will facilitate preventive maintenance, fault detection, and historical analysis of the plant in addition to real-time monitoring. Further, the simulation results validate the improved performance of the suggested method. To demonstrate the superiority of the proposed MPPT algorithm over current methods, such as cuckoo search algorithms and the incremental conductance approach, a performance comparison is offered. The outcomes demonstrate the suggested algorithm’s capability to track the Global Maximum Power Point (GMPP) with quicker convergence and less power oscillations than before. The results clearly show that the artificial intelligence algorithm-based MPPT is capable of tracking the GMPP with an average efficiency of 88%, and an average tracking time of 0.029 s, proving both its viability and effectiveness.

Adaptive output-feedback control design for maximum power point tracking of uncertain photovoltaic systems

The problem of maximum power point tracking (MPPT) control of uncertain photovoltaic (PV) systems is addressed in this paper, on the basis of an adaptive Kalman-like observer. The system is composed of a photovoltaic generator (PVG) supplying power to a DC centrifugal pump driven by a DC–DC boost converter. The PVG is connected to the converter by a long PV cable. Indeed, many PV power plant situations require that the PV panels be installed at a great distance from the converter for several reasons including the security and the property of the site and its exposure to good daily solar irradiance. This obviously leads to the difficulty of PVG output current and voltage measurement using ordinary sensors. Such quantities measurement being in fact necessary for MPPT algorithms and controllers design. Furthermore, the specific parameters of the long PV cable used, namely its resistance and inductance, could have a significant effect on the MPPT control efficiency if instead of PVG delivered voltage and current measurements, only those accessible at the converter side of the cable are used in MPPT controllers design. Therefore, this work aims at overcoming the two aforementioned issues by proposing an adaptive output-feedback control-based MPPT for PV systems. An adaptive Kalman-like observer providing online estimates of inaccessible state variables as well as of PV cable unknown parameters is firstly designed. Then, a backstepping control law is synthesized to meet the MPPT objective. The convergence of both adaptive observer and closed-loop system control has been established using Lyapunov approach and the effectiveness of the developed adaptive MPPT controller in achieving an accurate and robust MPPT control towards uncertainties of the PV cable parameters, has been validated through numerical simulations.

Jellyfish Search Optimization Algorithm for MPP Tracking of PV System

Because of the rapid increase in the depletion rate of conventional energy sources, the energy crisis has become a central problem in the contemporary world. This issue opens the gateway for exploring and developing renewable energy sources to fulfill the exigent energy demand. Solar energy is an abundant source of sustainable energy and hence, nowadays, solar photovoltaic (PV) systems are employed to extract energy from solar irradiation. However, the PV systems need to work at the maximum power point (MPP) to exploit the highest accessible power during varying operating conditions. For this reason, maximum power point tracking (MPPT) algorithms are used to track the optimum power point. Furthermore, the efficient utilization of PV systems is hindered by renowned partial shading conditions (PSC), which generate multiple peaks in the power-voltage characteristic of the PV array. Thus, this article addresses the performance of the newly developed jellyfish search optimization (JSO) strategy in the PV frameworks to follow the global maximum power point (GMPP) under PSC.

Space vector PWM of three-phase inverter with MPPT for photovoltaic system

ABSTRACT This study introduces a photoelectric (PV) system based on the maximum power point tracking (MPPT) technique, which is performed by the perturb and observe P&O method. This algorithm is chosen due to its ability to withstand any difference in parameters and high efficiency. The interleaved boost converter characteristics are controlled using maximum power point tracking (MPPT), and Space vector modulation (SVPWM) is the control technique used to control the three-phase voltage source converter. In order to measure the overall performance of the system, simulations are performed in the MATLAB/Simulink environment.

An Extensive Study on Online, Offline and Hybrid MPPT Algorithms for Photovoltaic Systems

To moderate global warming, conventional fossil fuels are depleted. As the population increased with the rising standard of living and industrial growth, the global environment is affected and cause the greenhouse gases occurrence, which are frequently increased by unlimited use of fossil fuels. The generation of electric power loads increases the power demand on the basics of modern power technology development. Several benefits can be attained by installing the distribution generation with the quality and reliability of power delivered. However, the global energy problem can be resolved by renewable energy sources as an alternative energy generation. Technological developments in the last decade have increased the use of renewable energy sources. In worldwide, several renewable energy sources are used to attain their own power demand. The photovoltaic (PV) generation is the essential renewable energy source to serve the increasing electrical loads. The fastest-growing PV system has the naturally available energy sources of robust evolution with elegant benefits. The foremost objective of this paper is to examine the performance of the PV system with various Maximum Power Point Tracking (MPPT) algorithms. The solar irradiance and temperature make it complex to track the MPPT of PV systems. This review is about various MPPT algorithms like online, offline, and hybrid methods. The selected algorithms from each discussion are simulated in MATLAB/Simulink environment to match their performance in footings of the dynamic response and efficiency of the PV system under the variations of solar irradiance and temperature. An explanation and discussion of the PV system are achieved with the study of different types of MPPT algorithms of PV systems.

Incremental conductance MPPT in grid connected PV system

PV (photovoltaic) system input is irradiation and ambient temperature. The solar radiation is varying in nature so efficiency is low of PV system. The various maximum power point tracking (MPPT) methods are used to enhance effectiveness of solar PV system. The incremental conductance(IC) algorithm is one of the MPPT algorithms which is extensively employed because, at steady state it has high tracking correctness and high productivity at quickly varying atmospheric circumstances. In this proposed system a combination of IC algorithm and integral regulator (IR) is used to obtain MPPT in PV system. This combination gives high accuracy, stable operation in PV module entire region and it also eliminate oscillations near about the peak power operating point. A 100kw PV system is design in MATLAB software 2016a version. This PV system consist proposed IC-IR algorithm to obtain maximal power operating point.

A conceptual framework and a Review of AI-Based MPPT Techniques for Photovoltaic Systems

Several Maximum Power Point Tracking (MPPT) techniques based on various Artificial Intelligence (AI) algorithms were recently developed due to the current availability of powerful computation controllers and adaptability of AI algorithms and their characteristic in handling non-linear problems. AI algorithms are perfectly suited to handle the problem of adverse conditions of rapid irradiance change and partial shading that the PV systems suffer. This paper presents a conceptual framework of the MPPT for photovoltaic systems and a comprehensive review of the current AI-based MPPT techniques. The paper also covers MPPT components, modeling, characteristics, affecting factors, and categories. The performance of different AI algorithms is evaluated and categorized based on many criteria including system complexity, tracking speed, cost, efficiency, accuracy, hardware implantation, sensory parameters, response to Partial Shading Conditions (PSC), etc. Finally, a summarizing comparison of performance of the main MPPT techniques is presented.

An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions

The problem of partial shading has serious effects on the performance of photovoltaic (PV) systems. Adding a bypass diode in shunt to each PV module avoids hot-spot phenomena, but causes multi-peaks in the power–voltage (P–V) characteristics of the PV array, which cause traditional maximum power point tracking (MPPT) techniques to become trapped in local peaks. This problem has forced researchers to search for smart techniques to track global peaks and prevent the possibility of convergence at local peaks. Swarm optimization techniques have been used to fill this shortcoming; unfortunately, however, these techniques suffer from unacceptably long convergence time. Cuckoo search (CS) is one of the fastest and most reliable optimization techniques, making it an ideal option to be used as an MPPT of PV systems under dynamic partial shading conditions. The standard CS algorithm has a long conversion time, high failure rate, and high oscillations at steady state; this paper aims to overcome these problems and to fill this research gap by improving the performance of the CS. The results obtained from this technique are compared to five swarm optimization techniques. The comparison study shows the superiority of the improved CS strategy introduced in this paper over the other swarm optimization techniques.

An Improved Particle Swarm Optimization Algorithm for Maximum Power Point Tracking Of Photovoltaic Cells in Normal and Under Partial Shading Conditions.(Dept.E)

Because of the world's acute energy crisis, the need for renewable energy sources is increasing today. In recent years, Standalone photovoltaic systems have been widely used in remote regions, following the growth of the photovoltaic cell industry. The key features of the systems of photovoltaic (PV) used to ingather solar energy while reducing the gas emissions of the greenhouse, maintenance costs are low, reduced site-related restrictions as a mechanical noise reduction due to no moving parts. Nevertheless, Photovoltaic systems are suffering from comparatively poor conversion efficiency. Hence, a PV system needs maximum power point tracking (MPPT) of the solar array. Several factors affect the maximum resulted power as nonlinear behavior of PV systems, temperature and the level of solar radiation that complicate monitoring of the maximum power point (MPP). This paper represents an evolutionary optimization algorithm using improved particle swarm optimization (PSO) technique for MPPT. The proposed technique has achieved high power in the conditions of partial shading rather than the conventional (PSO). The results of the simulation showed that the strategy could be reliable in monitoring the global MPPT in PV systems.