Search Results (22)

A newly developed hybrid maximum power point tracker (MPPT) utilizes a modified simulated annealing (SA) algorithm in conjunction with an adaptive hill climbing (HC) technique to optimize the extraction of the maximum power point (MPP) from photovoltaic (PV) systems. This innovative MPPT improves the ability to harvest maximum power from the PV system, particularly under rapidly fluctuating weather conditions and in situations of partial shading. The controller combines the rapid local search abilities of HC with the global optimization advantages of SA, which has been modified to retain and retrieve the maximum power achieved, thus ensuring the extraction of the global maximum. Furthermore, an adaptive HC algorithm is implemented with a variable step size adjustment, which accelerates convergence and reduces steady-state oscillations. Additionally, an offline SA algorithm is utilized to fine-tune the essential parameters of the proposed controller, including the maximum and minimum step sizes for duty cycle adjustments, initial temperature, and cooling rate. Simulations performed in Matlab/Simulink, along with experimental validation using Imperix-Opal-RT, confirm the effectiveness and robustness of the proposed controller. In the scenarios that were tested, the suggested HC–SA reached the global maximum power point (GMPP) of approximately 600 W in about 0.05 s, whereas the traditional HC stabilized at a local maximum close to 450 W, and the fuzzy-logic MPPT attained the GMPP at a slower rate, taking about 0.2 s, with a pronounced transient dip before settling with a small steady-state ripple. These findings emphasize that, under the operating conditions examined, the proposed method reliably demonstrates quicker convergence, enhanced tracking accuracy, and greater robustness compared with the other MPPT techniques. Full article

Photovoltaic (PV) power generation may vary with respect to several factors such as solar radiation, temperature, power conditioning units, environmental effects, and shading conditions. The partial shading of PV modules is one of the most crucial factors that causes the performance degradation of PV systems. The main reason for efficiency reduction under partial shading conditions is the creation of multiple local maximums and one global maximum operating point. The classical Maximum Power Point Tracking (MPPT) algorithm fails to determine the global maximum operating point to prevent power losses under partial shading conditions. In this study, a novel hybrid MPPT method based on Perturb & Observe and Particle Swarm Optimization that mainly aims to determine global operating point, is proposed. The proposed hybrid MPPT method is tested under different partial shading conditions and variable irradiance levels. In this manner, the dynamic response of the system is remarkably increased by the proposed MPPT method. To show the superiority of the developed method, a performance comparison is conducted with the P&O- and Kalman-Filter-based MPPT methods. The obtained results illustrate an improvement around 1.5 V in undershoot voltage and 0.2 ms in convergence speed. In addition, the overall system efficiency of the PV system is increased around 2% when compared to the P&O- and Kalman-Filter-based MPPT methods. Consequently, the proposed method seems to be an efficient method in terms of undershoot voltage, convergence time, tracking accuracy, and efficiency under partial shading conditions. Full article

The primary disadvantage of solar photovoltaic systems, particularly in partial shadowing conditions (PSC), is their low efficiency. A power–voltage curve with a homogenous distribution of solar irradiation often has a single maximum power point (MPP). Without a doubt, it can be extracted using any conventional tracker—for instance, perturb and observe. On the other hand, under PSC, the situation is entirely different since, depending on the number of distinct solar irradiation levels, the power–voltage curve has numerous MPPs (i.e., multiple local points and one global point). Conventional MPPTs can only extract the first point since they are unable to distinguish between local and global MPP. Thus, to track the global MPP, an optimized MPPT based on optimization algorithms is needed. The majority of global MPPT techniques seen in the literature call for sensors for voltage and current in addition to, occasionally, temperature and/or solar irradiance, which raises the cost of the system. Therefore, a single-sensor global MPPT based on the recent red-tailed hawk (RTH) algorithm for a PV system interconnected with a DC link operating under PSC is presented. Reducing the number of sensors leads to a decrease in the cost of a controller. To prove the superiority of the RTH, the results are compared with several metaheuristic algorithms. Three shading scenarios are considered, with the idea of changing the shading scenario to change the location of the global MPP to measure the consistency of the algorithms. The results verified the effectiveness of the suggested global MPPT based on the RTH in precisely capturing the global MPP compared with other methods. As an example, for the first shading situation, the mean PV power values varied between 6835.63 W and 5925.58 W. The RTH reaches the highest PV power of 6835.63 W flowing through particle swarm optimization (6808.64 W), whereas greylag goose optimizer achieved the smallest PV power production of 5925.58 W. Full article

Photovoltaic (PV) systems face challenges in achieving maximum energy extraction due to the non-linear nature of their current versus voltage (IxV) characteristics, which are influenced by temperature and solar irradiation. These factors lead to variations in power generation. The situation becomes even more complex under partial shading conditions, causing distortion in the characteristic curve and creating discrepancies between local and global maximum power points. Achieving the highest output is crucial to enhancing energy efficiency in such systems. However, conventional maximum power point tracking (MPPT) techniques often struggle to locate the global maximum point required to extract the maximum power from the PV system. This study employs genetic algorithms (GAs) to address this issue. The system can efficiently search for the global maximum point using genetic algorithms, maximizing power extraction from the PV arrangements. The proposed approach is compared with the traditional Perturb and Observe (P&O) method through simulations, demonstrating its superior effectiveness in achieving optimal power generation. Full article

Partial shading significantly affects the performance of photovoltaic (PV) power systems, rendering traditional maximum power point tracking (MPPT) methods ineffective. This study proposes a novel hybrid MPPT algorithm integrating voltage scanning and modified Perturb and Observe (P&O) techniques to overcome the limitations posed by partial shading. This algorithm has a simple structure and does not require panel information such as the number of panels or voltage due to its voltage scanning feature. To test the proposed algorithm, a grid-connected PV power system with a power of 252.6 kW was created in the MATLAB/Simulink environment. In this power system, six different PS conditions, containing quite challenging situations, were listed in three different scenarios and simulated. The proposed algorithm was compared with the voltage scanning and P&O and voltage scanning and variable-step P&O methods. It was observed that the proposed algorithm has lower power fluctuations compared to the other two traditional methods. Additionally, this algorithm managed to achieve higher efficiency than the other methods in some cases. Full article

Regular inspections of photovoltaic (PV) installations are an important procedure for optimizing their efficiency and prolonging their life expectancies; however, this might be a costly activity. One of the alternatives is visual monitoring using cameras with different spectra. This study presents the design and development of an experimental facility to conduct a wide range of thermographic studies. Initially, the common non-invasive inspection technologies are reviewed; then, an appropriate one is chosen according to the requirements of the lab. Next, the designed facility is analyzed in terms of resolution and accuracy, and the pixel count per solar cell is obtained for the different areas of the installation site. This study also analyzes the possible research activities that could be implemented within the created facility. They have been summarized in four categories: the identification of the type of soiling over a PV surface; the modeling of a string efficiency decrease as a function of partial shading/soiling of its surface; the investigation of the influence of distance on the accuracy and uncertainty of the performed IR measurements; and the continuous long-term monitoring of the PV installation. Examples of the above categories are also shown, which demonstrate the identification of typical urban PV exploitation situations with the infrared camera. Full article

Incorporating bypass diodes within photovoltaic arrays serves to mitigate the negative effects of partial shading scenarios. These situations can lead to the appearance of multiple peaks in the performance of solar panels. Nevertheless, there are cases where conventional maximum power point tracking (MPPT) techniques could encounter inaccuracies, causing them to identify the highest power point within a specific area (the local maximum power point; LMPP) instead of the overall highest power point across the entire array (the global maximum power point; GMPP). Numerous methods based on artificial intelligence (AI) were proposed to address this issue; however, they frequently used cumbersome and unreliable methodologies. This research presents the energy-valley-optimizer-based optimization (EVO) technique, which is designed to efficiently and dependably tackle the issue of partial shading (PS) in detecting the maximum power point (MPP) for photovoltaic (PV) systems. The EVO algorithm enhances the speed of tracking and minimizes power output fluctuations during the tracking phase. Through the utilization of the Typhoon hardware-in-the-loop (HIL) 402 emulator, extensive validation of the proposed technique is conducted. The effectiveness of the suggested method is compared with the established cuckoo search algorithm for achieving maximum power point tracking (MPPT) within a photovoltaic (PV) system. This comparison takes place under equivalent conditions to ensure a fair performance evaluation. Full article

This research articles proposes a comparative symmetric examination of the four maximum-power point-tracking algorithms under partial shaded conditions, including Conservative MPPT, Grey Wolf Optimization, and Fast Terminal Sliding Mode MPPT. In addition, the genetic algorithm MPPT, particle-swarm optimization MPPT, Grey Wolf optimization MPPT, and perturb and observe MPPT are included in this study. A smart method was to use Fast Terminal Sliding Mode Grey Wolf Optimization algorithms under partly shadowed situations on a global MPPT. This study included a GWO methodology that successfully set the efficient FTSM controller parameters such that the global maximum-power photovoltaic (PV) device point-tracking was monitored under partial shading. In the MATLAB setting and test performance, it was programmed for the suggested methodology of changing the shade pattern status dynamically. The findings were assessed and compared with other symmetric algorithms. Unlike other algorithms, the methodology used herein wasa less accurate way to monitor a global MPP. These methods were also evaluated and contrasted with PV arrays under various partial shadowing circumstances. The experimental investigations were used to verify the control techniques and stability analysis based on the asymmetric nonlinear load method of analysis in a closed-loop design. Full article

The search for charging electric vehicles using renewable energy sources and ensuring the stability of the electrical system has been growing. This has led to the development of charging stations that integrate photovoltaic systems, energy storage systems in batteries, and charging stations. The storage system allows for applications that increase the reliability of the electrical network. The objective of this work is to study, based on real operational scenarios, the impacts, and criteria of energy quality of a charging solution containing a photovoltaic system, a storage system, and a charging station. In the solution, the batteries are lead-carbon type and photovoltaics have priority in energy supply, using excess generation to charge the batteries. In this aspect, the work presents relevant contributions to the study of energy quality. An improvement in energy quality is pointed out with the use of batteries, proving that even in the most diverse operating situations, such as partial shading of the photovoltaic modules, with 20% shading relative to the nominal power, charging is not impacted and reductions in distortions are achieved, with a 50% reduction in voltage imbalance, when the storage system is a voltage reference and reference to the microgrid. Full article

In partial shading situations, the power–voltage (P–V) characteristics of photovoltaic (PV) systems become more complex due to many local maxima. Hence, traditional maximum power point tracking (MPPT) techniques fail to recognize the global maximum power point (MPP), resulting in a significant drop in the produced power. Global optimization strategies, such as metaheuristic approaches, efficiently address this issue. This work implements the recent “particle swarm optimization through targeted position-mutated elitism” (PSO-TPME) with a reinitialization mechanism on a PV system under partial shading conditions. The fast-converging and global exploration capabilities of PSO-TPME make it appealing for online optimization. PSO-TPME also offers the flexibility of tuning the particle classifier, elitism, mutation level, and mutation probability. This work analyzes several PSO-TPME parameter settings for the MPPT of partially shaded PV systems. Simulations of the PV system under varying shading patterns show that PSO-TPME, with balanced exploitation–exploration settings, outperforms PSO in terms of convergence speed and the amount of captured energy during convergence. Furthermore, simulations of partial shading conditions with fast-varying, smooth, and step-changing irradiance demonstrated that the proposed MPPT technique is capable of dealing with these severe conditions, capturing more than 97.7% and 98.35% of the available energy, respectively. Full article

This paper presents a novel architecture for simultaneous estimation of highly accurate optical flows and rigid scene transformations for difficult scenarios where the brightness assumption is violated by strong shading changes. In the case of rotating objects or moving light sources, such as those encountered for driving cars in the dark, the scene appearance often changes significantly from one view to the next. Unfortunately, standard methods for calculating optical flows or poses are based on the expectation that the appearance of features in the scene remains constant between views. These methods may fail frequently in the investigated cases. The presented method fuses texture and geometry information by combining image, vertex and normal data to compute an illumination-invariant optical flow. By using a coarse-to-fine strategy, globally anchored optical flows are learned, reducing the impact of erroneous shading-based pseudo-correspondences. Based on the learned optical flows, a second architecture is proposed that predicts robust rigid transformations from the warped vertex and normal maps. Particular attention is paid to situations with strong rotations, which often cause such shading changes. Therefore, a 3-step procedure is proposed that profitably exploits correlations between the normals and vertices. The method has been evaluated on a newly created dataset containing both synthetic and real data with strong rotations and shading effects. These data represent the typical use case in 3D reconstruction, where the object often rotates in large steps between the partial reconstructions. Additionally, we apply the method to the well-known Kitti Odometry dataset. Even if, due to fulfillment of the brightness assumption, this is not the typical use case of the method, the applicability to standard situations and the relation to other methods is therefore established. Full article

True sine wave DC-to-AC inverters are becoming more and more important in solar power generation in order to raise the system’s efficiency. A high-quality true sine wave DC-to-AC inverter can be built with a robust intelligent control method. This robust intelligent control method is comprised of improved sliding mode reaching law (ISMRL) and particle swarm optimization (PSO)—catfish effect (CE). The sliding mode reaching law is robust and insensitive to parameter variations and external disturbances. However, it has infinite system-state convergence times and steady-state errors. In addition, solar panels are often affected by partial shading, causing the output power–voltage characteristic curve to be multi-peaked. Such a situation causes misjudgment of the maximum power point tracking with conventional algorithms, which can neither obtain the global extremes nor establish high conversion efficiency. Therefore, this paper proposes an ISMRL based on PSO-CE applied to the tracking of maximum power in the case of partial shading of a solar power generation system. The ISMRL guarantees quick terminable time convergence, making it well-suited for digital implementation. In this paper, PSO-CE is used to find the global best solution of ISMRL, rejecting steady-state errors, slow convergence, and premature trapping in local optimums. Simulation and experimental results are verified using digital implementation based on a Texas Instruments digital signal processor to produce more accurate and better tracking control of true sine wave DC-to-AC inverter-based solar power generation systems. Full article

This paper describes the use of a Cascaded H-Bridge Multilevel Inverter to deal with the problem of partial shading in a photovoltaic system connected to the grid. Combined with the topology, it proposes the use of a virtual synchronous generator for power sharing between photovoltaic arrays and to ensure the connection to the power grid even in extreme shading situations with no power buffer. The experimental results demonstrate the feasibility of the strategy adopted and the limitations to overcome. Full article

In this work, a meta-heuristic optimization based method, known as the Firefly Algorithm (FA), to achieve the maximum power point (MPP) of a solar photo-voltaic (PV) system under partial shading conditions (PSC) is investigated. The Firefly Algorithm outperforms other techniques, such as the Perturb & Observe (P&O) method, proportional integral derivative (PID, and particle swarm optimization (PSO). These results show that the Firefly Algorithm (FA) tracks the MPP accurately compared with other above mentioned techniques. The PV system performance parameters i.e., convergence and tracking speed, is improved compared to conventional MPP tracking (MPPT) algorithms. It accurately tracks the various situations that outperform other methods. The proposed method significantly increased tracking efficiency and maximized the amount of energy recovered from PV arrays. Results show that FA exhibits high tracking efficiency (>99%) and less convergence time (<0.05 s) under PSCs with less power oscillations. All of these methods have been validated in Matlab simulation software. Full article

Because of the unpredictable activity of solar energy sources, photovoltaic (PV) maximum power point tracking (MPPT) is essential to guarantee the continuous operation of electrical energy generation at optimal power levels. Several works have extensively examined the generation of the maximum power from the PV systems under normal and shading conditions. The fuzzy logic control (FLC) method is one of the effective MPPT techniques, but it needs to be adapted to work in partial shading conditions. The current paper presents the FLC-based on dynamic safety margin (DSM) as an MPPT technique for a PV system to overcome the limitations of FLC in shading conditions. The DSM is a performance index that measures the system state deviation from the normal situation. As a performance index, DSM is used to adapt the FLC controller output to rapidly reach the global maxima of the PV system. The ability of the proposed algorithm and its performance are evaluated using simulation and practical implementation results for single phase grid-connected PV system under normal and partial shading operating conditions. Full article