Peak prediction method of maximum power point for series-parallel photovoltaic array under partial shading condition

Partial shading conditions (PSCs) can significantly reduce the output energy produced by photovoltaic (PV) systems. Moreover, when such conditions occur, conventional and advanced maximum power point tracking (MPPT) systems fail to operate the PV system at its peak because the bypassing diodes may cause the PV system to become trapped at a low power point when they are in conduction mode. The PV system can be operated at the global maximum power point (MPP) with the help of global peak searching tools. However, the frequent use of these tools will reduce the output of PV systems since they force the PV system to operate outside its power region while scanning the I-V curve in order to determine the global MPP. Thus, the global peak searching tools should be deployed only when a PSC occurs. In this paper, a simple and accurate method is proposed for detecting PSCs by means of monitoring the sign of voltage changes (positive or negative). The method predicts a PSC if the sign of successive voltage changes is the same for a certain number of successive changes. The proposed method was tested on two types of PV array configurations (series and series–parallel) with several shading patterns emulated on-site. The proposed method correctly and timely identified all emulated shading patterns. It can be used to trigger the global MPP searching techniques for improving the PV system’s output under PSCs; furthermore, it can be used to notify the PV system’s operator of the occurrence of PSCs.

This paper reviews the existing models, connection schemes and maximum power point tracking (MPPT) methods of PV arrays. The partial shading causes significant power losses. It is very difficult to maintain uniform irradiance over the entire PV array, especially for rooftop or building-integrated PV systems. The aspects such as connection scheme of PV array, shading pattern and maximum power point tracking (MPPT) techniques, etc., decide the power generated by the PV array. The conventional MPPT methods are only capable of reaching maximum power point (MPP) in identical insolation situations. Hence, conventional MPP tracking becomes inept due to the existence of several peaks in the P-V characteristics. There are different global maximum power point (GMPP) tracking techniques available in the literature that operate in partial shading scenarios. The reconfiguration of the PV array reduces the number of multiple peaks. This reduction in several peaks makes conventional MPP tracking techniques competent even under partial shading conditions with low implementation complexity and higher tracking speed. This paper can assist scientists in choosing a precise objective-based PV module model, connection schemes and MPP tracking methods out of numerous schemes available in the literature.

One of issues about array photovoltaic (PV) is partial shading conditions. It can cause mismatch between the modules, leading to undesirable effects such as reduction in power generated and hot spots. Partial shading is caused by passing clouds, tree shadows, nearby building, tower, poles, etc. So the power output is less than expected value. It is not only depended a number of shaded modules but also depended on array PV configuration. This paper analyzed two array PV configurations i.e., series-parallel (SP) configuration and total-cross-tied (TCT) configuration under partial shading conditions. In this experiment SP configuration (2×2 sized) and T CT configuration (2×2 sized) was made and conducted analyzed on partial shading conditions with several patterns. Both SP configuration and TCT configuration at measuring are performed at the same time for each pattern. The reference for measurement is solar irradiance at 1000 W/m2. Based on the results obtained the performance of TCT configuration is better than SP configuration under partial shading condition. Because TCT configuration is more internal connection than SP configuration, which provides more current paths so reducing power losses.

Performance analysis of mono crystalline, poly crystalline and thin film material based 6 × 6 T-C-T PV array under different partial shading situations

The power versus voltage curve of a photovoltaic (PV) panel exhibits several maximum power points (MPPs) in a partial shading (PS) condition. Thus, it remains an optimization challenge to ensure that PV systems operate at their global MPP (GMPP). Scanning the output characteristics of the PV panels seems a general solution for this issue. However, applying a short circuit to the terminal of PV panels where there exists an electrolytic capacitor, has a detrimental effect on the lifetime of the system. To this end, in this paper, a GMPP estimator is proposed as a global solution for conventional MPPT algorithms under PS conditions. The proposed technique improves existing simple MPPT algorithms with original approaches as follows: 1) An accurate microscopic analysis of a PV characteristic in PS conditions is considered; 2) an original definition of the dominant cells and modules in a PV panel is proposed that allows to reduce the PS patterns to a finite number and 3) the search area for the MPPT operation is reduced to find the accurate GMPP by proposing two voltage boundaries. The lower boundary corresponds to the GMPP under uniform shading condition that can be determined using a closed form formula, while the upper one refers to the GMPP of a dominant cell in a PV module that can be determined using an artificial intelligence technique. This can also help set the initial duty cycle in a convex area around the GMPP. The functionality of the proposed GMPP estimator is experimentally validated.

Solar modules under partial shading (PS) conditions will result in power and voltage characteristic curves (P-VCC) having multiple peaks. If the maximum power point cannot be obtained, the output power of the solar modules will be greatly reduced. Hence, there have been various maximum power point tracking (MPPT) control methods developed to address this problem. One alternative is to employ the meta-heuristic approach (MHA) to track the global maximum power point (GMPP). Recently, a new MHA called Bat Algorithm (BA) has performed well in the MPPT. Nevertheless, BA may fail to track the GMPP when there are some local maximum power points (LMPPs) close to the GMPP. Also, the tracking time needs to be further reduced to accommodate rapidly changing irradiance. Therefore, a combination of BA with the abandonment mechanism of Cuckoo Search (CS) is proposed to improve the tracking performance of the BA. Both simulation and experimental results show that the proposed method, as compared to BA, yields better accuracy and an improvement of convergence speed of about 35% for various P-VCCs can be achieved. Moreover, the MBA has also been tested against some of the state-of-the-art MPPT algorithms such as Particle Swarm Optimization and Grey Wolf Optimization (GWO), and the results showed the superiority of the proposed method.

The solar photovoltaic water pumping system is one of the major essential renewable energy applications, especially in rural areas. This work aimed to design four modules PV array for a PV based water pumping system and to achieve greater output power on varying partial shading and irradiation condition. The four-modules PV array adjusts the electrical configuration of panel depends on the insolation observed by the solar panels and Fully and Partial Shaded Condition (PSC). In this work the Switching network, reconfigurable program and reconfigurable PV controller units are proposed for consistent process of reconfigurable solar PV array. The proposed controller unit is designed and simulated in Simulink software to calculate system operation and output power under changing irradiance and PSC. The small scale prototype is developed in order to validate the results. The outdoor test results and its analysis shows the reconfigurable PV array is able to deliver maximum output on varying operating conditions. Maximum 53.87% percentage of power and 52% percentage running time of pump can be increased by using this reconfiguration strategy.

Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions

Partial shading condition (PSC) is regular phenomena in PV system, in which some sections of PV modules in an array are partly covered owing to the large building, moving clouds, dirt or big trees. The partial shading effect is limiting the array's power output and exhibits multiple peaks on the Power-Voltage (P-V) & Current-Voltage (I-V) characteristics. As a result, raises the power losses and decreases the array's performance. Suitable arrangement of the PV array configurations is one of the strategies for lowering the power losses. The primary goal of this paper is to provide unique hybrid PV array configuration arrangement, in which the benefits of conventional configurations have been combined. The hybrid PV array topologies which are presented in this paper are Series Parallel-Total Cross Tied (SPTCT), Honey Comb- Total Cross Tied (HCTCT), Bridge Linked- Total Cross Tied (BLTCT). The performance of the PV array topologies is evaluated in terms of maximum power generation, mismatch loss, fill factor by considering five distinct PSCs.

A maximum power point tracking (MPPT) controller was used to make the photovoltaic (PV) module operate at its maximum power point (MPP) under changing temperature and sunlight irradiance. Under partially shaded conditions, the characteristic power–voltage (P–V) curve of the PV modules will have more than one maximum power point, at least one local maximum power point and a global maximum power point. Conventional MPPT controllers may control the PV module array at the local maximum power point rather than the global maximum power point. MPPT control can be also implemented by using soft computing methods (SCM), which can handle the partial shade problem. However, to improve the robustness and speed of the MPPT controller, a hybrid MPPT controller has been proposed that combines two SCMs, the Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Matlab was used in a simulation of a GA-ACO MPPT controller where four SunPower SPR-305NE-WHT-D PV modules with a maximum power of 305.226 W connected in series were used under conditions of partial shade to investigate the performance of the proposed MPPT controller. The results obtained were analyzed and compared with others obtained under perturb and observe (P&O) MPPT and conventional ACO MPPT controllers were observed.

K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems

This paper presents an experimental investigation on photovoltaic array (PV array) power output affected by partial shading conditions (PSCs). An experiment setup of a PV array with a series configuration using 2 × 4 photovoltaic modules (PV modules) was built. The power output loss due to the shading effect on the first photovoltaic cells (PV cell) connected with bypass diodes of each photovoltaic module, installed in the PV array in the horizontal direction, was evaluated. Depending on the direction of the sun relative to the PV array configuration, the shading percentage was measured during the test and recorded the current and voltage of the PV array. The performance evaluation of the PV array configurations is referred to with respect to the values of maximum power voltage, the maximum power current, maximum power output, power output losses and fill factor (FF). The experimental results show that 44% shading of the first PV cells affects PV array power output loss by more than 80%.

Asymmetrical interval type-2 fuzzy logic control based MPPT tuning for PV system under partial shading condition

The power output of a photovoltaic (PV) system is inherently dependent on climatic factors. To maximize the energy harvested from PV arrays, maximum power point tracking (MPPT) algorithms are employed. These algorithms dynamically adjust the operating point of the system to extract the maximum available power. However, under partial shading conditions (PSCs), conventional MPPT algorithms often fail to locate the global maximum power point, leading to suboptimal power extraction. In this study, a robust MPPT technique based on sliding mode control (SMC) is proposed to enhance tracking efficiency and optimize power extraction from PV arrays under PSC. Particle swarm optimization (PSO) is incorporated into the MPPT framework, enabling the dynamic tuning of SMC parameters for improved adaptability and performance. The proposed SMC structure is designed to regulate the duty cycle of a boost converter, ensuring effective power conversion. The system is simulated in Matlab/Simulink for various PSCs. The simulation results demonstrate that the PSO-tuned SMC methodology exhibits superior tracking performance, enabling the PV system to rapidly and accurately converge to the true MPP under varying weather and shading scenarios. The findings indicate that the proposed technique enhances the efficiency and reliability of PV energy harvesting in PSCs.

Photovoltaic (PV) systems are increasingly used as a sustainable energy source, but their efficiency is significantly affected by environmental factors such as shading and temperature variations. This study proposes a novel Maximum Power Point Tracking (MPPT) algorithm based on the fusion of Support Vector Regression (SVR) and Incremental Conductance (INC) techniques, designed to enhance the performance of PV systems under partial shading conditions. The SVR model is employed to predict the power output of the PV system based on voltage and current measurements, leveraging its capability to handle non-linear relationships with high accuracy. The INC method is then integrated with the SVR predictions to dynamically adjust the operating point of the PV array, ensuring rapid and precise tracking of the maximum power point (MPP). The algorithm was implemented in MATLAB, and comprehensive simulations were conducted to evaluate its performance. Simulation results demonstrate that the SVR-INC fusion algorithm significantly improves the accuracy and stability of MPPT under varying shading conditions. The algorithm effectively reduces the tracking time to the MPP and minimizes power losses caused by partial shading. Compared to traditional MPPT methods, the proposed algorithm shows enhanced robustness and faster convergence, making it highly suitable for practical applications in real-world PV systems. Furthermore, the integration of SVR with INC provides a robust framework for dealing with the complexities of $P V$ power generation. The algorithm’s ability to adapt to changing environmental conditions ensures that the PV system operates at its maximum efficiency. The study also discusses the optimization of SVR parameters and the importance of datadriven approaches in enhancing MPPT performance. In conclusion, the proposed SVR-INC fusion MPPT algorithm offers a promising solution for improving the efficiency and reliability of PV systems under partial shading. The findings highlight the potential of this approach for future advancements in PV technology, contributing to the development of more efficient and resilient solar energy systems..