An instance segmentation based global MPPT method for PV systems under partial shading condition

Implementation of photovoltaic systems encounters problems, particularly concerning Partial Shading Conditions (PSC), solar irradiance, and temperature, which influence the generated output power. The PSC can diminish the power efficiency of the photovoltaic system. Consequently, a controller is required to optimize the photovoltaic system’s power output by considering the power supply characteristics. This paper discusses optimal power control in photovoltaic system under PSC. The proposed method employs a Modified Perturb and Observe (MP&O) algorithm based on the observation of current and voltage output from the photovoltaic system. The MP&O algorithm is integrated into a microcontroller and will provide PWM signals to operate the synchronous buck converter. Testing was performed under PSC. The experimental results indicated that the synchronous buck converter achieved a performance efficiency of 85%. The efficacy of the MP&O algorithm was evaluated without the MPPT method and conventional P&O algorithm. The MP&O algorithm outperformed compared to without MPPT method and conventional P&O algorithm. The MP&O algorithm yielded more consistent output power and necessitated a quicker tracking duration. The proposed method achieves an average output power efficiency of 84%; in contrast, without the MPPT method, it only reached 57%, and with the conventional P&O algorithm, it attains an efficiency of just 70%.

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.

INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Partial shading (PS) conditions pose additional challenges, leading to multiple peaks in the power-voltage (P-V) curve and reduced output power. Therefore, controlling MPPT under partial shading conditions is a complex task. OBJECTIVES: This study aims to introduce a novel MMPT algorithm based on the ant colony incorporated bald eagle search optimization (AC-BESO) method to enhance the efficiency of PV systems. METHODS: The effectiveness of the proposed MPPT algorithm was established through a series of experiments using MATLAB software, tested under various levels of solar irradiance. RESULTS: Compared to existing methods, the proposed AC-BESO algorithm stands out for its simplicity in implementation and reduced computational complexity. Furthermore, its tracking performance surpasses that of conventional methods, as validated through comparative analyses. CONCLUSION: This study confirms the efficacy of the AC-BESO method over traditional strategies. It serves as a framework for selecting an MPPT approach when designing PV systems.

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.

To achieve optimal energy extraction from photovoltaic array during partial shading (PS) condition, a maximum power points tracker (MPPT) with good performance is highly demanded. One of the main criteria of a good MPPT is the accuracy to track the maximum output power with minimal time. Therefore, few equations are proposed by the researchers to predict the peak voltage (V lp ) of PV array during PS occurrences. The equations basically claim that it can assist to improve the tracking efficiency and speed by predicting the maximum power point (MPP). But, it never was proved that those equations are valid for all PS patterns that can be occurred. Considering the above fact, this paper focus on analyzing the credibility of two maximum power point prediction methods namely (1) 0.8 Voc method and (2) improved 0.8 Voc method. Test performance is carried with five shading patterns of 5×4 series-parallel (SP) configuration of PV array. Results confirm that the equation is only valid for the shading that has similar irradiance level, for each of the strings inside the PV array.

Monthly energy yield assessment of solar photovoltaic system under uniform irradiance and partial shaded condition

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

Harvesting solar energy as a renewable source to meet the expanding energy demand is essential for sustainable growth and a greener planet. The performance of the photovoltaic panels is affected by factors such as partial shading, elevated temperature, mismatch between the cell parameters, etc., which contribute to the loss in an inter-correlated manner. In the current study, we isolate the contribution from each factor through experiments. The partial shading is configured through distributed paper chips in different patterns keeping the overall shaded area constant. The isolation of shading loss from the total loss allows us to find out the total contribution from the mismatch and temperature factors. In a further set of experiments, we isolate these two factors as well. We find that the major contribution comes from the shading loss. We also observe that not only the fraction of the partial shading but also the shading pattern has a crucial bearing on the panel performance. For equal fraction of the partial shading, the more distributed the shade, the less the loss. The next major contributor is the temperature loss for which we find out empirical formulae to predict the performance under different operating conditions. The overall effort is aimed at estimating the operational efficiency of the PV panels under soiled or partially shaded conditions.

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.

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.

This paper presents a study and comparative analysis of particle swarm optimization (PSO) and grey wolf optimization (GWO) to track global maximum power point (GMPP) of photovoltaic (PV) array under partial shading conditions (PSCs). Tracking GMPP of PV array under PSCs is difficult for classical algorithms like, hill climbing (HC), perturb and observe (P& O), increment conductance, etc. Under PSCs, there exists multiple local maxima and one global maximum in P-V curve of the PV array, and these algorithms are entrapped to the local maxima. Therefore, to track GMPP, many artificial intelligence based, optimization based and hybrid GMPP tracking algorithm are proposed. In this paper, the working principles of PSO and GWO based GMPP tracking algorithms are discussed for better understanding. The tracking performance of both algorithms under different PSCs are examined on MATLAB software using a resistive load connected PV system. Further, their energy losses, tracking error and tracking power efficiency are calculated and compared for further clarification.

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.

Genetic Algorithm-based Model Predictive Control for Partial Shading Mitigation on PV Array

Under partial shading conditions (PSCs), photovoltaic (PV) generation systems exhibit multiple local and a single global maximum power point. Consequently, global maximum power point tracking (GMPPT) is required to improve the performance of PV systems in such scenarios. This paper comparatively studies and evaluates the tracking performance of modern heuristic-optimization-based GMPPT techniques. Monte Carlo method is used to statistically analyze different methods. Simulation and experimental results indicate that many of the algorithms have difficulties in balancing the explorative and exploitative searching behaviors. Therefore, we propose a variable vortex search (VVS), which is capable of improving the performance of GMPPT by using a variable step size and deterministic starting points. This paper will aid researchers and practical engineers to gain a thorough understanding on how to use modern heuristic algorithms for maximum power out of PV systems. Furthermore, it offers a comprehensive guidance on how to perform efficiently GMPPT in the PV systems under PSCs.

Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC