Maximum power point tracking strategy for photovoltaic system based on probability

This paper proposes a hybrid photovoltaic (PV) Maximum Power Point Tracking (MPPT) strategy to tackle local optima, slow dynamic response, and steady-state oscillations under partial shading conditions (PSC). The method combines an Improved Whale Migration Algorithm (IWMA) with a variable-step Incremental Conductance (VINC) technique. IWMA employs a Tent–Logistic–Cosine chaotic initialization, dynamic weight coefficients, random feedback, and a distance-sensitive term to enhance population diversity, strengthen global exploration, and reduce the risk of convergence to local maxima. The VINC stage adaptively adjusts the step size based on incremental conductance, providing fine local refinement around the global maximum power point (GMPP) and suppressing steady-state power ripple. Extensive MATLAB/Simulink simulations with multiple random trials show that the proposed IWMA-VINC strategy consistently outperforms the Whale Migration Algorithm (WMA), A Simplified Particle Swarm Optimization Algorithm Combining Natural Selection and Conductivity Incremental Approach (NSNPSO-INC), and the Grey Wolf Optimizer and Whale Optimization Algorithm (GWO-WOA) under both static and dynamic PSC, achieving the highest tracking accuracies (99.74% static, 99.44% dynamic), higher average output power, shorter convergence times, and the smallest variance across trials. These results demonstrate that IWMA-VINC offers a robust and high-performance MPPT solution for PV systems operating in complex illumination environments.

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.

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.

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.

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

To get the most power out of photovoltaic (PV) panels, PV systems must utilize a maximum power point tracking (MPPT) controller. In partial shading conditions (PSC), the power-voltage (P-V) characteristic of the PV network may show a single global maximum power point (GMPP) and two or more local maximum power points (LMPP). This indicates that the PV cells and panels do not get uniform illumination. As they converge on the maximum power point (MPP) that makes contact first, which is often one of the LMPPs in this scenario, common MPPT approaches like incremental conductance (InC) and perturb and observe (P&O) are unable to distinguish between a GMPP and LMPPs. In this paper, the extraction of the GMPP of the PV system under PSC based on a suggested particle swarm optimization (PSO) approaches is presented. The particularity of the suggested approach is that it takes into account the calculation of the position of each particle as a function of the duty cycle and the global maximum power. Results on the performance of the suggested PSO method show an advantage over the conventional PSO and the commonly used traditional P&O method. The suggested PSO technique offer better performance in terms of global power extracted, ripple rate of the power and efficiency.

Partial shading conditions (PSCs) in Photovoltaic (PV) system is an inevasible situation which curtails the PV array output by exhibiting multiple peaks in its Power-Voltage (P-V) curve. The multiple peaks consist of a single global maximum power point (GMPP) and many local maximum power points (LMPP). The presence of multiple peaks makes tracking of maximum power point more difficult and demands an efficient controller to track the global peak of the P-V curve. In the present work, a novel intelligent asymmetrical Fuzzy Logic Control (AFLC) based maximum power point tracking (MPPT) algorithm has been proposed for tracking GMPP. The fuzzy membership functions of the proposed algorithm have been optimized using a heuristic approach. The algorithm has been designed, developed and analyzed using MATLAB/Simulink. Furthermore, to establish the superiority of proposed AFLC algorithm, it has been compared with conventional perturb & observe (P&O) algorithm and intelligent Fuzzy Logic Control (FLC) based algorithm for GMPP tracking and shading losses under standard test condition (STC) and partially shaded conditions.

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

The use of solar energy as an alternative energy source continues to increase along with global energy needs and environmental awareness. However, the efficiency of the photovoltaic (PV) panel system is greatly influenced by variations in sunlight intensity, especially in partial shading conditions. This condition causes the emergence of several local maximum power points, which makes it difficult for conventional MPPT (Maximum Power Point Tracking) systems to find the global maximum power point (GMPP). In this study, an MPPT method based on the Firefly Algorithm (FA) was developed, a metaheuristic optimization algorithm inspired by the behavior of fireflies in attracting partners through light intensity. This method was chosen because of its ability to explore non-linear search spaces and avoid traps at local maximum points. The study was conducted through modeling of the PV panel system and simulations in various partial shading scenarios using MATLAB/Simulink software. The Firefly Algorithm was then applied to search for the maximum power point and its performance was compared with conventional MPPT methods such as Perturb and Observe (P&O) and Incremental Conductance (IC). The discussion plan includes analysis of power tracking efficiency, convergence time, and system stability under changing lighting conditions. It is expected that the results of this study can contribute to the development of a more adaptive and efficient MPPT control system for modern solar power systems, especially in areas with high levels of shade.

The position of MPP of a solar panel changes with changes in weather conditions such as radiation, temperature and partial Shadow. In such a situation, several local MPPs and one GMPP are created in the P-V curve, and the common methods of MPPT(P&O, IC,…) have converged due to the failure to recognise the local MPP. They cause a lot of losses to the photovoltaic system. Therefore, in this article, it has been tried to provide a new method of particle swarm optimisation with variable coefficients, which in combination with the perturb and observe method can reach the GMPP in the fastest possible time. In this proposed method, we first reach the convergence of the GMPP using the VCPSO method, and after converging to it, we go to the P&O method and use P&O until significant changes are made in the system, and if observed Variations To re-examine the GMPP, we return to the VCPSO algorithm. The results of the simulation of the efficiency of the proposed method show that this method was able to reach the GMPP in the shortest time, and the efficiency of the photovoltaic system increases and the effects of partial shading are reduced.

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

The presence of bypass diodes in photovoltaic (PV) arrays can mitigate the negative effects of partial shading conditions (PSCs), which can cause multiple peak characteristics at the output. However, conventional maximum power point tracking (MPPT) methods can develop errors and detect the local maximum power point (LMPP) instead of the global maximum power point (GMPP) under certain circumstances. To address this issue, several artificial intelligence (AI)‐based methods have been proposed, but they result in complicated and unreliable methodologies. This study introduces the driving training‐based optimization (DTBO) method, which aims to address the partial shading (PS) problem quickly and reliably in maximum power point (MPP) detection for PV systems. DTBO improves tracking speed and reduces fluctuations in the power output during the tracking period. The proposed method is extensively verified using the Typhoon hardware‐in‐the‐loop (HIL) 402 emulator and compared to conventional methods such as particle swarm optimization (PSO), and JAYA, as well as the recently proposed adaptive JAYA (AJAYA) method for MPPT in a PV system under similar conditions.

Partial shading is an unavoidable condition which significantly reduces the efficiency and stability of a photovoltaic (PV) system. When partial shading occurs the system has multiple-peak output power characteristics. In order to track the global maximum power point (GMPP) within an appropriate period a reliable technique is required. Conventional techniques such as hill climbing and perturbation and observation (P&O) are inadequate in tracking the GMPP subject to this condition resulting in a dramatic reduction in the efficiency of the PV system. Recent artificial intelligence methods have been proposed, however they have a higher computational cost, slower processing time and increased oscillations which results in further instability at the output of the PV system. This paper proposes a fast and efficient technique based on Radial Movement Optimization (RMO) for detecting the GMPP under partial shading conditions. The paper begins with a brief description of the behavior of PV systems under partial shading conditions followed by the introduction of the new RMO-based technique for GMPP tracking. Finally, results are presented to demonstration the performance of the proposed technique under different partial shading conditions. The results are compared with those of the PSO method, one of the most widely used methods in the literature. Four factors, namely convergence speed, efficiency (power loss reduction), stability (oscillation reduction) and computational cost, are considered in the comparison with the PSO technique.

An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.