Swarm Optimization based PV Array Re-configuration to Achieve Higher GMPP during Partial Shadowing Situations

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.

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.

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

Among all the conventional maximum power point tracking (MPPT) techniques for a photovoltaic (PV) system that have been proposed, incremental conductance (INC) and perturb and observe (P&O) are the most popular because of their simplicity and ease of implementation. However, under partial shading conditions (PSCs), these MPPT algorithms fail to track the global maximum power point (GMPP) and instead converge into local maximum power points (LMPPs), resulting in considerable PV power loss. This paper presents a new hybrid MPPT technique combining the artificial bee colony (ABC) and P&O algorithms named ABC-P&O. The P&O technique is used to track the MPP under uniform irradiance, and only during irradiance variations is the ABC algorithm employed. The effectiveness of the proposed hybrid algorithm at tracking the GMPP, under both uniform and nonuniform irradiance conditions, was assessed by hardware-in-the-loop (HIL) tests employed by a dc–dc boost converter. Then, the ABC-P&O strategy was applied to obtain the voltage reference for the outer PI control loop, which provided the current reference to the discrete-time sliding-mode current control. The ABC-P&O algorithm has a reasonable computational cost, allowing the use of a commercial, low-priced digital signal controller (DSC) with outer voltage and inner current control loops. Many challenging tests validated that the proposed ABC-P&O technique converges fast to the GMPP with high efficiency and superior performance under different PSCs.

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.

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.

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.

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.

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.

Partial shading (PS) is a major challenge faced by photovoltaic (PV) arrays invoking mismatch loss (ML). In this paper, the effect of PS on PV array performance is analyzed through experimental results and MATLAB simulation. The effect of PS on PV characteristics is investigated in two conditions. In condition one, modules are not coupled with bypass diodes, and in condition two modules are equipped with bypass diodes.New mathematical equations have been derived to calculate ML for condition one, and for condition two; locations of power peaks are identified in the power-voltage (P-V) curve. For both the conditions, theoretical results are verified through experimental results. From the obtained results it is observed that the error in loss calculation for condition one is less than 2% and for condition two, power peaks are being identified analytically with less than 3% error. To reduce the effect of PS on PV array, different configurations of the PV array were considered in the earlier literature. Among all, Total Cross Tied (TCT) PV array configuration is popularly used for its improved performance. To curtail the cost of TCT, two-hybrid array configurations such as TCT with bridge link (TCT+BL) and TCT with honey-comb (TCT+HC) are proposed here. Performances of different array configurations are analyzed considering various parameters such as global maximum power point (GMPP), currents and voltages at GMPP, the number of local peaks, shading losses, and mismatch losses. It is observed that proposed array configurations outshine in performance by reducing the amount of ML and the number of local peaks in the P-V graph, mainly for the stochastic shading patterns.

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.

This study explores the efficacy of supervised machine learning regression models in precisely forecasting the Global Maximum Power Point (GMPP) in Photovoltaic (PV) systems under Partial Shading Conditions (PSC). Various supervised machine learning models are examined for the suitability of GMPP tracking during PSC. The study evaluated the effectiveness of Neural Network architectures, including Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (Bi LSTM), Recurrent Neural Networks (RNN), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) in predicting GMPP. The data set for training was obtained from the PV system, modelled in MATLAB Simulink. All regression models underwent rigorous training in the MATLAB Simulink environment, followed by an analytical review of their performance, during partial shading scenarios. Comparative analysis conducted across different regression models to evaluate their effectiveness in predicting the GMPP in PV systems under PSC suggests that Neural Networks exhibit the capability to forecast the GMPP during PSC scenario. This finding underscores the potential of Feedforward Neural Network (FNN), particularly its deep variant, demonstrated superior performer in terms of predicting the GMPP.

In this paper, photovoltaic (PV) array system is reconfigured using the Futoshiki game theory to enhance the global maximum power point (GMPP). A comparison is carried out with the conventional PV arrangements such as series-parallel (SP) and total-cross-tied (TCT) arrangements in terms of low power loss (PL), improved fill factor (FF), and execution ratio (ER) under partial shading cases. Overall, the present study proves that the reconfigured PV array using game theory Futoshiki has higher performance compare to conventional methods. The performance indices are measured in term higher GMPP, minimized PL, improved FF for proposed methods to prove the system feasibility under test scenarios. Present study is performed through MATLAB/Simulink study.

Comprehensive analysis of line losses and valuation of energy savings in optimized photovoltaic array subjected to partial shadings

SummaryEven with significant progresses in the maximum power point (MPP) research area, the necessity to improve the existing methods becomes mandatory to increase the energy conversion efficiency. Since the power–voltage (P‐V) characteristic curve of photovoltaic (PV) arrays has multiple peaks under partially shaded (PS) conditions, the conventional MPP tracking (MPPT) control methods have the difficult challenge of locate the global MPP (GMPP) among many local MPPs (LMPPs). In recent years, numerous research papers have been focused on techniques to efficiently track the GMPP and alleviate the partial shading effects. One of the most popular evolutionary search technique is particle swarm optimization (PSO) that provides high tracking speed and the ability operate under different environmental conditions. For solving some conventional PSO technique common weaknesses, several modifications and improvements have emerged in the past years. This paper provides a comparative and comprehensive review of some relevant PSO‐based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading, and efficiency. The simulation results are validated under numerous test conditions using MATLAB code and Simulink package.