Variable DC-Link Voltage Algorithm with a Wide Range of Maximum Power Point Tracking for a Two-String PV System

Due to the Randomness and fluctuation of the photovoltaic (PV) system, Maximum Power Point Tracking (MPPT) algorithm is employed to ensure the maximum power extraction from PV arrays, when it is connected to the grid. The previous studies have indicated that the MPPT algorithm is one of the main causes of interharmonics emission in PV systems. The MPPT algorithm parameters such as perturbation step size inevitably have a strong impact on the interharmonics in the current injected to the grid. In general, a smaller MPPT perturbation step size will reduce the interharmonics emission level, but it will result in poor tracking performance of the MPPT algorithm. Thus, there is a trade-off between the MPPT efficiency and the interharmonics emission in PV system. In this paper, an interharmonics suppression scheme with variable step size MPPT algorithm was proposed. In the suppression scheme, two voltage values near by the MPPT voltage and two tracking step size are preset. When the dc-link voltage of PV is between the two preset voltage values, it means that the PV power near its MPP and the smaller perturbation step size implemented in the MPPT algorithm. Otherwise, the bigger perturbation step size is employed. It can greatly reduce interharmonics emission level and maintain the MPPT efficiency. Simulations have been carried out to verify the effectiveness of the suppression scheme.

Solar photovoltaic (PV) system contributes a significant role in meeting power requirements and helps the decentralization of power generation and distribution. The output of the individual photovoltaic module depends on local temperature and irradiance. Since the variation of these parameters depends on the environmental factors, it is difficult to obtain maximum power in all instances. The maximum power point tracking (MPPT) technique is used for tracking the maximum power available at a particular instant. This paper presents the maximum power extraction from a photovoltaic system using Artificial Intelligence (AI) techniques. AI based methods provide a fast dynamic response with high accuracy and mitigate the effects of partial shading. A comparative study is performed for various AI based MPPT techniques such as Fuzzy, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS). PV module output is interfaced with the resistive load through the Z-Source inverter (ZSI). The ZSI boost the input voltage and provides an AC output voltage. The AI based MPPT techniques have been investigated with conventional Z-source Inverter (ZSI) and Switched Inductor Z-Source inverter (SL-ZSI). A simple boost control technique is employed to control, both conventional and SL Z-source inverter. The performance of the system is verified using MATLAB Simulink.

This paper presents a control for a three-phase an eleven-level cascaded H-bridge inverter for photovoltaic (PV) system. The maximum power point tracking (MPPT) is capable of extracting maximum power from the PV array connected to each DC link voltage level. The MPPT algorithm is solved by fuzzy logic controller. Space vector pulse width modulation (SVPWM) algorithm uses a simple mapping to generate gate signals for the inverter. This is done to achieve high dynamic performance with low total harmonic distortion (THD). The validity of the system is verified through MATLAB/Simulink and results are compared with three-phase three-level, five-level, seven-level and nine-level cascaded H-bridge inverter for PV system in terms of THD. Finally, simulation results are presented to verify the effectiveness and accuracy of the proposed system.

An off-grid photovoltaic (PV) system's PV array is connected with a charge controller as the first power conversion stage. The average efficiency of the power stage of charge controllers is around 90%. Supercapacitor (SC) battery hybrid PV system is a novel PV system that utilizes some of the wasted energy of a typical system and enhances the system's efficiency up to 98%. However, the feasibility of maximum power point tracking (MPPT) for this system is yet to be validated. This paper presents a comparative study to adapt MPPT for the proposed system consisting of a series-connected SC bank with PV array and step-down DC-DC converter. The step-down converter is used as the impedance matching network. Different solar irradiance profiles were emulated to check the feasibility and efficiency of MPPT. Experimentally, it was shown that the typical MPPT could be adapted to the proposed PV system with very high MPPT efficiency.

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.

Energy crises and the growth of the energy demand have increased the interest in utilizing unconventional power sources. Thus, renewable energy sources have become a topic of interest to mitigate rising energy concerns and cope with increased electricity demand. With remarkable merits including cleanness and abundance, photovoltaic (PV) solar energy systems are a key to solving these issues. The employed inverters should effectively utilize the maximum available power from the PV solar system and transfer this power to the utility grid without posing any further limitations. However, the unequal power generation of different PV systems caused by partial shading (PS) and other PV panel degradation factors leads to a reduction in generation capacity. One of the relatively new solutions to mitigate the mismatch concerns between the PV modules and sub-modules is to extract the maximum power of each sub-module individually. The main objective of this paper is to present a comprehensive review of such PV grid-connected inverters topologies associated with sub-module connection and control. It will classify the PV grid-tied inverters in accordance with the level where the maximum power point tracking (MPPT) system is implemented. A special focus has been placed on sub-module microinverters (MI) in terms of circuit topologies, conversion efficiency, and controller design. This paper provides a comprehensive analysis of employing the distributed MPPT (DMPPT) approach to maximize the power generation of PV systems by mitigating the mismatch issues inside the PV module. The circuit topology, PV system configuration, and MPPT algorithms used for applying DMPPT solutions in PV SMs are discussed in detail in this study.

This work presents an improved perturb and observe (P&O) algorithm based MPPT (Maximum Power Point Tracking) for a single stage solar photovoltaic (PV) array integrated with a three phase grid. Due to simple implementation and efficient tracking, the P&O approach of extracting maximum power from a PV array is very popular and efficient tracking technique among all MPPT techniques. However, the major pitfalls of this technique are that P&O MPPT is affected from drift, the outcome of drift occurring is much disapproving when there is a quick rate of increment in insolation. Drift takes place because of erroneous judgments taken by traditional P&O technique at the initial step alteration in voltage reference throughout the rise in insolation. An improved P&O algorithm is proposed here to improve the drift issue by deploying the details of variation in PV current (ΔDLI <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pv</inf> ) to make the judgment in extension to the variation in PV power (ΔDLP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pv</inf> ) and variation in PV voltage (ΔDLV <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pv</inf> ). In this PV system, a VSI (Voltage Source Inverter) and three phase grid are used. In a three phase distribution feeder network for indemnify, the quality of power as per an IEEE-519 standard on harmonics, it satisfies the power quality norms. So, for availing the real power feeding to the grid and to sustain the terminal voltage constant at the PCC (Point of Common Coupling) with sinusoidal grid voltages and grid currents, this system works well simultaneously with improved performance of P&O technique. The Matlab/Simulink is used for simulation and modelling of proposed system and the behavior of proposed MPPT algorithm has been examined for various insolation levels and different environmental situations on an experimental system which is established in the laboratory

The increasing use of photovoltaic (PV) systems for sustainable energy faces two major challenges: tracking the global maximum power point (GMPP) during partial shading conditions (PSC) and mitigating Total Harmonic Distortion (THD) caused by fluctuating solar irradiance. These challenges impact the efficiency and performance of PV systems integrated into grid networks. Artificial Intelligence (AI) has proven effective in optimizing Maximum Power Point Tracking (MPPT), addressing both GMPP tracking and THD reduction. Interestingly, AI's adaptive strategies in PV systems parallel cognitive adaptation techniques in ReAttach Therapy, which fosters neuroplasticity and cognitive flexibility in individuals with developmental diversities. Both frameworks rely on timely, dynamic adjustments to optimize outcomes, whether in energy management or cognitive rehabilitation. This review examines AI-based MPPT algorithms and their role in THD reduction, drawing parallels with ReAttach Therapy’s adaptive processes. Just as adaptive filters (AF) reduce noise and disturbances in PV systems, ReAttach techniques help individuals adapt to sensory stimuli and improve emotional regulation. The paper highlights how AI-based solutions can inspire advancements in therapeutic approaches by integrating adaptive strategies to enhance performance in both fields. By providing a multidisciplinary perspective, this review offers new insights into selecting optimal AI-based MPPT methods while promoting adaptive strategies applicable in both technological and therapeutic contexts.

The escalating demands and increasing awareness for the environment, resulted in deployment of Photovoltaic (PV) system as a viable option. PV system are widely installed for numerous applications. However, the challenges in tracking the maximum power with intermittent atmospheric condition and varying load is significant. Maximum Power Point Tracking (MPPT) algorithms are employed and based on their convergence speed, control of external variations and oscillation, the output power efficiency, and other significant factors viz. the algorithm complexity and implementation cost, novel MPPT approach are preferable than the conventional approach. This paper presents an artificial intelligence-based optimization controller for MPPT in a PV system under varying load and irradiance conditions. Comparative analysis of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) based MPPT is simulated and analysed. The PV system consisting of PV array and boost converter with MPPT controller feeds the DC load. The power conversion and panel efficiency are the significant factors to determine the effectiveness of tracking maximum power point. The simulation results show the performance of these controllers on the PV panel output power and the load side output power under changing loads and irradiance. In addition, the comparison of PV panel efficiency of ANN and PSO based MPPT techniques w.r.t changing loads is carried out. Based on the above analysis, PSO based MPPT algorithm marginally outperforms the ANN based MPPT algorithm. Further, the implementation of hybrid MPPT (ANN &amp;PSO) for higher accuracy and tracking capability can be carried out as future work.

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.

Recently, real-time simulation of renewable energy sources are indispensible for evaluating the performance of the maximum power point tracking (MPPT) controller, especially in the photovoltaic (PV) system in order to reduce cost in the testing phase. Nowadays, real time PV simulators are obtained by using analog and/or digital components. In this paper, a real-time simulation of a PV system with a boost converter was proposed using only the digital signal processor (DSP) processor with two DC voltage sources to emulate the temperature and irradiation in the PV system. AMATLAB/ Simulink environment was used to develop the real-time PV system with a boost converter into a C-program and build it into a DSP controller TMS320F28335. Besides, the performance of the real-time DSP-based PV was tested in different temperature and irradiation conditions to observe the P-V and V-I characteristics. Further, the performance of the PV with a boost converter was tested at different temperatures and irradiations using MPPT algorithms. This scheme was tested through simulation and the results were validated with that of standard conditions given in the PV data sheets. Implementation of this project helped to attract more researchers to study renewable energy applications without real sources. This might facilitate the study of PV systems in a real-time scenario and the evaluation of what should be expected for PV modules available in the market.

A maximum power point tracking (MPPT) DC/DC converter for a photovoltaic (PV) system is an important component in a PV system. The converter is used to convert the value of DC voltage produced by PV panels to a value accepted by the inverter for on-grid, or battery for off-grid system. In addition, the MPPT is used to extract and maintain the solar PV to its maximum power in all conditions. However, the values of components used in literature of MPPT DC/DC converter for a specific PV module cannot be applied for different capacity. Although sets of equations are given in literature to determine the values of inductor and capacitor used in the circuit, the calculated values do not produce the desired output in most cases because of unclear explanation. The validation of input parameters of the converter is also not properly presented for different solar irradiance and temperature. The efficiency of the converter is still low. Hence, this paper presents step-by-step process on how to design a MPPT DC/DC boost converter in MATLAB/Simulink environment. The incremental conductance method is used as MPPT algorithm to control the duty cycle of the converter. Then, a thorough analysis is done by simulating the developed model under different solar irradiance and temperature. Simulation results are then compared with the theoretical values from I-V and P-V curves of the selected PV modules. The results show that the proposed MPPT DC/DC converter is able to extract maximum power from PV modules and the output power produced by the converter also indicates that the converter has a high efficiency at 99.7% in all selected values of solar irradiance and temperature.

Currently, Photovoltaic (PV) energy is the most important energy resource in electrical power applications around the world since it is inexhaustible, clean and pollution free. As the output power from the PV arrays changing with solar irradiance and temperature, so it is necessary to operate the photovoltaic (PV) system at the maximum power point (MPP) to increase the power extraction from the solar panel. For this effective operation and maximum power extraction from a PV system, Maximum power point tracking (MPPT) algorithm plays a vital role by operating the PV energy system near the MPP. In this work, amongst several existing well established MPPT algorithms, the open voltage (OV) based algorithm is chosen for simulation of a PV system using Matlab / Simscape. As maximum power point (MPP) can be attained with a certain amount of load from the PV power system, so load matching is an important concept of MPPT algorithms. Here, the performances of this open voltage (OV) based algorithm method is evaluated for different values of solar radiation and battery was used as load to see the charging performance for the algorithm.

This study introduces a novel methodology aimed at minimising total harmonic distortion (THD) in grid-connected photovoltaic (PV) systems (GCPVs) through the implementation of a maximum power point tracking (MPPT) approach based on artificial neural networks (ANN). High THD levels in PV systems can lead to inefficiencies, power quality issues, and potential damage to the grid infrastructure. Although traditional MPPT methods effectively optimise the power output, they often fail to address harmonics. The proposed ANN-based MPPT algorithm improves PV power harvesting while actively minimising the harmonic distortions. The ANN was trained using a comprehensive dataset that included various environmental conditions, ensuring robust performance in diverse operational scenarios. Simulation results demonstrate that the ANN-based MPPT approach significantly reduces THD to below 1% across various irradiance levels, in contrast to the 1.18% to 2.72% observed with conventional methods such as perturb and observe (PO), while simultaneously preserving optimal power output. Reducing harmonic distortion improves the power quality, system efficiency, and lifespan of grid-connected components. This study highlights ANN-based control strategies for addressing the challenge of maximising energy harvesting and maintaining power quality in modern PV systems, offering a solution for the sustainable integration of solar energy into the grid.

Harsh outdoor operating circumstances of photovoltaic (PV) array leads to non-uniform aging phenomenon among PV panels which in turn reduces the life span of the PV modules and the energy efficiency of the whole array. Under non-uniform aging conditions for series-parallel (SP) connected PV modules, the characteristics of the entire PV array are deformed resulting in multiple peaks where one of them is the global peak. The appearance of such multiple peaks can mislead traditional maximum power point tracking (MPPT) techniques to get trapped at local peaks, which leads to misleading power losses. This paper proposes a current collector optimizer (CCO) topology to extract maximum power from a non-uniform aged PV array. By using CCO topology, the PV array characteristics have a unique maximum power point (MPP) which easy to follow by a simple MPPT algorithm. Consequently, the proposed topology does not suffer from misleading power losses. Besides, the CCO topology requires offline rearrangement of aged modules to obtain the optimal maximum power. A comparative study is conducted to demonstrate the efficacy of the proposed topology. Ultimately, the paper presents an offline algorithm for rearranging erratic aged CCO modules to extract the optimum power from large-scale PV arrays.