Modeling Of Photovoltaic System Research Articles

Design of a two-stage photovoltaic grid-connected system with dual closed-loop control

Abstract This paper designs a two-stage photovoltaic grid-connected system with dual closed-loop control, cascading the topological structures of photovoltaic cells, boost chopper circuits, and inverter circuits. The front-stage boost chopper circuit control utilizes the incremental conductance method to achieve maximum power point tracking control. The rear-stage inverter circuit employs a dual closed-loop control system with an outer voltage loop and an inner current loop, incorporating a proportional-integral controller, to ensure stable DC-side voltage and unity power factor for grid connection. A simulation model of the two-stage photovoltaic grid-connected system designed in this paper was constructed and analyzed through simulation runs. The firmness of the DC-side voltage and the achievement of unity power factor during grid connection confirmed the operability and resultiveness of the cascaded topology and the control method.

Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.

Research on Hybrid Logic Dynamic Model and Voltage Predictive Control of Photovoltaic Storage System

This paper investigates microgrid systems characterized by the coexistence of discrete events and continuous events, a typical hybrid system. By selecting the charging and discharging processes of the energy storage unit as logical variables, a mixed logical dynamic (MLD) model for the microgrid in islanded mode is established. Based on this model, model predictive control (MPC) theory is employed to optimize the energy management strategy, aiming to stabilize the DC bus voltage of the photovoltaic (PV) unit and minimize the switching frequency of the energy storage unit’s charging and discharging processes during system operation.

Supervised classification and fault detection in grid-connected PV systems using 1D-CNN: Simulation and real-time validation

Photovoltaic (PV) systems are prone to various faults, including short-circuit, open-circuit, partial shading, and inverter bypass diode issues, which reduce power output and can damage components. This study presents an innovative fault detection and online monitoring technique for grid-connected PV (GCPV) systems, combining Internet of Things (IoT) technology with a one-dimensional convolutional neural network (1D-CNN) deep learning approach. The method involves developing a temperature-dependent PV system model using series resistance and ideality factor, capturing real-time data from a 15kWp GCPV system with optimally placed sensors to minimize sensor count while maintaining data accuracy, and validating the model through MATLAB/Simulink simulations and real-time experiments under various fault scenarios. The collected data is used to train the 1D-CNN model to classify different fault types. The model is then implemented on an IoT platform for real-time monitoring and fault detection, displaying system status and alerts via a dashboard. The proposed system achieves a high fault detection accuracy of 98.15 % and 93.12 % during cyberattacks, with an uncertainty of ±4 %, significantly enhancing fault detection reliability and efficiency compared to existing methods. The IoT dashboard provides an effective tool for monitoring system performance and issuing alerts under abnormal conditions.

Improved Quality Parameter Estimation of Photovoltaic System Models based on SAO Algorithm

Improved Quality Parameter Estimation of Photovoltaic System Models based on SAO Algorithm

Enhanced Whale optimization algorithms for parameter identification of solar photovoltaic cell models: a comparative study

Parameter identification of solar photovoltaic (PV) cells is crucial for the PV system modeling. However, finding optimal parameters of PV models is an intractable problem due to the highly nonlinear characteristics between currents and voltages in different environments. To address this problem, whale optimization algorithm (WOA)-based meta-heuristic algorithm has turned out to be a feasible and effective approach. As a highly promising optimization algorithm, different enhanced WOA variants have been proposed. Nevertheless, there has been no comparative study of WOA and its variants for parameter identification of PV models so far. To further investigate and analyze the performance of WOA in the studied problem, this work applied and compared WOA and ten enhanced WOA variants for identifying five PV model parameters. Different evaluation indices including solution accuracy, search robustness, and convergence curve were employed to reveal their performance variation. Based on the simulation results, a multi-model statistical analysis with the Friedman test at a confidence level 0.05 was conducted to rank all algorithms. EWOA that hybridizes the sorting-based differential mutation operator and the Lévy flight strategy ranked first and its performance was further verified. Besides, according to the simulation results, possible effective improvement directions for WOA in tackling this intractable problem are concluded to guide future work.

Coordinated control strategy of photovoltaic energy storage power station based on adaptive dynamic programming

In order to solve the problem of variable steady-state operation nodes and poor coordination control effect in photovoltaic energy storage plants, the coordination control strategy of photovoltaic energy storage plants based on ADP is studied. Establish the photovoltaic energy storage power station model including photovoltaic system model, super capacitor system model and battery system model; Set the maximum limit of active power change as the power constraint condition for coordinated control of photovoltaic energy storage station; The optimal control problem of multi voltage and reactive power resource coordination is fully considered, the optimal voltage control model is established by using ADP algorithm, and the optimal coordinated control strategy is obtained by online learning the collected dynamic operation information. The experimental results show that this strategy can improve the coordinated control effect of the photovoltaic energy storage station, ensure the photovoltaic energy storage station in a stable operation state, improve the service life of the energy storage device in the photovoltaic energy storage station, and stabilize the steady-state operating node voltage within the ideal fluctuation range.

Marine predators algorithm with deep learning based solar photovoltaic system modelling and optimization of green hydrogen production

Green hydrogen production depends on solar energy contains employing renewable solar power to purpose the electrolysis of water, causing the separation of hydrogen and oxygen molecules. This method contains a high potential to address the energy transition challenges caused by weather changes and vital carbon-neutral alternatives. Photovoltaic (PV) based combined energy systems performance as a potential new technological solution for clean and affordable green hydrogen production. Optimizing solar PV systems for the effectual generation of green hydrogen includes maximizing the energy output of solar panels and increasing the entire hydrogen production method. Several researchers and scientists are paid attention to the optimizer and modelling of many blocks developing the PV electrolysis method to acquire the optimum solution. With this motivation, the study presents a new Marine Predator Algorithm with Deep Learning-based Modelling and Optimization of the Green Hydrogen Production (MPADL-MOGHP) technique. The MPADL-MOGHP technique can be employed for determining the optimum operational variables of the water electrolysis procedure relevant to hydrogen (H2) production. Catalyst amount (μg), electrolysis time (min), and electric voltage (V) are the three controlling factors that should be properly detected to increase hydrogen production. The MPADL-MOGHP technique comprises two major stages of operations such as modelling and optimization. Primarily, the DBN model is applied for simulating the water electrolysis procedure designed based on electric voltage, quantity of catalyst, and electrolysis time. Next, the MPA is applied for determining the optimum parameters of the water electrolysis process to maximize the generation rate of the hydrogen. The quantity of catalyst, the electrolysis time, and the electric voltage are applied as decision parameters during the optimization process. The performances of the MPADL-MOGHP system are tested on different aspects. The experimental values highlighted the promising results of the MPADL-MOGHP method over other existing techniques.

Time-domain equivalent model for harmonic simulations of wind and photovoltaic plants

Equivalent models of wind power plants or photovoltaic systems reduce the computational burden relative to detailed models (DMs) during power quality studies. However, existing time-domain equivalent models are ineffective for predicting harmonic currents because they do not consider switching behavior from power converters. This paper proposes a novel time-domain equivalent model, the aggregated harmonic model (AHM), to perform harmonic current prediction in large plants. The AHM uses the National Renewable Energy Laboratory method to equalize the collector system and applies the superposition principle to include the dead time and switching effects. Simulations were executed using the DM and AHM of actual plants. Field measurements were performed to validate the AHM. The results show that the proposed model produced well-matched curves for current waveforms, frequency responses, and harmonic spectra. Consequently, this model is an accurate tool for transmission system operators.

Investigating the power and efficiency of the 3D model of the concentrated photovoltaic thermoelectric hybrid system and estimating the power consumption of the water pump for cooling

Solar energy is one of the most useful types of renewable energy. Solar cells are being used to convert solar energy into electricity. By focusing the radiation on the cells, more energy is radiated in a smaller area of the cell which is called concentrated photovoltaic (CPV). Due to the concentration of radiation, the temperature of the cell rises which can be converted into electricity through thermoelectric generators (TEG). The present study is a hybrid system of solar cell, thermoelectric generator, and heat exchanger (CPV-TEG). By this survey being created, it was revealed that the combined power produced by the CPV-TEG reaches its maximum value at concentration ratio of 40. This increase continues with the increase in fluid flow rate. however, according to the power consumption of the pump, it was observed that in all types of heat exchangers, an increase in flow rate of more than 8 liters per hour reduces the output power of the system. The replacement of the secondary heat exchanger with the primary one illustrated that the increase of the channels in the exchanger, despite the increase in the power consumption of the pump, causes a very small increase in the output power and efficiency of the system. Moreover, replacing the tertiary heat exchanger instead of the secondary exchanger showed that changing the direction of fluid output increases the power consumption of the pump in high flow rates.

MPPT constraint conditions based on four-parameter cell model for some usual photovoltaic systems

Hitherto there is no work to specially analyze in detail the question when the maximum power point (MPP) of the photovoltaic (PV) system exists and when it disappears. It has caused the difficulty in the hardware and software designs of the PV system when the maximum power point tracking (MPPT) control must be implemented all the time. To solve this question, in this paper, the MPPT constraint conditions (MPPTCC) of the 12 usual PV systems are proposed under ideal conditions and expressed by the model parameters of the four-parameter cell model in the engineering application. They are the necessary and sufficient conditions to guarantee the existence of the MPPs of these 12 PV systems. Meanwhile, they also disclose the inherent relationships between load (or bus voltage) and cell parameters when the MPP of the PV system always exists. In addition, to apply them in practice, their expressions in practical application are also presented. Finally, some simulation experiments verify the reasonableness and accuracy of these MPPTCC in practical application, and test the influence of the varying weather to the MPPT range. By this work, the possible failure of the MPPT control arising from the occasional disappearance of the MPP of the PV system can be completely avoided. Therefore, the findings in this work can be used as the theory foundation for the hardware design, theoretical research and product installation of the PV system.

Enhancing interpretability in data-driven modeling of photovoltaic inverter systems through digital twin approach

The utilization of data-driven modeling techniques has been extensively employed in the simulation analysis, power prediction, and condition monitoring of photovoltaic power generation systems. However, the absence of interpretability regarding the intrinsic mechanisms in the modeling process has resulted in numerous constraints in practical implementation and subsequent promotion. To this end, we propose a novel digital twin modeling approach that eliminates the need for injecting additional signals or sensors, estimating unknown parameters in the mechanism model solely by using operational data from physical systems. A time synchronization filter was added to address the frequency mismatch between the actual sampling frequency and the solution step size. The results of numerical research indicate that the proposed digital twin model has the ability to accurately simulate the dynamic characteristics of photovoltaic grid connected inverters. The digital twin model of photovoltaic inverters has achieved good results in the cross experiment of device degradation trend monitoring, indicating that the proposed method is expected to make significant contributions to the simulation, power prediction, and degradation monitoring of grid connected photovoltaic systems.

Photovoltaic Model Parameters Estimation Via the Fully Informed Search Algorithm

Effective parameter estimation for photovoltaic (PV) systems holds significant importance for both researchers and industry professionals. An accurate understanding of PV models, achieved through modeling and simulation, plays a pivotal role in optimizing the design, control, testing, and forecasting of PV system performance. Developing a precise and robust parameter identification method significantly contributes to enhancing the modeling, control, and optimization of photovoltaic systems. In this context, our research contribution introduces a novel version of Rao metaheuristic algorithm named the Fully Informed Search Algorithm (FISA). Which demonstrate acceptable performance to solving optimization problems in several applied fields. While, maintaining the simplicity and non-parametric nature of the original algorithm. The proposed algorithm holds promise for various industrial applications, particularly in optimizing complex systems such as photovoltaic (PV) systems. For which, we used it to efficiently identifying the parameters of the single-diode model (SDM). Thus, we demonstrate its effectiveness through the application in two distinct case studies within our simulation research. in the end, we compared the results of FISA algorithm to seven other well-known algorithms, the obtained results indicate the superiority of the proposed algorithm in term of the stability of the system, a faster convergence and higher accuracy.

A novel economic and technical dispatch model for household photovoltaic system considering energy storage system in “Duhok” City/Iraq as a case study

Photovoltaic (PV) systems harnessing solar power to generate electricity have gained widespread adoption worldwide due to clean innovations. The geographic position of the Kurdistan region, north of Iraq, is very favourable for the photovoltaic power generation system. In Duhok City, the average daily hours of sunshine per year are 8.5 h; this is a good point, which makes installing photovoltaic systems more interesting because the performance of PV systems depends mainly on solar radiation. This study analyses the design of a PV system that supplies electricity to households in Duhok City during the period when there is no general electricity considering the economic and technical dispatch aspects. The system is to be designed to power the household for 10 h (Grid-connected PV system) since it is assumed that the general electricity supply only lasts for 10 h per day. The system will be an alternative source of electricity to the local generators. The full design of the proposed system is implemented, so that the data on the electrical load and site radiations of a specific household at the site under consideration are collected during the design stages. Further, the technician's and economic evaluations are considered as well during each step of analysis and selection for system components. The cost estimate was based on the following data: the electrical load of the house is 6239.1 Wh/day, the electricity cost is $0.005 per Wh and the total investment amount is $3475. The ultimate outcomes, when juxtaposed with the local expenses of diesel generators and PV solar systems, indicate that within 4.5 years, PV solar systems will recoup their initial costs, providing a clean and noise-free energy source thereafter.

Global MPPT controllers for enhancing dynamic performance of photovoltaic systems under partial shading condition

This research focuses on the development and evaluation of global maximum power point tracking MPPT controllers for enhancing the dynamic performance of Photovoltaic (PV) systems under partial shading conditions. Three different controllers, namely Proportional-Integral (PI), Fractional-Order PI (FOPI), and Fuzzy Logic Controllers (FLC), are studied in conjunction with a modified Incremental Conductance (IC) and Fractional Open-Circuit Voltage (FOCV) algorithms. The proposed controllers with a novel optimization algorithm called Atom Search Optimization Algorithm (ASOA) are compared with classical control. The objective is to investigate the effectiveness of the global MPPT controllers in achieving higher tracking accuracy, faster convergence, and improved stability under varying shading conditions. The study involves extensive simulations using a representative PV system model subjected to different shading scenarios. The results demonstrate that the proposed global MPPT controllers with the ASOA algorithm, when integrated with the modified MPPT algorithms, outperform the classical control techniques in terms of dynamic performance and response to partial shading conditions. The suggested ASOA-based fuzzy IC MPPT controllers can reach the MPP more quickly with a shorter settling time and a better tracking response. Furthermore, with a reduced settling time of about 1.9 % and an average power harvesting efficiency of over 97.9 %, ASOA-based fuzzy IC MPPT controllers offer the best harvesting characteristics. The controller exhibits superior tracking accuracy and stability, mitigating the effects of shading-induced multiple peaks in the P-V curve.

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

A maximum power point tracking (MPPT) control procedure constructed based on the Artificial intelligence optimization algorithm is proposed. A mathematical model of photovoltaic cells was established under varying light intensity and ambient temperature. Maximal power tracking control for iterative search using an artificial intelligence (AI) optimization technique Excellent, the artificial intelligence-based MPPT algorithm's principle is presented. Simulation results it shows that the artificial intelligence algorithm can faster and exactly track the maximum power point and remains stable, and compared to Perturb and Observe algorithm under dynamic shadow conditions and artificial intelligence MPPT control method, which has higher tracking accuracy and faster convergence speed. Faster and smaller oscillation amplitude, which is the best response to sunlight conditions for photovoltaic maximum power point tracking technology Flexibility. Using Matlab/Simulink software to build a simulation model of an independent photovoltaic system. It controls variables by keeping the temperature continuous and changes the light intensity to simulate different lighting environments. The identical comparison was conducted between all based MPPT methods such as the fuzzy control approach, demonstrating that the fuzzy control based technique exhibits higher results in terms of efficiency.

Hybrid Solar PV and WECS Power Quality Improvement by STATCOM

The increasing integration of off-grid energy sources in the utility load has ended up resulting in elevated standards regarding power quality, voltage stabilisation purposes, and efficient energy use. The electrical network Wind and solar energy have been considered to be among the most reliable renewable energy sources. However, the self-sufficient operation of either photovoltaic or wind energy systems does not offer a highly consistent source of electricity production owing to the unpredictability of the wind and solar irradiance availability. As a result of this, a variety of solar and wind power generation systems have the ability to produce a very reliable and promising electrical supply. In the present study, a hybrid wind and photovoltaic panels system model has been provided. This specific type of technology possesses plenty of possibilities for its users from afar. This particular kind of technology is highly beneficial in inaccessible or offshore locations where integrating with the grid is not very cost-effective. Nevertheless, integrating power electronics to dissipated generation (DG) systems brings substantial issues with power quality, which include reactive power adjustment and harmonic development, which throws off the system for power distribution. The present study proposes a simulation framework for a hybrid wind-photovoltaic generation system. The system's efficiency in gridconnected mode is assessed. Calculations of total harmonic distortion (THD) at various speeds of wind were used for assessing the wind-SPV hybrid system's power quality. This hybrid system's power quality has been enhanced owing to its employing of STATCOM.

Novel approach to remote rural heating: Direct coupled photovoltaic electric heater underfloor heating system with phase change materials

Limited access to grids and the unavailability of traditional energy sources present significant challenges to effective heating in remote rural. This paper proposes a direct coupled photovoltaic electric heater underfloor heating system with phase change materials (direct coupled PVEH-UFHs), which eliminates the reliance on the power grid or conventional energy sources for heating in remote areas. The model of the direct coupled photovoltaic electric heating system (direct coupled PVEHs) and the building model with PCM floor were created and the accuracy of the direct coupled PVEHs model was verified experimentally. The study proposes using the total heating achievement rate (THAR) as the evaluation index and identifies the critical parameters affecting the system performance through sensitivity analysis. The results indicate that the photovoltaic capacity factor (PVCF), PCMs thickness and melting temperature are the key parameters affecting the heating performance of the system. The recommended 1.4 PVCF and PCM melting temperature of 294 K. The minimum payback time for system configuration that achieves 90 % THAR is 16.2 years. In summary, this paper proposes a design method and evaluation criteria for direct coupled PVEH-UFHs suitable for energy-efficient heating in remote areas.

Enhanced FOPID Controller Based Cascaded Inverter for Grid-Connected Photovoltaic System

An enhanced controller is proposed to investigate grid-connected photovoltaic systems employing cascaded two-level inverters. The primary focus is on optimizing power output, achieved through the development, modeling, and testing of photovoltaic systems using the suggested upgraded controller. This advanced controller, specifically a Fractional Order PID (FOPID) controller, incorporates the Ant-Lion Optimizer (ALO) algorithm for enhanced performance compared to conventional controllers. The FOPID controller’s reliability is emphasized, and further improvements are pursued by optimizing gain parameters through the prescribed method. Power supply under both schemes is examined, and the controller’s performance in extracting maximum power under specified operating conditions is deemed satisfactory. To validate the proposed approach, practical implementation is conducted using the MATLAB/Simulink platform. The effectiveness of proposed and conventional methodologies for analyzing the id and iq currents is assessed. A comparison is drawn between the suggested approach and current methods such as the base controller, GSA, and ABC methodologies. In comparison to the existing methods, the suggested FOPID controller demonstrates superior performance in maintaining the DC link voltage, achieving an efficiency of 94%, while the GSA method reaches 91.5%, the ABC method achieves 91%, and the base controller achieves 90%.

Novel Multi-Port Converter for Distributed MPPT Operation in Solar PV System

Solar photovoltaic (PV) systems continue to be the most prevalent renewable energy resource despite the presence of numerous limitations. A power discrepancy between PV modules on a large scale may result in power dissipation throughout the entire PV system. This particular paper proposes an efficient multi-port converter for distributed maximum power point tracking operation (D-MPPT) for a solar PV system. The operation details of the proposed multi-port converter along with analytical waveforms are presented in this paper. To implement the D-MPPT approach in the proposed multi-port converter, a detailed analysis of mathematical modeling of solar PV systems with a mismatch of PV power and voltage stabilization approach is done. In addition, the proposed approach eliminates the need for additional current sensors and semiconductor components to overcome the effect of mismatched power in the PV system. To validate this, the prototype has been built and integrated with the real environment of the solar PV system. To verify the operation, a detailed simulation study and experimental investigation have been carried out and presented in this paper. After a detailed investigation and discussion of measured results and analysis, it is concluded that the proposed multi-port DC-DC converter is the most suitable solution for solar PV applications.