Grid-connected Photovoltaic System Research Articles

Passivity-based Rieman Liouville fractional order sliding mode control of three phase inverter in a grid-connected photovoltaic system.

Photovoltaic (PV) system parameters are always non-linear due to variable environmental conditions. The Maximum power point tracking (MPPT) is difficult under multiple uncertainties, disruptions and the occurrence of time-varying stochastic conditions. Therefore, Passivity based Fractional order Sliding-Mode controller (PBSMC) is proposed to examine and develop a storage function in error tracking for PV power and direct voltage in this research work. A unique sliding surface for Fractional Order Sliding Mode Control (FOSMC) framework is proposed and its stability and finite time convergence is proved by implementing Lyapunov stability method. An additional input of sliding mode control (SMC) is also added to a passive system to boost the controller performance by removing the rapid uncertainties and disturbances. Therefore, PBSMC, along with globally consistent control efficiency under varying operating conditions is implemented with enhanced system damping and substantial robustness. The novelty of the proposed technique lies in a unique sliding surface for FOSMC framework based on Riemann Liouville (R-L) fractional calculus. Results have shown that the proposed control technique reduces the tracking error in PV output power, under variable irradiance conditions, by 81%, compared to fractional order proportional integral derivative (FOPID) controller. It is reduced by 39%, when compared to passivity based control (PBC) and 28%, when compared to passivity based FOPID (EPBFOPID). The proposed technique led to the least total harmonic distortion in the grid side voltage and current. The tracking time of PV output power is 0.025 seconds in PBSMC under varying solar irradiance, however FOPID, PBC, EPBFOPID, have failed to converge fully. Similarly the dc link voltage has tracked the reference voltage in 0.05 seconds however the rest of the methods either could not converge, or converged after significant amount of time. During solar irradiance and temperature change, the photovoltaic output power has converged in 0.018 seconds using PBSMC, however remaining methods failed to converge or track fully and the dc link voltage has minimum tracking error due to PBSMC as compared to the other methods. Furthermore, the photovoltaic output power converges to the reference power in 0.1 seconds in power grid voltage drop, whereas other methods failed to converge fully. In addition power is also injected from the PV inverter into the grid at unity power factor.

Comparative Power Quality Performance Evaluation of Grid-Connected Solar PV System

Comparative Power Quality Performance Evaluation of Grid-Connected Solar PV System

Comparison of Harmonics Mitigation Techniques for Grid-Connected PV System and Introduction of a Concept of Hybrid Filter

Comparison of Harmonics Mitigation Techniques for Grid-Connected PV System and Introduction of a Concept of Hybrid Filter

Leakage Current Analysis of Non-Isolated Photovoltaic Grid-Connected System

Amidst mounting concerns over energy scarcity and the environmental repercussions of fossil fuels, renewable sources, spearheaded by solar energy, have garnered substantial societal attention, thereby propelling advancements in photovoltaics (PVs). In the realm of grid-connected (GC) PV systems, a pressing issue is the leakage current suppression in non-isolated GC PV inverters. As a ubiquitous architecture in PV systems, the non-isolated GC configuration is of paramount importance in practical applications. Yet, intrinsic attributes of PV systems have amplified the leakage current challenge within this setup. This paper endeavors to dissect the leakage currents in non-isolated PV GC systems, delving into the salient factors influencing them. It embarks on an exploration of the origins of leakage currents, subsequently analyzing the repercussions of LC filter parameters—spanning filter capacitance, Y capacitance, and common-mode inductance—on these currents. Drawing insights from extant scholarly discourse on leakage current mitigation, this study offers a synthesized perspective accentuated with augmented strategies, elucidating a comprehensive path forward for leakage current suppression.

A Modified Perturb-and-Observe Control for Improved Maximum Power Point Tracking Performance on Grid-Connected Photovoltaic System

A Modified Perturb-and-Observe Control for Improved Maximum Power Point Tracking Performance on Grid-Connected Photovoltaic System

Monitoring results of the 30 kWp PV grid-connected power system installed at University POLITEHNICA of Bucharest, Romania

A 30 kWp PV grid-connected demonstration system was installed in summer of 2006 at University Politehnica of Bucharest, Romania, in the framework of the EU Project “PV enlargement" and of the Romanian project “PV grid". It is the largest PV system of the country. In this paper, the results of the main performance measurements during the first two years are presented.

ANN-Fuzzy Hybrid Control Strategy for MPPT of Grid-connected PV System with Battery Storage under Fast-charging Atmospheric Condition

Abstract: The use of solar energy through photovoltaic arrays is continually expanding and has recently been regarded as one of the cleanest sources of energy. Increasing the amount of electricity provided to the load is one method for lowering the cost of solar systems. Contrarily, altering the load creates a divergence from the maximum power point (MPP) and changes the operating point of the solar conversion system. Due to this, attention has been given to MPPT techniques that can be used with solar systems in numerous research investigations. In this paper, we implement an MPPT method based on an Artificial Neural Network (ANN). This method combined two controllers, ANN and fuzzy logic controller, under the name ANNFuzzy logic hybrid. In the first stage, ANN can determine Vmpp from irradiation and temperature, and both of them are variable. In the second stage, this Vmpp has been corrected by implementing Fuzzy logic in order to minimize the error of the voltage and VMPP. ANN-fuzzy hybrid has been simulated in Matlab-Simulink and was found to be the best solution to follow the maximum power point when irradiation and temperature are varied. The energy extracted from PV is delivered to a battery in order to inject this power into the smart grid by using an inverter controlled by PID. Finally, a LC filter has been used to eliminate the harmonics and compensate for the reactive power. For energy storage, we consider the utilization of lithiumion batteries, which are recognized as an optimal solution for storing energy efficiently. To manage the charge and discharge of the battery, we employ a PID controller and a buck-boost converter. Through this research, we aim to explore the performance and effectiveness of the hybrid ANN-fuzzy simulation for the boost converter system. By combining the capabilities of ANN and fuzzy logic, we expect to achieve improved control and optimization of the power conversion processes in the system. Additionally, the integration of lithium-ion batteries and the use of the PID controller and buck-boost converter allow for efficient management of energy storage and retrieval. Overall, this study investigates the hybrid ANN-fuzzy simulation for the boost converter system, highlighting the integration of various components and control techniques to enhance the performance and efficiency of the system.

Marine Predator Algorithm-Based Optimal PI Controllers for LVRT Capability Enhancement of Grid-Connected PV Systems.

Photovoltaic (PV) systems are becoming essential to our energy landscape as renewable energy sources become more widely integrated into power networks. Preserving grid stability, especially during voltage sags, is one of the significant difficulties confronting the implementation of these technologies. This attribute is referred to as low-voltage ride-through (LVRT). To overcome this issue, adopting a Proportional-Integral (PI) controller, a control system standard, is proving to be an efficient solution. This paper provides a unique algorithm-based approach of the Marine Predator Algorithm (MPA) for optimized tuning of the used PI controller, mainly focusing on inverter control, to improve the LVRT of the grid, leading to improvements in the overshoot, undershoot, settling time, and steady-state response of the system. The fitness function is optimized using the MPA to determine the settings of the PI controller. This process helps to optimally design the controllers optimally, thus improving the inverter control and performance and enhancing the system's LVRT capability. The methodology is tested in case of a 3L-G fault. To test its validity, the proposed approach is compared with rival standard optimization-based PI controllers, namely Grey Wolf Optimization and Particle Swarm Optimization. The comparison shows that the used algorithm provides better results with a higher convergence rate with overshoot ranging from 14% to 40% less in the case of DC-Link Voltage and active power and also settling times in the case of MPA being less than PSO and GWO by 0.76 to 0.95 s.

Control Approach of Grid-Connected PV Inverter under Unbalanced Grid Conditions

In grid-connected photovoltaic (PV) systems, power quality and voltage control are necessary, particularly under unbalanced grid conditions. These conditions frequently lead to double-line frequency power oscillations, which worsen Direct Current (DC)-link voltage ripples and stress DC-link capacitors. The well-known dq frame vector control technique, which is effective under normal conditions, struggles with oscillatory component management in unbalanced grid conditions. To address this issue, this paper presents an advanced control approach designed for grid-connected PV inverters. The proposed approach is effective at reducing oscillations in the DC-link voltage at double the grid frequency, thereby enhancing system stability and component longevity. This method introduces a feedback control method designed to regulate oscillatory components that appeared within the dq frame and suppress the DC-link voltage oscillations under imbalance conditions, including single line-to-ground (SLG) faults. Additionally, the control scheme incorporates a maximum power point tracking (MPPT) controller to optimize PV efficiency. Comprehensive simulations demonstrate the effectiveness of this method in maintaining sinusoidal current injections and stabilizing DC-link voltage during unbalanced grid conditions. Simulation results show that the control scheme effectively stabilizes DC-link voltage, maintains balanced grid current, and ensures constant active power under various conditions, including SLG faults and solar irradiance changes.

Techno-economic and environmental analysis of a grid-connected rooftop solar photovoltaic system in three climate zones

Abstract This study aims to fill a gap in research on technical-economic and environmental assessments of grid-connected photovoltaic (PV) panels for residential electricity supply. To combat this, a study examines the feasibility of grid-connected rooftop solar PV systems in three cities. Using PVsyst software, technical, economic, and environmental factors were analyzed, including energy injected into the grid, net present value (NPV), internal rate of return (IRR), levelized cost of energy (LCOE), and life cycle emissions. It is concluded from this study that PV power plants are technically, economically, and environmentally feasible for all three climate zones, but cold zone with the highest annual production of 10.66 MWh, the highest NPV ($5449.47), the highest IRR (14.28%), and the lowest LCOE ($0.063/kWh) is the most appropriate place to set up a PV power plant (Scenario 1: No tracking system). Furthermore, using horizontal (Scenario 2), vertical (Scenario 3), and two-axis tracking (Scenario 4) systems instead of the no-tracking system (Scenario 1) increases the amount of electricity injected into the grid by 7.56%, 24.67%, and 36.35%, respectively. According to life cycle emission, the annual production of 10.66 MWh by the 5 kW power plant installed in cold zone will prevent 102.02 tons of carbon dioxide emissions into the atmosphere (Scenario 1: No tracking system). To remove financial obstacles and increase the viability of renewable energy, the government must provide financial incentives, lower the cost of equipment, and enact strong renewable energy legislation.

Theoretic and experimental performance of a grid-connected photovoltaic system: Multiple prediction model of efficiency and annual energy generation

In this article, the performance of a 3.36 kWp grid-connected photovoltaic system (GCPVS) under warm and subhumid weather conditions and the development of a predictive mathematical model is presented. Climate data of the 2021 year were used to evaluate energy generation, different types of performance, and efficiency. The average annual yield, corrected yield, array, and final yields were 6.45 h/day, 6.18 h/day, 5.16 h/day, and 4.97 h/day, respectively. The overall annual mean capacity factor and efficiency ratios were 20.73% and 77.22%, correspondingly. Experimental data were analyzed and correlated by multivariate linear regression (MLR) prediction and simulation to validate models. The MLR analysis showed that the efficiency is highly dependent on the temperature of the PV modules and that climatic parameters significantly affect the efficiency and output electric power. The prediction models for PV module efficiency, system efficiency, and direct current energy exhibit an uncertainty of ±1.04%, ±0.57%, and ±35.38 kWh, one-to-one. The monthly generation was compared with results obtained by Energy3D simulation-free software, showing an absolute error of ±2.33 kWh. This information can be used as a methodological tool for predicting efficiency and power generation in direct current.

Techno-Economic Analysis of Residential Grid-Connected Rooftop Solar PV Systems in Indonesia Under MEMR 26/2021 Regulation

Grid-connected commercial rooftop solar PV systems have been widely used worldwide to provide affordable, clean energy and long-term energy security solutions. This is especially true in cities where space is limited and rooftops are not being used. According to the Ministry of Energy and Mineral Resources (MEMR) No 26/2021 regulation in Indonesia, the rooftop PV system is allowed to be integrated with electricity grid with an export-import metering. The owner of the rooftop PV system will not only reduce their electricity bill, but also offset the energy over a period of 10 years. This paper provides a design and sizing analysis of a small scale rooftop solar PV system for residential. This study uses the PVsyst and SolarGIS tools to estimate the amount of energy produced by a 3 kWp solar system. It also provides a financial evaluation of the system's profitability, with particular attention to commercial buildings that are covered under MEMR regulation. This detailed analysis can be used by asset owners to decide the best way forward in terms of sustainable energy production and cost savings, as well as addressing the energy security problem.

Comprehensive Quantitative Evaluation of the Photovoltaic Grid-Connected Power Generation System Adopting nX DC/DC Converter

Comprehensive Quantitative Evaluation of the Photovoltaic Grid-Connected Power Generation System Adopting nX DC/DC Converter

Design and Performance Analysis of Grid-Connected Photovoltaic Systems in Kalar City, Kurdistan, Iraq: A Case Study

Design and Performance Analysis of Grid-Connected Photovoltaic Systems in Kalar City, Kurdistan, Iraq: A Case Study

Honey Badger Algorithm Based PI Controller for DC Link Voltage Control of Solar Photovoltaic System Connected to Grid for Enhanced Power Quality

The parameters of the proportional integral (PI) controller used for DC link voltage control play a vital role in regulating the DC link voltage of grid-connected solar photovoltaic system that experiences heavy DC voltage fluctuation and various power quality issues due to integration of non-linear loads, varying irradiance and loading conditions. The existing strategies might fail to respond to the above complications due to fixed PI gains which restrict their scope of controlling domain. Therefore, adaptive PI gain values should be selected to quickly adjust to the varying conditions and dynamically obtain the optimal control settings. This article employs the honey badger algorithm (HBA), developed in Matlab to adaptively tune the PI controller gains value by minimizing the DC link voltage deviation. The advantages of using this technique over conventional PI controllers are its quick response, adaptability, stability, and optimal self-tuning process. The proposed algorithm has been tested under steady-state and dynamic conditions for different irradiation and loading conditions. Further, results of the proposed algorithm have been compared with PI controller and particle swarm optimization-PI controller to validate the efficacy of the proposed algorithm for grid-connected solar photovoltaic systems. It has been observed that the proposed algorithm maintains THD <3% in all the tested conditions.

Simulation system of intelligent photovoltaic grid-connected inverter considering fuzzy PI control

The grid connected inverter is the core component of the photovoltaic grid connected power generation system, which mainly converts the direct current of the photovoltaic matrix into alternating current that meets the grid connected requirements, playing a key role in the efficient and stable operation of the photovoltaic grid connected power generation system.This paper uses fuzzy PI control model which to improve the performance of intelligent photovoltaic grid-connected inverter to simulate the intelligent photovoltaic inverter system, using mathematical analysis and reasoning methods for model analysis,adopts two-stage three-phase LCL grid-connected inverter, establishes mathematical models in two-phase synchronous rotating and two-phase static coordinate systems, and adopts an active damping strategy based on grid-connected current. Based on existing research and empirical analysis,aiming at the disadvantage of poor dynamic response of repetitive control, an improved repetitive control strategy is adopted, and the controller is analyzed from two aspects of stability and dynamic performance, and the simulation model of photovoltaic grid-connected power generation system is built. Use experimental analysis method to verify the effectiveness of the model in this article,The experimental results show that the simulation system of intelligent photovoltaic grid-connected inverter considering fuzzy PI control proposed in this paper has certain effects.

Estimation and monitoring of grid connected PV system generation

This paper presents a new method to identify the operating performance parameters of a photovoltaic (PV) generator and a grid connected inverter. This method has been applied to the behavior modeling and simulation of the grid connected PV system at the Centre de Developpement des Energies Renouvelables (CDER) in Algeria. The PV module (PVM) parameters has been identified in static with outdoor measurements of I-V curves in order to model the PV array generation. Using the Linear Reoriented Coordinates Method (LRCM), the prediction of maximum power point (MPP) of the PV array has been performed without using complicate approximations or Taylor series. In order to estimate the ac output power, a PV inverter performance model based on empirical relationship was used. However, an accurate identification of the inverter performance parameters is performed using an optimization algorithm, named Levenberg-Marquardt (LM) algorithm, and a moutput current and voltage. The simulation results were achieved through MATLAB/Simulink environment, for the setting and the testing of the models. The results show a good agreement between the measured and estimated results based on the identified parameters. The integration of the proposed method and the presented model present a simple way to estimate the expected system performance with a high degree of accuracy. This solution allows a development of automatic supervision, energy generation prediction and fault detection procedure.

Design, Installation, and Performance Monitoring of a 105 kWp Rooftop Solar PV System

Rooftop Solar PV systems are gaining growing attention on the path toward carbon neutrality. Hence, a great effort must be exerted to ensure smooth and efficient penetration of such systems into the existing utility grids. In the current work, a 105 kWp rooftop solar system is designed and installed to fulfil part of the electric demand of the faculty of Engineering, Zagazig University, Egypt. The performance parameters of this grid-connected solar PV system are optimized for the least-cost of electricity supply through using industry-standard software NREL SAM. For the current rooftop system, fixed orientation of an optimized tilt angle of 23 degrees is preferred over sun-tracking systems due to the reduced operating and maintenance costs. The annual energy production of the system is 175,700 kWh with a capacity factor of 19.1%. Through using parametric optimization and inter-connected financial analysis algorithms, it was possible to define the least cost for the electricity supplied to the grid to be 0.55 EGP/kWh, which achieves grid parity in Egypt. The project is finically profitable without any subsidy, with a discounted payback period of 7.9 years, and IRR (on equity) of 20.1 % and saves 1882-ton CO2 over its lifetime. Sensitivity analysis showed that the electricity escalation rate greatly enhances the project finances. In the first year of operation, the system performed fairly well compared with the theoretical design, provided that the system is frequently cleaned (at least once a week) and continuously connected.

Robust Capacity Planning of a Grid-Connected Photovoltaic System Under Climate Change and Economic Instabilities

Robust Capacity Planning of a Grid-Connected Photovoltaic System Under Climate Change and Economic Instabilities

Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time.