Grid-connected Photovoltaic System Research Articles

Enhancing Grid Stability and Efficiency: Cost-Effective Hardware Implementation for Advanced Control of Grid-Connected PV Systems

Enhancing Grid Stability and Efficiency: Cost-Effective Hardware Implementation for Advanced Control of Grid-Connected PV Systems

Experimental Validation of PSO and GWO-Based MPPT for a Single-Stage Three-Phase Grid-Connected PV System Under Partial Shading

Experimental Validation of PSO and GWO-Based MPPT for a Single-Stage Three-Phase Grid-Connected PV System Under Partial Shading

Hybrid control for capacitor-assisted Z-source inverter in grid-connected photovoltaic system

Renewable energy integration into the power grid is essential for sustainable energy systems, but maintaining efficiency and reliability in such systems remains a key challenge. This study introduces a novel hybrid control topology for a Capacitor-Assisted Extended Boost Z-Source Multilevel Inverter in a grid associated solar photovoltaic (PV) structure. The suggested hybrid technique implies a united implementation of a Gannet Optimization Algorithm and Spiking Deep Residual Network and usually referred as GOA-SDRN technique. Initially, the modelling of the inverter is collected to get the best signal by the proposed controller. The proposed inverter configuration consists of a minimal quantity of diodes, switches, and sources. This configuration also offers advantages like less total harmonic distortion (THD) and reduced electromagnetic interference, making it a favourable choice for PV integration. The GOA is employed to determine the most favourable gain limiting factor based on a variation of power from their regular values. This control method ideally satisfies the load demand while reducing changes in the system limiting factor and external disturbances. The proposed control topology is executed in MATLAB and the concept is contrasted to different techniques. The THD values of existing methods such as Particle Swarm Optimization, Genetic Algorithm, and Grasshopper Optimization Algorithm are 1.36 %,0.89 %, and 1.99 % respectively, while the THD value of the proposedmethod is 0.63 %, demonstrating its optimal performance over existing methods. The proposed GOA-SDRN technique provides an effective solution for enhancing inverter efficiency and reducing distortion in grid-associated PV systems.

Implementing of a grid-connected PV energy system in building with medium consumption: A techno- economic case study

Renewable energy sources facilitate decentralized electricity production, reducing dependence on fossil fuels and contributing to a sustainable energy era. Algeria is one of the countries implementing a policy aimed at energy transition, with buildings playing a major role in this process. They consume more energy than any other sector and therefore significantly contribute to climate change. While various projects related to energy efficiency are being carried out, they are mostly limited to rural and Saharan areas or low-consumption residential buildings. The objective of this paper is to demonstrate the feasibility of integrating a grid-connected photovoltaic system into a medium-consumption building located in one of Algeria’s major cities, Constantine, to meet the building’s electrical energy demand. This paper presents a methodology for optimally sizing a grid-connected photovoltaic system to supply power to the Institute of Nutrition, Food, and Agro-Food Technologies (INATAA) in Constantine, Algeria. The technical and economic aspects of the system’s components were considered to determine the most feasible configuration, using real data from the institute and the HOMER Pro simulation software. The simulation results show that the grid-connected PV system, without storage, can meet the institute’s energy demand (with 671,061 kWh/year of energy produced, of which 81.3 % is photovoltaic energy). This configuration offers the lowest costs, with a Net Present Cost (NPC) of $748,413 and a Cost of Energy (COE) of $0.0894/kWh. Therefore, integration of a grid-connected photovoltaic system in medium-consumption buildings can effectively meet energy needs while reducing costs. Thus, this study demonstrates the technical and economic feasibility of such system to support the energy transition in urban areas in Algeria.

Performance assessment of a 30.26 kW grid-connected photovoltaic plant in Egypt

Abstract This paper presents an experimental analysis and performance evaluation of a grid-connected photovoltaic plant installed on the rooftop of the Electronics Research Institute in Cairo, Egypt. Cairo is classified as a hot-desert climate region according to the standard Koppen-Geiger climate classification system. Over a year, we monitored real-time data to assess key system performance metrics, such as energy yield, efficiencies, performance ratio, capacity factor, and losses. Based on the obtained experimental results, the highest final yield of 5.2498 hr/day was observed in the summer, whereas the lowest yield of 3.439 hr/day occurred in the winter months. The photovoltaic plant had an average annual system efficiency of 15.8%, while the photovoltaic and inverter had mean yearly efficiencies of 17.1% and 97.2%, respectively. The average annual performance ratio is 83.03%, and the capacity factor is 18.72%. The monthly total loss exhibited a linear rise alongside increasing ambient temperature and solar irradiance. The ambient temperature affected the system efficiency, photovoltaic efficiency, and performance ratio. The findings can help strengthen forecasts of future large-scale photovoltaic plants in hot desert climates. Moreover, they can guide the design, optimization, operation, and maintenance of new grid-connected photovoltaic systems.

Enhanced modelling and control strategy for grid-connected PV system utilizing high-gain Quasi-Z source converter and optimized ANN-MPPT algorithm

Enhanced modelling and control strategy for grid-connected PV system utilizing high-gain Quasi-Z source converter and optimized ANN-MPPT algorithm

Extreme learning machine based on BDE feature selection to detect fault scenarios in grid-connected PV systems under MPPT mode

This paper considers real-time data-driven adaptive fault detection (FD) in grid-connected PV (GPV) systems under maximum power point tracking (MPPT) modes during large variations. Faults under MPPT modes remain undetected for longer periods, introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual Micro Phasor Measurement Unit (Micro-PMU) estimations. The high-dimensional and high-frequency multivariate features vary over time, and computational efficiency becomes crucial to realizing online adaptive FD. The goal of this study is to present an artificial intelligence (AI) technique for detecting seven faults: inverter fault, feedback sensor fault, grid anomaly, nonhomogeneous partial shading, open circuit in PV array, MPPT controller fault, and boost converter controller fault. In this work, it was found that the application of Extreme Learning Machine (ELM) plays an important role in fault detection and localization. Nine (9) statistical features and eight (8) wavelet packet parameters are extracted from the data based on multiple default values. These features were used as an input vector to train and test the ELM and determine whether the system is operating under normal conditions or is faulty. The BDE feature selection algorithm is adopted to optimize the seven-fault classification procedure to reduce the number of features. The results showed that the Extreme Learning Machine (ELM), based on statistical parameters followed by BDE, can detect faults with high accuracy (98.3%) compared to a case without optimization.

Modified puma-optimized novel control strategy for seven-level modular multilevel converter-based static synchronous compensator in grid-connected photovoltaic systems

Modified puma-optimized novel control strategy for seven-level modular multilevel converter-based static synchronous compensator in grid-connected photovoltaic systems

Power quality enhancement of grid-integrated solar photovoltaic system with unified power quality conditioner

Introduction. To enhance the quality of power and ensure a consistent electricity supply, this study proposes the utilization of a unified power quality conditioner (UPQC) system integrated with solar photovoltaic (PV) technology. The innovation involves single DC-link connecting back-to-back voltage-compensating components arranged in series and shunt, forming the PV-UPQC. The shunt compensator utilizes energy from a PV array to address harmonics in the load current. The objective is to mitigate voltage dips and spikes by injecting voltage that is either in phase with or out of phase with the common coupling point through a series compensator. The method combines the benefits of generating renewable energy to enhance electrical quality. The goal of the paper is the power quality enhancement of grid-integrated solar PV system. The novelty of the proposed work consists of enhancement of grid-integrated solar PV system with UPQC. The purpose of integrating a UPQC into a grid-connected solar PV system is to enhance power quality by mitigating issues such as voltage fluctuations, harmonics and reactive power imbalance. Methods. The proposed topology is implemented in MATLAB/Simulink with grid-integrated solar PV system with UPQC. Results. Integrating UPQC with a grid-connected solar PV system yields substantial improvements in power quality. This includes effectively mitigating voltage fluctuations and harmonics, resulting in smoother operation and reduced disturbances on the grid. Practical value. The proposed topology has proven to be extremely useful for grid-integrated solar PV system with UPQC applications. References 15, table 2, figures 9.

Design and implementation of a solar power optimizer for module level power electronics application

AbstractPartial shading on series‐connected photovoltaic (PV) panels in conventional PV systems results in lower harvested power. To resolve this, it is vital to utilize module level power electronics (MLPE) such as Solar Power Optimizers (SPOs). This paper introduced a non‐isolated common ground non‐inverting output voltage buck‐boost converter as an SPO. Proposed converter benefits from continuous input and output currents which has a significant role in designing SPOs. Having a quadratic gain, beside acceptable step‐down range are other features of the converter. Operating principle, design, steady‐state, small‐signal analysis, and dynamic performance of proposed converter are included. Proposed converter is compared with other buck‐boost converters in terms of voltage gain, voltage stresses, continuous input and output current, and output polarity. To validate the performance of introduced converter, experimental results for a prototype with input voltage 24 V, output voltage 72 V for step‐up and 15 V for step‐down modes are given and results are examined. The maximum efficiency of the prototype is 93% and 89% for step‐up and step‐down modes, respectively. To evaluate the effect of proposed SPO for extracting maximum available power from PVs, simulation results of a grid connected PV system with two series connected SPOs is discussed.

Direct Normal Irradiance Prediction based Optimum Interval Tilt angles for Enhancement of Energy Output, Levelized Cost of Energy and CO2 Emission in a Grid-Connected PV System

Abstract The tilt angle of photovoltaic (PV) panels is a crucial determinant of their performance and can be adjusted using different tracking methods. Periodically changing the tilt angle strikes a practical balance between efficiency and cost. This work introduces a Bi-directional Long Short Term Memory (Bi-LSTM)-based Direct Normal Irradiance (DNI) prediction to estimate the time intervals for the tilt angle adjustments. DNI Prediction involves 22-year (2000–2022) historical time series data and the Bi-LSTM deep learning model to predict DNI at different time frames for the location Madurai, India. Using the Predicted DNI, Tilt angle-based DNI is mapped using the tilt angle correlation through a nearest neighborhood interpolation method. DNI potential over a specific period is utilized to find the optimum time intervals for the tilt angle adjustments. The simulation study of this work is implemented with the design of a 5 kW grid-connected solar PV system in PVSYST software. The effectiveness of the proposed methodology is evaluated based on improvements in power output, Levelized Cost of Energy (LCOE) and carbon emission reductions and compared with other existing methods. The results showed that using the proposed optimal tilt angle intervals led to a 10.31% increase in PV output power, the lowest LCOE at 3.61 c/kWh and 8.363 tCO2/year carbon emissions.

Sizing of Grid-Connected Photovoltaic System with Roof Space Constraint

The installation of building-integrated photovoltaic systems has increased over the past years. This paper presents the sizing method of photovoltaic systems with rooftop space as a constraint. A sizing tool is developed to assist the design of PV systems with roof space constraints by using Visual Basic for Application. This new sizing tool is developed by implementing an iterative technique and is able to optimize the quantity of PV panels while overcoming the problem of space constraints on the rooftop. A roof dimension from Google Maps is used for the case study. This tool minimizes the design process time for an integrated PV system with minimum information on a rooftop.

Economic viability of decentralised battery storage systems for single-family buildings up to cross-building utilisation

Due to the great cost decrease of photovoltaics as well as battery storage, especially in the segment of decentralised home storage, the number of grid-connected battery-supported photovoltaic systems being installed in recent years is steadily increasing. However, the scientific community has intensively discussed that lithium-based battery storage systems cannot yet be operated economically in most cases. This paper addresses the level to which the cost of lithium battery storage needs to decrease in order to be economically viable. For this purpose, the economic viability of battery storage systems in single-family buildings, multi-apartment buildings and across-buildings is analysed on the basis of a linear optimisation model and the method of the internal rate of return. The utilisation of the storage system is optimised for different battery and photovoltaic capacities on the basis of generation and consumption. The internal rate of return method is used to compare the savings resulting from the reduced consumption from the electricity grid with the investment costs and the operation and maintenance costs. In order to be able to estimate the influence of the most important parameters a sensitivity analysis is also carried out. The analysis concludes that, depending on the combination of capacities of photovoltaics, battery storage and in relation to the load profile, the battery storage costs would have to drop by at least 85% in order to generate a certain predefined return over a depreciation period of 25 years. Furthermore, the more different load profiles can be covered directly with photovoltaic electricity, e.g. in a multi-apartment building or across buildings, the less electricity needs to be stored and this reduces the benefit and the utilisation of the battery storage and therefore the specific investment costs must further decrease. Another conclusion that emerges from the sensitivity analysis is that the electricity price and the spread between the electricity price and the feed-in tariff have the greatest influence on the investment costs and profitability. Due to limited space for photovoltaics and simultaneously high consumption, self-consumption is already quite high with cross-building utilisation and can no longer be increased to the necessary extent by the battery storage system, which is why the investment costs must also be lower. The novelty of this paper lies in particular in the fact that it deals with the target costs of battery storage systems in various scenarios for certain rates of return. The analyses in this paper are intended to provide a deeper understanding of the framework conditions for the economic operation of a battery storage system in the aforementioned scenarios. However, this paper does not take into account alternative sources of income other than savings on grid consumption. The possibility of time-variable (grid) tariffs, for example, is also not considered in detail in this paper and should be analysed further in future work.

A review on modulation techniques of Quasi-Z-source inverter for grid-connected photovoltaic systems

Photovoltaic (PV) is a promising way to meet the increasing global energy demand due to its sustainability, efficiency, and cost-effectiveness. For the wide-scale adoption of PV systems, converters with reliable input sources, stable control strategies and appropriate modulation techniques must be designed. In the literature, various modulation techniques have been developed that help to boost the voltage of the PV modules by implementing shoot-through (ST) in which the upper and lower switches of an inverter conduct simultaneously and short-circuit occurs. Various optimised modulation techniques have been implemented to enhance its performance. Among those, the quasi-Z-source inverter (qZSI) has attracted much attention due to its ability to achieve higher conversion ratios for grid-connected PV applications. In this paper, a detailed comparison of the modulation schemes for the qZSI PV systems has been done to understand the trade-off and select the most suitable approach. Upon the selection of the space vector modulation with unique switching sequences and rearranging upper ST and lower ST states, the inverter can achieve ST with reduced switching losses. Furthermore, a 600 VA three-phase grid-connected system utilizing a three-level neutral-point-clamped qZSI topology is modulated and simulated. It has been demonstrated that the constant boost control offers good performance in terms of reduced voltage stresses on switches, lower total harmonic distortion, and hence, higher efficiency.

A novel technique to detect and mitigate harmonic during islanding in grid connected PV system

The integration of photovoltaic (PV) systems into the power grid has become increasingly prevalent, providing sustainable and renewable energy solutions. However, the occurrence of islanding events, where a portion of the PV system operates independently from the main grid, presents challenges in maintaining power quality. This research paper investigates the harmonic distortions during islanding in grid-connected PV systems and proposes effective mitigation strategies. The study employs advanced simulation tools to analyse the dynamic behavior of the PV system during islanding scenarios, focusing on the occurrence and characteristics of harmonics. A detailed investigation into the root causes of harmonic distortions is conducted, considering factors such as grid fluctuations, inverter operation, and system impedance. Based on the findings, this paper introduces novel harmonic mitigation techniques tailored for islanding conditions. These techniques encompass advanced control algorithms, filter designs, and modulation strategies to suppress harmonics and enhance the overall system performance during islanded operation. Comparative assessments of different mitigation approaches are presented, evaluating their effectiveness in minimizing harmonic distortions and maintaining grid code compliance. The research contributes valuable insights to the field of grid-connected PV systems, offering a comprehensive understanding of harmonic issues during islanding events and proposing innovative solutions for their mitigation. The outcomes of this study are crucial for advancing the reliability, stability, and power quality of PV systems that are connected to the grid, which makes it possible for green energy sources to be added to the grid without any problems.

Reducing neural network complexity via optimization algorithms for fault diagnosis in renewable energy systems

This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-connected photovoltaic (GCPV) and wind energy conversion (WEC) systems in fault diagnosis. To address this issue, several methods are proposed to reduce computation time, minimize memory requirements, and improve efficiency. First, optimization algorithms such as the Salp Swarm Algorithm (SSA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), combined with machine learning classifiers for feature selection, are suggested to reduce computational time and memory space requirements. This approach notably decreases computation time but has a limited impact on memory space. Secondly, further reduction in memory space requirements is recommended by using the variogram method for data reduction. This method can leverage the outputs of preceding algorithms to optimize renewable energy systems, making their operations cost-effective and efficient. Finally, the outputs of the variogram are used to train neural network (NN), recurrent neural network (RNN), and long short-term memory (LSTM) classifiers to differentiate between various modes of operation in GCPV and WEC systems. Experimental results demonstrate the effectiveness and robustness of the proposed methods, showing that memory space can be reduced by 1.3 to 5.6 times and CPU time by 1.1 to 11 times while maintaining accuracy and improving efficiency compared to conventional algorithms.

Failure analysis of photovoltaic strings by constructing a digital multi-twin integrating theory, features, and vision

Timely and accurate failure analysis of photovoltaic (PV) systems is crucial forensuring the stable operation of power grids. However, existing failure analysis and diagnosis algorithms based on deep neural networks excessively rely on high-quality failure state data collected by sensors. This is extremely difficult to achieve in real photovoltaic power plants that are commonly equipped with self-protection mechanisms. To address this issue, we propose a Digital Multi-Twin integrating Theory, Features, and Vision (TFV-DMT) for failure analysis of PV strings in PV systems. This method first constructs theoretical simulation twins, feature twins, and visual twins based on the concept of digital twins, specifically tailored for actual PV systems, and designs a multi-twin collaborative model for model updating and failure diagnosis. Secondly, to better construct the visual twin, we introduce a Two-Dimensional Gram Angle Field Transformation Algorithm (TDGAF) to achieve targeted two-dimensional mapping of PV feature data, facilitating a more direct expression of failure characteristics. Furthermore, by constructing a Swish-activated Deep Convolutional Generative Adversarial Network (SDCGAN) to achieve balanced augmentation of mapping data, the model bias of theoretical simulation twins can be reduced. Finally, we propose a Swin-LT network that incorporates a Lightweight Dual-Channel Attention Module (LDAM) to better analyze the features of the visual twin, enabling more precise fault diagnosis. The algorithm has been validated on a real 250 kW grid-connected PV system, with results indicating that the proposed digital twin model is effective, achieving a diagnosis accuracy rate of 98.8 % for string failures.

Dynamic Global Maximum Power Point Tracking for Partially Shaded PV Arrays in Grid-Connected PV Systems

Dynamic Global Maximum Power Point Tracking for Partially Shaded PV Arrays in Grid-Connected PV Systems

Assessing dynamics of urban solar PV power generation using grid divisional method

The assessment of solar energy potential and urban density has become a crucial prerequisite for urban sustainable development. However, two significant challenges persist: the high computational cost associated with assessments across large urban areas and the lack of detailed rooftop information for each building. This study aims to integrate solar photovoltaic (PV) systems in urban environments of varying built density in an Indian city and assesses the solar energy potential (SEP) using grid divisional method and simulations. The methodology involved extracting, filtering and developing a 2.5 D model with GIS using the open buildings dataset, followed by simulations of grid-connected PV systems using PV-Sol. The results demonstrated the dynamics in correlation of SEP and grid morphology indicators, with built density and roof area showing a strong positive relation. The financial analysis resulted in average payback period of 8–10 years. The study also highlighted significant CO2 emission reductions, with thermal power plants generating 26,982 tons/kWh compared to just 1,513 tons/kWh for solar power. The findings demonstrate the financial viability and environmental benefits of PV adoption in urban areas, offering crucial insights for sustainable energy planning and policy development.

CGAformer: Multi-scale feature Transformer with MLP architecture for short-term photovoltaic power forecasting

Accurately predicting the output power of photovoltaic (PV) systems is an effective means to ensure the reliable and economical operation of grid-connected PV systems. Aiming at the characteristics of PV power generation such as strong volatility, high intermittency and obvious periodicity, a hybrid model named CGAformer based on One-Dimensional Convolutional Neural Networks (CNN1D), Global Additive Attention (GADAttention), and Auto-Correlation is proposed for short-term PV power generation prediction. The model uses CNN1D to extract local features and obtains global weights by improving the GADAttetion obtained by additive attention. Auto-Correlation integrates local features and global weights and identifies repeated patterns in the sequence to obtain highly coupled multi-scale features, and finally generates the final prediction results through Multilayer Perceptron (MLP). In order to verify the effectiveness of the model, this paper uses a historical dataset from a PV system located in Uluru, Australia for sufficient experiments. In the comparative experiments, The overall average RMSE and MAE of CGAformer are improved by 6.82% and 20.46% respectively compared with long short-term memory (LSTM). In addition, ablation experiments and seasonal analysis are used to verify the effectiveness of the model and its excellent generalization ability for different seasons.