Grid Integration Of Photovoltaic Systems Research Articles

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.

Novel adaptive MPPT technique for enhanced performance of grid integrated solar photovoltaic system

In order to extract the maximum photovoltaic (PV) power under stochastic weather conditions, a maximum power point tracking (MPPT) technique is required. PV modules behave nonlinearly in unpredictable weather conditions, and the effectiveness of many control strategies under such conditions especially conventional ones—decreases significantly. It is essential to maximize the use of PV power in the system; thus, MPPT algorithms have been developed to ensure that grid integrated PV systems work best at the maximum power point regardless of weather conditions. This research presents a novel HLO-ANN (Horned Lizard optimized artificial neural network) MPPT technique for grid-integrated solar photovoltaic systems which is the improved version of the ANN-MPPT, developed using a novel HLO technique, which effectively optimizes weights of the neural network. The irradiance and temperature datasets from the NASA website have been utilized to train the neural network. The proposed methods have been evaluated by comparing their simulation results to other ANN optimized MPPT techniques: The results showed that the proposed method outperforms the other methods in terms of robustness, DC bus voltage regulation, tracking speed, convergence to the minimum error value, and tracking efficiency in maximizing the harvested power from the PV systems. Furthermore, the EN50530 MPPT efficiency test was carried out during both fast and slow-varying irradiance levels to examine the effectiveness of proposed algorithms.

Enhanced MPPT approach for grid-integrated solar PV system: Simulation and experimental study

The global solar energy utilization has significantly risen, mostly due to the technological, economic, and environmental advantages it offers, particularly through the implementation of photovoltaic (PV) systems. The energy output of PV systems is contingent upon climate conditions. A new hybrid two-stage maximum power point tracking (MPPT) system developed to optimize power extraction and increase efficiency in PV systems. The system consists of two stages: a perturb and observe (P&O) MPPT for regulating the reference voltage in the first stage, and an enhanced model reference adaptive controller (EMRAC) for adjusting the DC-DC converter duty cycle in second stage. This system is designed primarily for 100.7 kW three-phase grid-integrated PV systems operating in environments with rapidly changing solar irradiation and temperature levels. In order to demonstrate efficacy of the suggested approach, a probabilistic evaluation is conducted across three levels of uncertain scenarios, namely ropp, step irradiance profile, and rapid changing irradiation and temperature profile. The proposed approach attained maximum power point (MPP) in 0.11 s and had an average tracking efficiency of 98.28 %. Quantitative, statistical, comparative and experimental analyses utilizing the dSPACE 1202 reveal the effectiveness of the proposed control strategy and demonstrate the superior performance over P&O, INC and ANN based on the power output, response time, power loss, efficiency, and error rates.

Harmonic Mitigation for Enhancing Grid-Integrated Photo voltaic System Power Quality: A Review

With the growing integration of renewable energy sources, such solar energy systems into electrical grids, power quality issues emerge from possible harmonic distortions. This study analyzes the effectiveness of grid-integrated photovoltaic systems and discusses harmonic mitigation to improve electricity quality. Power quality can be negatively impacted by harmonic distortion due to voltage swings, higher losses, and interference with delicate equipment. As a result, efficient and dependable PV system functioning inside the grid depends on the implementation of effective mitigation techniques. We examine a range of mitigation strategies, such as hybrid solutions, active filters, and passive filters, each with unique benefits and drawbacks in terms of expense, intricacy, and efficiency. The effects of integrating PV systems on grid stability, voltage regulation, and frequency management are also covered in the article, emphasizing the significance of thorough performance analysis. The effectiveness of various harmonic mitigation techniques is assessed by modeling studies and experimental validation, taking into account variables including system efficiency, dependability, and compliance with international power quality standards. The study also looks at how grid circumstances, PV system layout, and size affect harmonic generation and mitigation needs. The research yields valuable insights that aid in the formulation of strategies aimed at improving power quality and maximizing grid-integrated photovoltaic systems' performance under various operating situations. Keywords: Renewable Energy sources, Shunt Active Power Filter, harmonics, fuzzy logic controller, PID controller.

Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.

ANFIS Controlled MMC-UPQC to Mitigate Power Quality Problems in Solar PV Integrated Power System

This paper focuses on the implementation of a Modular Multilevel Converter (MMC) based Unified Power Quality Conditioner (UPQC) in a grid-integrated Photovoltaic (PV) system. The integration of photovoltaic (PV) systems into the grid brings numerous benefits in terms of renewable energy utilization like sustainable energy generation, energy security, grid resilience, and economic growth. However, the voltage source converters (VSCs) employed in PV systems can introduce power quality (PQ) issues, including voltage fluctuations, harmonics, and reactive power imbalances and grid instability. The objective of this work is to enhance power quality by mitigating voltage and current fluctuations and harmonics generated due to VSC, compensating reactive power requirements by the load and PV system, and regulating the DC link voltage. For DC voltage regulation, an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is employed to control for MMC-UPQC. The proposed control strategy is simulated using MATLAB/SIMULINK software and the results are compared with conventional Proportional Integral (PI) and fuzzy controllers. Various dynamic conditions such as voltage sag and swell at the point of common coupling (PCC), changes in irradiance for the PV system, and load variations are considered. The simulation results demonstrate that the ANFIS-controlled MMC-based UPQC outperforms the PI and fuzzy controllers in terms of power quality improvement. It effectively suppresses harmonics, maintains a stable DC link voltage, and compensates for reactive power fluctuations. The proposed control strategy provides superior performance and achieves better power quality compared to conventional controllers. The findings of this work highlight the potential of using an ANFIS controller in MMC-based UPQC systems for grid-integrated PV systems.

Power Quality Alleviation of a Grid-Integrated Photovoltaic System Based on Novel Meta-Heuristic Optimization Technique Using OPAL-RT Real-Time Platform

Power Quality Alleviation of a Grid-Integrated Photovoltaic System Based on Novel Meta-Heuristic Optimization Technique Using OPAL-RT Real-Time Platform

A novel single phase grid connected solar photovoltaic system for state of charge estimation using recurrent neural networks

ABSTRACT Renewable energy (RE) resources have reached the top-notch state in satisfying the energy demands of recent days. Solar photovoltaic (PV) is considered as a clean form of energy among the various RE resources. A novel single-phase grid-integrated solar PV system with Re-lift Luo converter with aid of a chicken swarm (CS) optimization algorithm is presented. The Re-lift Luo converter is implemented as it has maximum power density, extreme efficacy, high-voltage-gain ratio, and reduced switching losses. Since the PV output is intermittent, the output of the converter also gets varied, which will be maintained constant by implementing a proportional–integral (PI)-based closed loop control through the optimized CS algorithm. The normalized output is further applied to the grid via single-phase voltage source inverter. A battery is utilized to store the daytime’s solar energy and discharges to the grid during the night time. The monitoring of the battery’s state of charge (SoC) is performed by recurrent neural network (RNN). A PI controller achieves the real and reactive grid side power control. The novelty of the proposed work is the utilization of three control strategies such as PI, CS, and RNN in improving the overall performance of a grid connected system in improving the power quality. Proper control and analysis of the converters and battery through intelligent controllers are the major objectives of the paper. The outcome of the proposed concept reigns in the improvement of power quality and the proper estimation of SoC, which was supported by the detailed simulation and experimental studies. The proposed configuration is verified by simulation in MATLAB/Simulink and also tested in a real-time experimental setup, and the obtained results are as per the IEEE standards.

Cascade feedforward neural network and deep neural network controller on photovoltaic system with cascaded multilevel inverters: Comparison on standalone and grid integrated system

The introduction of a micro-grid-based power generation network will help to meet the demands of consumers while reducing environmental impact. Several industrialized and emerging countries allocate considerable resources to renewable energy-based power generation and invest significant sums of money in this area. This study examines the challenges involved with electricity generation through photovoltaic (PV) systems and the integration of the same with the grid to mitigate power quality issues and improve the power factor for various loading conditions. An innovative multilayer inverter for grid-connected PV systems has been developed to enhance the voltage profile and resulted in a drop in total harmonic distortion (THD). A cascade multilevel inverter (associated with a grid-integrated PV system and managed using multiple innovative artificial intelligence controllers) was developed in this research project. Various advanced intelligent controllers, such as cascade feedforward neural networks (CFFNN) and deep neural networks (DNN), have been analyzed under various operating situations and observed that the THD of voltage, current at the grid, and the load is less than 3 % as per the IEEE 519 standards along with this power factor is maintained nearly unity for the grid-connected system. The quality of power in terms of voltage, frequency, total harmonics distortion, and power factor are improved by using a novel deep neural network algorithm in a cascaded multilevel inverter and verified according to IEEE 1547 and IEEE 519 standards to determine the efficacy of the proposed system.

Grid-integrated single phase solar PV system employed with single switch high gain boost converter

• This paper deals with a grid-integrated single phase solar photovoltaic system (GISPSPS) employed with a single switched based high gain boost converter. The significant contributions in this paper are; • The dynamic model of HGBC including effective series resistance (ESR) of passive elements is developed for improving accuracy with slight increase in complexity of model. Then its stability analysis is carried out under different solar irradiations (i.e. 300, 500, 800 and 1000 W/m 2 ) using an eigenvalue plot. • The HGBC control is obtained using the sliding mode control (SMC) for PPE under change in solar irradiations. In proposed SMC only two sensing signals PV panel voltage and current (i.e. v pv and i pv ) are needed for PPE in comparison to three sensing signals PV panel voltage, current and DC link voltage (i.e. v pv , i pv and v dc ) require in SMC of ref. [10] . • The control of FBVSC is obtained by a F i OGI controller. The F i OGI provides noise-less extraction of synchronization signal, under presence of DC offset and noise in grid voltage. However, FOGI used in ref. [10] exhibits poor performance under presence of noise in grid supply voltage. This paper presents a grid-integrated single phase solar photovoltaic (PV) system (GISPSPS) with its modeling, design and control. The GISPSPS consists of a single switch based high gain boost converter (HGBC) and full bridge voltage source converter (FBVSC). The HGBC, boost the low PV panel voltage and extracts the optimum PV power. The HGBC allows high voltage conversion ratio and possesses less voltage stresses on power devices. The optimum PV power is extracted via a sliding mode control (SMC) strategy that generates the switching signal for the switch of HGBC. The FBVSC delivers PV power to the single phase utility grid, as well as load connected at point of interfacing (POI). A fifth order generalized integrator (F i OGI) is developed for FBVSC control to ensure harmonic suppression and DC offset rejection capability during extraction of fundamental grid voltage. Experimental validation of proposed GISPSPS and its control is also verified.

Optimized RNN-oriented power quality enhancement and THD reduction for micro grid integration of PV system with MLI: Crow Search-based Harris Hawks Optimization concept

Grid-connected Photo Voltaic (PV) power systems are becoming increasingly popular in several nations. The goal of achieving maximum power and acceptable power quality in a grid-connected PV power system is considered a major difficulty. Hence, this paper develops an artificial intelligence-based optimization concept for PV system and novel cascaded Multi Level Inverter (MLI) for the grid integration of PV system. The cascaded MLI was designed with fewer power electronic switches and can function at asynchronous voltage sources, making it the most suitable for PV systems. This novel inverter minimizes the THD at the output with the help of enhancing the output voltage level. It also improves the power quality of the system. The micro grid integration of the introduced inverter is controlled by Optimized Recurrent Neural Network (ORNN), where the hidden neurons are tuned by novel hybrid meta heuristic algorithm by merging Crow Search Algorithm (CSA) and Harris Hawks Optimization (HHO) leading to Crow Search-based Harris Hawks Optimization (CS-HHO). The proposed model is designed at several loading conditions and weather conditions. The simulation findings proved the efficiency of the developed system.

Anti-windup filtered second-order generalized integrator-based spontaneous control for single-phase grid-tied solar PV-H2/Br2 redox flow battery storage microgrid system

Photovoltaic (PV) sources are increasingly deployed as distributed generators in remote single-phase utility grids. Meanwhile, variable power demand and fluctuating PV power output pose a challenge to utility grid stability and consistent power supply. This article introduces a hydrogen/bromine (H2/Br2) redox flow battery (RFB) for a single-phase grid-integrated PV system. A unique spontaneous control scheme for a single-phase two-stage grid-connected PV-H2/Br2 RFB system is proposed using an orthogonal signal generator-anti-windup filtered second-order generalized integrator (OSG-AWFSOGI) and tilt integral derivative (TID) controller. The OSG-AWFSOGI extracts the fundamental component from the grid voltage, even when the grid voltage is characterized by under-voltage, over-voltage, severe harmonics distortion, and other uncertain conditions. The TID controller is also used to regulate the DC-link voltage and current of the H2/Br2 RFB under various disturbances and dynamic conditions to deal with system fluctuation and prevent further degradation of the voltage quality. The control architecture also incorporates a feed-forward component to provide a quick dynamic response. Furthermore, a new sparrow search optimization algorithm is used to fine-tune the controller parameters for optimum TID control action. A battery control scheme is developed to control the H2/Br2 RFB to smooth the PV output and boost system operation planning, resiliency, and efficiency by providing short response time, rapid regulation, and voltage support. An incremental conductance-voltage current control (INC-VCC) maximum power point tracking (MPPT) system is proposed, with VCC based on a TID controller for rapid PV MPPT under solar irradiation changes and unexpected grid disturbance conditions. The proposed control is modelled and simulated in Matlab and verified using the OPAL-RT real-time simulator test bench. During simulation and testing, irradiance and temperature variations on PV array, load perturbation, over voltage, under voltage in grid voltage, and different complex disturbances and dynamic conditions are considered. Also, in comparison results, the proposed strategy outperformed compared to state-of-the-art approaches.

A Novel Control Strategy in Grid-Integrated Photovoltaic System for Power Quality Enhancement

The integration of solar photovoltaic (PV) systems and utility grids has gradually gained significant interest in improving the sustainability of clean power supply for society. However, power quality remains a challenge due to partial shading conditions and harmonics. To overcome these drawbacks, a flexible radial movement optimization based on a dynamic safety perimeter maximum power point tracking algorithm is employed to track maximum power out of a PV system and to ensure that the optimum voltage level at the common DC bus is obtained under partial shading conditions using fixed-tilt installation configuration. Furthermore, a novel inverter control loop system with a double second order generalized integrator phase-locked loop (DSOGI-PLL) is also proposed to mitigate harmonics and improve the power quality of the grid interfacing PV system using MATLAB SIMULINK software. The proposed system has several merits, such as better harmonic suppression capability, control adaptivity, rapid tracking speed, low computational burden and phase and grid synchronization.

Lead compensator-based voltage control of quasi Z-source inverter for grid integration of photovoltaic system

This paper proposes lead compensator-based voltage control of a 3-phase quasi Z-source inverter (q-ZSI) integrating a solar PV system with the grid. Shoot-through control is incorporated in voltage-oriented control for regulating the dc-link voltage of q-ZSI. The lead compensator, which provides robust performance by shifting the pole pair to the right hand of the s-plane to ensure system stability, helps to determine the shoot-through duty ratio. The lead compensator is designed based on the bode diagram for the transfer function of the capacitor voltage to shoot-through duty ratio with unity feedback gain. Additionally, using a lead compensator also results in simpler tuning requirements. The performance of 3-phase q-ZSI-based grid-tied PV with lead compensator-based dc-link voltage control is analysed under different operating conditions. The analysis reveals IEEE-1547 compliant operation with only active power being injected into the grid and dc-link voltage regulation.

An adaptive TS‐fuzzy model based RBF neural network learning for grid integrated photovoltaic applications

In this research work, an adaptive TS-fuzzy based RBF neural network (ATSFRBFNN) algorithm based maximum power point tracker (MPPT) is employed for grid integrated photovoltaic (PV) system. The novel learning algorithm provides accurate and rapid PV power tracking under fluctuating solar insolation. However, an improved damping circulating current limiting inverter controller is employed to generate gating signal of voltage source inverter and to deliver reduced harmonic constant, less power loss, mitigation of power quality issues regulation of Dc-link voltage utilization for grid integrated PV system under steady, and transient weather conditions. The experimental results justify the improved system performance under varying operating conditions. The behaviour of novel hybrid PV MPPT structure is compared with TS-fuzzy, RBFNN, and GA-RBF for higher tracking efficacy, least tracking deviation, accurate responses, and zero steady-state power oscillation around MPP region.

Grey wolf optimization-recurrent neural network based maximum power point tracking for photovoltaic application

To increase the photovoltaic (PV) power-generation conversion, MPPT is the primary concern. This works explains about the grey wolf optimization (GWO - RNN)-based hybrid maximum power point tracking (MPPT) method to get quick and maximum photovoltaic (PV) power with zero oscillation tracking. The GWO – RNN based MPPT method doesn’t need additional sensor for measuring irradiance and temperature variables. The NLT is used for the multi-level inverter (MLI) control strategy to achieve less harmonics distraction and less switching losses with better voltage and current profile. This employed methodology brings remarkable aspects in the PV boosting potential extraction. A GWO – RNN controlled LUO converter is a zero output harmonic agreement impedance matching interface that is MPPT is performed by placing the PV modules between the load regulator power circuit and the load regulator power circuit. To actualize the proposed hybrid GWO – RNN model for the PV system, perturb and observe, RNN, ant colony optimization, and artificial bee colony MPPT techniques are employed. The MATLAB interfaced dSPACE interface is used to finish the hands-on validation of the intended grid-integrated PV system. The obtained results eloquently support the appropriate design of higher-performance control algorithms.

Power Quality Assessment & Mitigation of Grid Integrated PV System in Distribution Network

the need for electrical energy has become increasingly important. With the advancement of technology, computer and telecommunication networks, railway networks, banking, post offices, and life support systems are only a few examples of applications that cannot function without electricity. At the same time these applications demand qualitative energy. Thus, this Paper Presents the Integration of PV (renewable sources) into the existing grid to meet rising demand of electricity without stress on conventional power plant. Thereby, reducing greenhouse gases emissions to environment and this paper also presents D- STATCOM model to mitigate major power quality issues like Voltage Sag & Swell. In This paper, the design and performance of the D-STATCOM Control strategy were examined and simulated in this work utilizing the MATLAB Simulink environment. Keywords—Integration of Photovoltaic (PV), Voltage Sag & Swell, Phase Locked Loop (PLL), Voltage Source Inverter (VSI), Dynamic Voltage Restorer (DVR), D-STATCOM.

An experimental performance verification of continuous mixed P‐norm based adaptive asymmetrical fuzzy logic controller for single stage photovoltaic grid integration

AbstractThis paper deals with a new continuous mixed p‐norm (CMPN) based adaptive asymmetrical fuzzy logic controller (AAFLC) method for single stage grid integrated photovoltaic (PV) system as an inverter control strategy. Here, asymmetrical membership functions are employed to compensate the non‐linear characteristics of the PV module with effective system improvement. The proposed CMPN based algorithm provides online updates of the fuzzy logic controller (FLC) with rapid convergence velocity under varying operating conditions. The performance of a single‐stage grid integrated PV system controlled using CMPN based AAFLC has been investigated using the dSPACE DS1104 platform under steady and dynamic operating situations. Extensive practical analysis has been presented for verification of effective proposed inverter controller design under changing environmental conditions.

An investigation of Grid-Integrated Photovoltaic system intended for hybrid Moth Flame Optimization using ANFIS techniques

As the move towards Grid Integrated-Photovoltaic (GI-PV) system is proposed to improve the power quality development. A novel Adaptive Neuro-Fuzzy Inference System (ANFIS) based on improved Moth Flame Optimization (MFO) algorithm is described for grid integrated approach. The solar integration of Maximum Power Point (MPP) fed into modified Switched Boost Inverter (SBI) is presented, this GI-PV connected circuit has become prominent research in a recent scenario for energy demand. Proposed MFOA-ANFIS controller has generated the duty cycle pulses to each converter circuit. The benefit of grid-tied SBI is direct control outer-loop employed to obtain MFO-ANFIS techniques. To maintain a constant voltage DC-link is employed for inner-loop, this presence of constant DC-power to grid loads with support of MFO-ANFIS assists Proportional Integral Differential (PID) method. The results acquired by the simulation expressed that the proposed controller is addressed to maintain active and reactive power exchange, regulate DC bus-link voltages, grid voltage, and grid current. The effectiveness of the practical implication research is achieved by the output as represented as minimum grid harmonics, load current, and compensator current as verified in MATLAB/Simulink platform.

Feedforward-Feedback Based Adaptive Control Algorithm for Power Quality Improvement of Grid Integrated Photovoltaic System

Integration of renewable energy systems such as wind and solar energy with the regular grid via inverters has shown to be a dependable approach for delivering end customers with consistent and high-quality power. Controlling and synchronizing generated power with the grid requires integrating the control algorithm in a Grid Integrated Photovoltaic (GIPV) system. A feedforward-feedback (FFFB) based adaptive control technique for the 3-stage dual-stage GIPV system is proposed in this research. The fuzzy logic controller is employed for DC-link voltage feedback control in the proposed feedforward-feedback adaptive control algorithm, and an additional feedforward loop is introduced to improve the GIPV system's performance. The efficiency of the suggested control algorithm in managing inverter operation while maintaining power quality is demonstrated. Furthermore, the system response of providing reactive power compensation, harmonic compensation, and load balancing for varied loads is explored using MATLAB and the Simulink tools.