Grid-connected Photovoltaic System Research Articles

Fault detection and diagnosis of grid-connected photovoltaic systems using energy valley optimizer based lightweight CNN and wavelet transform

Early fault detection and diagnosis of grid-connected photovoltaic systems (GCPS) is imperative to improve their performance and reliability. Low-cost edge devices have emerged as innovative solutions for real-time monitoring, reducing latency, and improving response times. In this work, a lightweight Convolutional Neural Network (CNN) is designed and fine-tuned using Energy Valley Optimizer (EVO) for fault diagnosis. The CNN input consists of two-dimensional scalograms generated using Continuous Wavelet Transform (CWT). The proposed diagnosis technique demonstrated superior performance compared to benchmark architectures, namely MobileNet, NASNetMobile, and InceptionV3, achieving higher test accuracies and lower losses on binary and multi-fault classification tasks on balanced, unbalanced, and noisy datasets. Further, a quantitative comparison is conducted with similar recent studies. The obtained results indicate good performance and high reliability of the proposed fault diagnosis method.

Fault classification and mitigation in grid-connected solar PV system using hybrid whale optimization algorithm with ANFIS

ABSTRACT Generally, fault mitigation and classification technique has become an important issue in grid-connected PV (GCPV) systems. This study intends a control topology for fault grouping and mitigation scheme in GCPV systems. Whale optimization algorithm (WOA)-ANFIS controllers are applied to uphold the DC-link voltage in UPQC as a perpetual limit. The ANFIS algorithm is employed to attain the apprising procedure of the WOA. The ANFIS controller is working based on the ANN (Artificial Neural Network) and FIS (Fuzzy Interference system). Additionally, the fault classification is proposed in this paper. The fault classification is attained through the use of DWT and ANFIS. Initially, the normal signals are analyzed; the fault is created and extracted in the consumption of DWT. Then the extracted characteristics are fed to the ANFIS for classification. The ANFIS classified faults in the consumption of fuzzy rules. The goals of the projected model are to classify and mitigate the faults in GCPV systems. The projected system is executed using MATLAB/Simulink. So as to investigate the efficacy of the projected technique, the several categories of concerns are examined and verified with the prevailing tactics for instance, UPQC with PSO-ANFIS, UPQC with WOA-ANN method.

Nonlinear MPPT techniques to control hybrid power systems

In recent years, grid-connected multifunctional photovoltaic (PV) systems have proven to be highly efficient. This system integrates PV panels with a DC–DC boost converter (DC–DC–BC) and the electrical distribution grid (DEG). Linking the PV to the AC-DEG is accomplished through a three-level multifunctional voltage source inverter (MVSI). The DC–DC–BC component in this study is engineered to perform maximum power point tracking (MPPT) irrespective of normal or abnormal conditions. The conventional MPPT technique poses several challenges and constraints on system usage. Hence, the suggestion is to adopt synergetic control (SC) and sliding mode control (SMC) to enhance the MPPT technique's performance within the proposed system framework. Moreover, predictive direct power control is applied to the MVSI-based shunt active power filter, utilizing a phase-locked loop technique to achieve multiple objectives: minimizing energy fluctuations, injecting active power, correcting power factors, compensating reactive power, and mitigating harmonic currents. To implement the proposed system, the MATLAB is used for this purpose, with several tests used to study the behavior of the controls proposed in this work. Numerical results indicate significant reductions in active and reactive power fluctuations, with estimated rates of 38.46% and 15.30%, respectively, compared to traditional strategies. Moreover, the total harmonic distortion (THD) of the source current after filtering is reduced by 31.88% under solar irradiation of G = 1000 Wm2. Before filtering, the THD of current experiences a reduction estimated at 97.65%. These findings underscore the superior performance of the proposed control technique across all evaluated aspects.

A comprehensive analysis of the optimal GWO based FOPID MPPT controller for grid-tied photovoltaics system under atmospheric uncertainty

Recent years have seen an increase in the deployment of renewable energy sources on power system networks as a result of the gradual development of renewable energy sources. In order to utilize these intermittent sources, grid integration is crucial, since weather conditions are hard to predict accurately and do not match the pattern of generation. As a result, controlling energy becomes an unavoidable challenge when the alternative energy source is connected to the grid in a distributed manner. A number of factors contribute to the problem, including sporadic sources, daytime expenses, and limitations concerning the specifications of solar panels. Therefore, a more robust control system is needed to enhance the reliability and efficiency of the renewable energy integrated power network. Thus, this manuscript presents the design, implementation, and performance of an improved fractional order PID (FOPID) controller for proposed grid-connected photovoltaic systems. To maximize the power from the photovoltaic source, the controller is designed to extract as much energy as possible. In addition to having the adaptive nature of a PID controller, the proposed FOPID controller is capable of optimizing its gain parameter according to the generator and grid side parameters being considered. The proposed study utilized grey wolf optimization (GWO) to tune the FOPID controller for tracking the quadrature axis model, DC link voltage and current regulation, and maximizing the maximum power point. Further, FOPID is used to perform the system's current control functions, and each time the error is measured, the regulating parameters are updated accordingly. This paper contrasts fuzzy logic controllers (FLC), flying squirrel search optimization (FSSO), and PSO-tuned FOPID controllers with the proposed work. The simulation's results are displayed and examined in the section on results and outcomes. The obtained results demonstrate maximum solar power output under erratic weather conditions, validating the effectiveness of the proposed controller.

Design Study of high-power PV grid-connected Inverter System based on the Particle Swarm Algorithm

Design Study of high-power PV grid-connected Inverter System based on the Particle Swarm Algorithm

Investigation of condition monitoring system for grid connected photovoltaic (GCPV) system with power electronics converters using machine learning techniques

Power systems protection has become more vital in recent years to ensure stability, reliability, security, and power quality due to the exponential growth of grid-connected photovoltaic (GCPV) systems. As a result, several nations have set new grid codes for the grid integration of PV plant installations to overcome these concerns. Investigating Fault Ride Through (FRT) capacity is one of the primary criteria for grid codes. For 3-phase GCPV systems, fault detection and classification approaches are proposed in this study. Firstly, different faults that occurred in the GCPV system are categorized and compared, with the critical and analytical evaluation of grid codes, particularly FRT requirements such as Low Voltage Ride Through (LVRT) and High Voltage Ride Through (HVRT) for different nations. Further, a detailed classification and a comparison of the existing FRT techniques are presented for better control methods based on system complexity, detection accuracy, and other evolutionary criteria. To ensure smooth grid operation, accurate fault detection and condition monitoring of the systems are required. This paper discusses a machine learning (ML) based technique for detecting faults in 3-phase GCPV systems. Multi-peak phenomena caused by FRT capabilities and anti-islanding detection are the main problems experienced while integrating a PV system into the local grid. The fault classification technique is developed using weighted K-nearest neighbor (WKNN) and fine Gaussian support vector machine (FGSVM) based ML approaches utilizing Wavelet Transform. The proposed ML-based findings show that the fault detection algorithm-based classification accuracy has significantly improved.

Analyzing the consequences of power factor degradation in grid-connected solar photovoltaic systems

This study examines the impact of integrating solar photovoltaic (PV) systems on power factor (PF) within low-voltage radial distribution networks, using empirical data from the Energy Self-Sufficiency for Health Facilities in Ghana (EnerSHelF) project sites in Ghana. The research included simulations focusing on optimal PV integration, with and without PF considerations, and the strategic placement of PV and shunt capacitors (SC). Three scenarios evaluated PV injection at high-load demand nodes, achieving penetration levels of 85.00 percent, 82.88 percent with high voltage drop, and 100.00 percent with high loss nodes. Additionally, three scenarios assessed SC allocation methods: proportional to the node's reactive power demand (Scenario I), even distribution (Scenario II), and proportional to installed PV capacity at PV nodes (Scenario III).The analysis used a twin-objective index (TOI), combining voltage deviations and power factor degradation. Results showed significant PV curtailment was necessary to achieve standard PF. Optimal penetration levels, considering TOI, reduced PV penetration from 85.00 percent to 63.75 percent, 82.88 percent to 57.38 percent, and 100.00 percent to 72.50 percent for high load, high voltage drops, and high loss nodes, respectively. Notably, all scenarios showed a concerning PF of 0.00 at dead-end nodes (P20, P21, P22).Scenario I achieved PF ranges of -0.26 to 1.00 with PV at high load, -0.69 to 1.00 with PV at high voltage drop, and 0.95 to 1.00 with PV at high loss nodes. Scenario II produced similar ranges, -0.48 to 1.00, -1.00 to 0.99, and 0.30 to 0.96, with PV placement at high load, voltage drops, and loss nodes, respectively. Scenario III yielded ranges of -0.19 to 0.97 (high load), -0.23 to 1.00 (high voltage drop), and 0.86 to 0.96 (high losses).The study concluded that the most effective strategy involves installing PVs at high-loss nodes and distributing SCs proportionally to the node's reactive power demand (Scenario I). This approach achieved a more uniform PF pattern throughout the network, highlighting the practical implications of strategic PV placement and targeted reactive power compensation for maintaining a healthy and efficient distribution system with solar PV integration.

Experimental development of a method of short and medium-term photovoltaic generation forecasting using multivariate statistics and mathematical modeling

Uncertainties in photovoltaic solar energy production can make it challenging to dispatch energy into the electricity grid. Although photovoltaic generation storage solves this problem, a forecasting of the photovoltaic solar energy produced is necessary to control the energy injected into the grid. This article aims to develop the probabilistic methodology Reduced-Rank Regression (RRR) for forecasting photovoltaic generation in the short and medium terms. The RRR methodology forecasting uses the generation data of a grid-connected photovoltaic system. The proposed RRR model is simple, easy to access and apply, and does not use irradiance data. The model developed uses the multivariate statistical analysis technique. A advantage is that with a correlation with the performance indices of photovoltaic solar energy systems, the proposed method can be applied in any geographical location on the planet and with different photovoltaic solar energy systems. The application of the RRR methodology requires two searches/inputs. The first input is weather forecast data obtained from a weather forecasting platform, and the second is actual historical data on photovoltaic generation at the site where the method was developed. The proposed method was compared with the persistence method. Using a horizon of 1–10 h, the average monthly root mean square error for the RRR ranged from 7.3 % to 50.1 %. For the persistence method, the average monthly root mean square error ranged from 15.1 % to 65.0 %. Therefore, with the horizon of 24 h, the average monthly root mean square error for the RRR ranged from 4.5 % to 43.2 %. For the persistence method, the average monthly root mean square error ranged from 11.5 % to 75.0 %. We show experimentally that our method is competitive with the state-of-the-art in terms of obtaining photovoltaic generation forecasting without using solar radiation data.

Research on the coupling mechanism of transient overvoltage in large-scale new energy station grid connection

Abstract The large-scale new energy station transmission system is relatively weak, and long-distance transmission is prone to transient overvoltage. The hysteresis of the control loop of the Static Var Generator (SVG) can also contribute to the increase of transient overvoltage. Therefore, this article first establishes a photovoltaic power generation model, SVG model, and photovoltaic grid-connected system. Secondly, the transient overvoltage coupling mechanism of photovoltaic grid-connected systems under various faults such as commutation failure, DC blocking, and DC restart was analyzed through PSCAD simulation. At the same time, the changes in transient overvoltage before and after the installation of SVG were compared, and SVG’s reactive power dynamic behavior was analyzed. Finally, the mechanism of SVG assisting in increasing transient overvoltage was summarized.

Energy savings by adapting consumer behavior in grid-connected photovoltaic systems with battery storage

Today, the world faces many energy challenges that make the use of clean energy an obligation and an emergency. These challenges require changes in a wide range of sectors, including transport, industry and residential areas. At present, energy production is responsible for a large amount of greenhouse gas emissions, the main cause of climate change with its various dangerous effects on human life. Therefore, a change towards non-carbon energy is becoming a necessity. Certainly, this evolution has been underway for years and the renewable energy market has developed in a surprisingly efficient way. However, the current situation has reached an alarming state and requires the active participation of all stakeholders, especially consumers. Therefore, the main objective of this paper is to illustrate how consumer behavior can significantly influence and contribute to the optimization of renewable energy systems, especially photovoltaic systems. The paper emphasizes the beneficial integration of batteries and storage systems to achieve energy savings. The results show that with some adjustments in daily behavior, an overall energy saving of 62% can be achieved compared to the normal consumption scenario: the energy obtained from the grid and then the electricity bill is reduced.

Design of a perturb and observe and neural network algorithms-based maximum power point tracking for a grid-connected photovoltaic system

Integrating photovoltaic systems (PV) into the grid has garnered significant attention due to increasing interest in renewable energy sources. Maximizing the PV systems output power is crucial for improving energy efficiency and reducing operating costs. This paper presents a comparative analysis of two different techniques of maximum power point tracking (MPPT): perturb and observe (P&O) and artificial neural network (ANN) MPPT, focusing on their application in grid-connected PV systems. The paper evaluates their performance under various operating conditions, including changes in irradiance and temperature, that are discussed in three cases. The comparative analysis includes metrics such as voltage regulation and powerloss. MATLAB Simulink is utilized to implement P&O and ANN MPPT methods, which include a PV cell connected to an MPPT-controlled boost converter. The simulation demonstrates the power loss of the PV model as well as the voltage regulation in the three cases for the two methods. The results obtained in simulation and implementations show that the ANN method outperforms the P&O in the three cases discussed in terms of powerloss, voltage regulation, and efficiency. The results also show that the change in output power from PV is noticeable when compared to changes in radiation, while the change is slight when temperatures change.

Dual-objective optimal scheduling of grid-connected distributed photovoltaic systems considering life loss cost of energy storage

Dual-objective optimal scheduling of grid-connected distributed photovoltaic systems considering life loss cost of energy storage

Research on photovoltaic grid-connected inverters with source filtering function

Abstract To suppress the current harmonics caused by the nonlinear load connected to the grid when the photovoltaic system is connected to the grid for power generation, this paper adopts a composite control strategy based on the PI-RC controller to realize the synthesis of the command current signal in different modes. According to the different command current signals, the system can work in three modes: photovoltaic grid-connected harmonic compensation control, photovoltaic grid-connected control, and harmonic compensation unified control. Under the composite control strategy, the system realizes the active output of the photovoltaic grid-connected system with the function of source filtering, compensates for the harmonics caused by nonlinear loads, successfully reduces the harmonic distortion rate of grid current, and then realizes the function expansion of photovoltaic grid-connected inverter. Finally, the feasibility and rationality of the unified control strategy designed in this study are verified by simulation experiments.

Utilizing a Five-Level Inverter for Grid-Connected PV Systems: Implementing an MPPT Algorithm with Super Twisting Sliding Mode Control

Utilizing a Five-Level Inverter for Grid-Connected PV Systems: Implementing an MPPT Algorithm with Super Twisting Sliding Mode Control

Passivity-based adaptive current control loop of cascaded H-bridge multilevel converter for grid-tied photovoltaic system

This paper proposes an algorithm to passivity-based control (PBC) for the current control loop of the cascaded H-bridge converter (CHB) connecting the grid-connected PV system with the space vector modulation (SVM) algorithm using the law of expanding any number of voltage levels. The algorithm SVM in this paper is suitable for CHB converters. It can create a number of high levels of alternating voltage that other methods cannot do. Passivity-based control (PBC) algorithm aims to create a flexible controller that can change its structure or parameters to adapt to changes in the system, ensuring stable system quality, reducing switching losses and gaining energy conversion efficiency. It enhances the capability of accurately delivering power on demand to the grid with the CHB inverter in situations in which the grid demands the mobilization of maximum power from the photovoltaics (PV) system. The simulation results of the 9-level CHB system performed by MATLAB/Simulink software have proved the feasibility of the proposed algorithm.

A new adaptive Perturb & Observe controller via robust approach for PV grid-connected

Given the importance of renewable energies, several countries are integrating these energies into their energy strategies, such as their connections to the grid, in particular photovoltaic energy. In this paper, a PV grid-connected system in a two-stage configuration is studied. In this configuration, an improvement to a control MPPT is proposed. To achieve this, we propose a novel concept for MPPT control that consists of generating an adaptive step size for P&O control that is based on sliding mode control in order to minimise ripple phenomena and chattering phenomena. The proposed control approach was then tested using MATLAB/Simulink simulations under varying irradiation conditions. We compared this approach to the conventional P&O method as well as the adaptive step size of P&O and hybrid P&O-sliding mode control. The results of the test indicate that the proposed control strategy significantly minimises the two phenomena and improves overall efficiency.

PERFORMANCE EVALUATION OF A 67.2 KWP SI-POLY PHOTOVOLTAIC SYSTEM CONNECTED TO THE GRID USING PVSYST TOOL

This paper presents the simulated performance evaluation of a 67.2 kWp grid-connected Si-poly photovoltaic system. This study was carried out to assess the feasibility of installing a photovoltaic system to supply the electrical loads at the Alioune DIOP University of Bambey, Senegal, a public institution. Indeed, the UADB is facing load shedding problems caused by its exorbitant energy consumption. The study system comprises 240 Si-poly modules. Each module has a rated output of 280 Wp. All photovoltaic modules are connected in 12 strings, each string consisting of 20 modules in series. Three solar inverters, each rated at 20 kW, are used for interconnection with the grid via a public meter. For the case study, PVsyst 7.4 is used for simulation. PVsyst 7.4 is a PC software package for the design, dimensioning and data analysis of complete photovoltaic systems. Meteonorm 8.1 meteorological data sets on solar radiation, ambient temperature, wind speed and humidity from the PVsyst database were used for this analysis. In this study, the parameters evaluated were the photovoltaic arrays effective energy production, the energy injected into the grid, the performance rate and the normalized energy production per kWp installed. The 67.2 kWp photovoltaic system generates 111.287 MWh/year of DC energy, of which 108.980 MWh/year of AC energy is fed into the grid. The systems annual performance rate is around 81.5%, and the loss diagram over the whole year is also computed.

An Optimized 3DOF-FOPID2-DF Controller with Improved Power Quality for Multi-Level Inverter in Grid-Connected PV Systems

Multi-level Inverters (MLIs) are increasingly employed in grid-connected Photovoltaic (PV) systems to optimize power quality. The use of MLIs offers several benefits over traditional inverters, including improved output waveform quality, reduced Total Harmonic Distortion (THD) and increased efficiency. However, the control of MLI is complex, and a precise control system is required to ensure that the system operates at optimal conditions. Therefore, an Adaptive Chaotic Mapping Philippine Eagle Optimization (ACM-PEO) algorithm-based Three degrees of freedom Fractional Order Proportional Integral Derivative with Dual Filter (3DOF-FOPID2-DF) controller is proposed. The dual derivative and dual filter component introduced in the 3DOF-FOPID controller improve the transient response of the system even under sudden load changes. The ACM-PEO algorithm further enhances the system performance of the 3DOF-FOPID2-DF controller by optimizing the parameter gains. The proposed ACM-PEO-based 3DOF-FOPID2-DF control technique is simulated in MATLAB and the simulation results demonstrate that it has improved transient and dynamic response, reduced THD, faster convergence and better tracking of the reference signal. These benefits enhance the efficiency and reliability of the systems.

A hybrid cyber–physical risk identification method for grid-connected photovoltaic systems

Identifying risks in modern electric power systems is essential, and one of the main difficulties concerns covering the wide range of technologies that permeate its cyber and physical domains. Different risk identification methods have been proposed, but applying them individually does not guarantee coverage of both domains. On the other hand, the simple non-articulated application of a set of existing risk identification methods can lead to an exhaustive and inefficient process. This paper proposes a new Cyber–Physical Risks Identification Method (CPRIM) to comprehensively and efficiently identify risks in electrical power systems. To systematically cover risks ranging from the cyber domain to the physical domain, CPRIM combines in a complimentary and articulated way the National Institute of Standards and Technology (NIST) Cybersecurity Framework, a Risk Factor model, and the HAZOP, establishing a novel hybrid risk identification approach. This work also proposes a method based on Jaccard and overlap indexes to quantitatively assess the complementarity and superposition that may exist when applying different risk identification methods to electrical power systems. The results obtained in a real computer-managed photovoltaic plant indicate that CPRIM can efficiently identify cyber–physical risks, showing a reasonable trade-off between system coverage and redundancy in identified risks.

Power Factor Analysis of Grid-Connected Solar Inverter under Different Irradiance Levels throughout the Day

The power factor (PF) plays a crucial role in determining the quality of energy produced by grid-connected photovoltaic (PV) systems. When irradiation levels are high, typically during peak sunlight hours, the PV panels generate more electricity. In this scenario, the PF tends to be higher because the real power output closely matches the apparent power drawn from the grid. Whereas, when irradiation levels are low, such as during cloudy weather or nighttime, the PV panels produce less electricity. In these conditions, the power factor may decrease because the real power output diminishes compared to the apparent power drawn from the grid. This could be due to reduced efficiency or increased reactive power flow. PF decreases linearly at solar irradiance values lower than 220 (W/m2). At the same time, it approaches unity at higher solar irradiance values than 220 (W/m2). In this study, the variation of the power coefficient of the grid-connected PV solar system depending on solar irradiation was modeled and analyzed using MATLAB/Simulink 41016490. The analytical expression of the power factor depending on the change in solar irradiation was found using the curve fitting method.