Grid-connected Photovoltaic System Research Articles

The Technical and Economic Assessment of a Solar Rooftop Grid-Connected Photovoltaic System for a Dairy Farm

This study covers the technical and economic analysis of a grid-connected rooftop 216 kWp photovoltaic (PV) system to meet the average annual energy demand of 45,327 kWh of a dairy farm. According to the results of the analysis, an average of 326,819 kWh/year is received at the output of the panel arrays, while 294,838 kWh/year is supplied to the grid. The amount of energy to be received from the grid was found to be 25,013 kWh/year, and the performance ratio of the system was 0.808. The net present value (NPV) of the system was calculated as $36,463.39, and the levelized cost of electricity (LCOE) was calculated as 0.065 $/kWh. Considering that the annual electricity generation amount of the rooftop PV system evaluated within the scope of the study is 315,152 kWh, it is estimated that 127.419 tCO2 per year of emission release will be prevented annually with the installation of the system.

Day Ahead Forecasting of Photovoltaic Power Output in Maiduguri Using Feedforward Neural Network

With the increasing use of large scale grid-connected PV system, accurate forecasting approach for the power output PV system has become an important issue. Power planning is necessary for cost efficiency of power generation in which power forecasting is an essential part. For the Feed Forward Neural Network (FFNN) models, pre-processing techniques was used on the relevant variables. In this research work, FFNN based model was designed to forecast the next day PV power output of Maiduguri depending on available variables (Time of the day, day of the week, temperature, relative humidity, wind speed, cloud cover and PV power output) for the model. The FFNN based models were designed in the MATLAB® (R2015a) environment. Hence based on the results obtained from this research, it can be concluded that the FFNN based model performance is satisfactory for predicting next day PV power output. From the mean absolute percentage error (MAPE) value of 8.9093 on the test data set, it can be concluded that the Model had a better MAPE value which is an indication that the system performance is better than ANFIS which had 15.0048 as MAPE.

Optimization design of single phase inverter connected to the grid

In grid-connected photovoltaic systems significant improvements can be carried out in the design and implementation of inverters: reduction of harmonic distortion, elimination of the DC component injected into the grid and the proposed control. This paper proposes a control strategy based on PWM switching patterns for an inverter for photovoltaic system connected to the grid in order to control the injected current. The current injected must be sinusoidal with reduced harmonic distortion. An additional filter is designed to reduce high-order harmonics on the output side. This strategy exhibits the advantages: Simplicity, reduction of harmonics, size of the line filter, reduction of the memory requirements and power calculation for the control.

A hybrid control topology for cascaded H-bridge multilevel inverter to improve the power quality of smart grid connected system: NBO-RERNN approach

This manuscript proposes a hybrid control technique for the grid-connected photovoltaic (PV) generation system with a cascaded multilevel inverter (CMLI). The proposed hybrid approach integrates the Namib beetle optimization (NBO) and recalling-enhanced recurrent neural network (RERNN) approach, known as the NBO-RERNN technique. The cascaded multilevel inverter (MLI) is intended to increase the optimum control signal using the proposed controller. The cascaded multilevel inverter (MLI) is designed by using the minimum count of switches. The key purpose of the proposed approach is to improve power regulation or maximal solar energy conversion system (SECS) and to achieve good power quality (PQ) of the system. The proposed NBO technique generates the optimum control signal dataset offline. Based on the fulfilled dataset, the RERNN generates the best CMLI control signals inonlinemanner. The resultant control signals are used to regulate cascaded multilevel inverter insulated gate bi-polar switches (IGBT). For this purpose, the proposed NBO-RERNN control topology defines converter switching states by modeling generation system operating modes. The parameter variations of the system and external disturbances are mitigated, and the load demands are fulfilled optimally by using this control technique. The NBO-RERNN control topology is implemented in MATLAB, and its performance is compared with existing approaches. The simulation reveals that the proposed method has lower THD than the existing one.

An Efficient Grid Connected Photovoltaic System Based on H6 Transformer-less Inverter

Grid-connected photovoltaic (PV) systems based on transformer-less inverters have been widely used. Various topologies of transformer-less inverters are presented. The requirements of minimizing leakage current through transformer-less inverters and maximizing the power generated from PV arrays are of great importance. This paper presents a complete system that can effectively minimize the leakage current to safe values. In addition, operation at maximum PV array power point is realized. The system is based on modified H6 transformer-less inverter to minimize the leakage current. Perturbation and Observation P&O maximum power point tracker in addition to a dc-dc boost converter are utilized to achieve the second target. Analysis of each part and system controllers in the proposed system are presented in detail. Losses and system efficiency are introduced. Effects of sun irradiance variations are included. Simulation results prove the effectiveness of the proposed system.

A model-based power switch fault diagnosis strategy for cascaded H-Bridge converter

Cascaded H-Bridge (CHB) converter is a promising solution in medium-voltage high-power application, such as railway traction, grid-connected photovoltaic (PV) system. However, the large number of power switches involved in the topology is a potential reliability risk since the power switch is the most vulnerable component in a power electronic system. To achieve a fast and accurate power switch fault diagnosis (FD) in CHB, this paper proposes a model-based hierarchical FD strategy, consisting of phase-level and cell-level fault detection. There is no need of extra sensors or high computational burden. The effectiveness of the method is verified by simulation and experiment.

Performance Assessment of Four Grid-Connected Solar Photovoltaic Technologies under Similar Environmental Conditions in Nigeria

This research presents the performance of four grid-connected solar photovoltaic (PV) systems installed at the Adamawa State College of Health Technology, Mubi, Adamawa, Nigeria. The system consists of 3.5 kWp each of four different PV technologies. The mis and psi cells manufactured by China Sunergy and Conergy comprised 14 modules with a power capacity of 250 watts. The asi produced by Moser Baer consists of 10 modules, each having 350 watts, and the CIS manufactured by Solar Frontier has 20 modules, each having 175 watts. The PV was observed and recorded data from January 2022 to December 2022. The system performance was analyzed using eleven evaluation criteria. Based on the technical assessment of the systems for this location, the asi solar PV has the best annual performance. It demonstrated the most favorable performance ratio (PR) and capacity factor (CF) of 90.7% and 27.6%, respectively, when considering energy output, system yields, performance ratio, and capacity ratio. On the other hand, the msi system, manufactured by China Sunergy, exhibited significant efficiencies at both the panel and system levels, with values of 14.3% and 13.7%, respectively. This system stood out for its minimal overall losses, recorded at 7.78 hours, demonstrating superior efficiency and lower overall losses than the other systems under study. This study is critical because the result will provide valuable information to researchers in identifying the appropriate PV systems suitable for Nigeria. In addition, it will assist PV installers and manufacturers with updated information on the PV system with the best performance.

Fuzzy logic controller-based enhancement for grid-connected large-scale PV systems

: In recent years, considerable scholarly attention has been directed toward photovoltaic (PV) power generation, driven by the imperative to ameliorate the ecological ramifications inherent in conventional electricity generators. The economic viability of PV generation is intrinsically contingent upon both the cost-efficiency of the cells employed and the quantum of energy that the arrays have the capacity to deliver over their operational lifespan. This scholarly exposition elucidates an exploratory inquiry into the intricate realm of modeling and simulating solar PV systems, conjoined with the distribution grid via the employment of a Fuzzy Logic Controller (FLC). The inquiry delves into the nuanced realm of the influence wielded by fluctuations in solar radiation upon the system's operational efficacy, concurrently proffering a novel energy storage framework engineered to counterbalance the power quality predicaments stemming from the intermittent nature of solar energy. The empirical manifestations gleaned from simulations evince the system's conceptual prowess, effectuating an amelioration in the comprehensive operational dexterity of the solar PV system, thereby embellishing energy capture efficiency and mollifying the perturbing power quality predicaments. A comparative investigation, encompassing varied conditions, substantiates the prowess of the proposed methodology vis-à-vis the fixed perturbation Maximum Power Point Tracking (MPPT) algorithm. Empirical observations emanating from these investigations conspicuously affirm the feasibility and distinct advantages underpinning the purview of the proposed methodology.

Fuzzy Logic Control of an Automatic Changeover for the Management of a Grid-Connected Photovoltaic System

The main objective is to design a smart switch controlled using fuzzy logic which is able to take decisions on different sources of energy in a very brief “switching time” to avoid load shedding. For that, a fuzzy controller is implemented by considering input signals from three power sources: photovoltaic array, batteries, and grid. The inputs are irradiance being the instantaneous measurement of solar energy, voltage range for the power grid, and state of charge for the battery to be able to know if the battery can supply or not. For these corresponding inputs, Takagi’s fuzzy inference system was applied, that permitted to have linear constants as outputs. as such as to be able to control the switch with respect to the source available at a particular period of the day, either day or night. Designing the fuzzy logic controller was able to take decisions with respect to the conditions. The load feeding signal behaviour has been obtained when either of the switches are open or closed over a period of 1000 s. In this system, the batteries charge is either done by the PV array or the power grid according to the established rules of the system.

Performances Analysis of Three Grid-Tied Large-Scale Solar PV Plants in Varied Climatic Conditions: A Case Study in Algeria

Currently, for the determination of the suitable and optimal PV power plant according to the climate conditions of the concerned region, researchers focus on the estimation of certain performance factors, which are reported to be the key parameters for the analysis of the performances of grid-connected photovoltaic (PV) power systems. In this context, this paper focuses on on-site real-time analysis of the performance of three solar photovoltaic plants: Sidi-bel-Abbés (12 MWp), Laghouat (60 MWp), and Ghardaïa (1.1 MWp). These plants are located in different regions experiencing diverse climatic conditions in Algeria. The analysis was carried out by the standardized norms of IEC 61724, using monitoring data collected over one year. The photovoltaic power plants were evaluated in terms of performance factors, such as the reference yield (Yr), final yield (Yf), performance ratio (PR), and capacity factor (CF). On the other side, based on real data collected at the concerned sites, two linear functions depending on solar irradiance and the PV module temperature for each site are proposed for the evaluation of the generated alternative power output (PAC) for the three PV plants. The obtained results based on the study presented in this paper can help designers of PV power plants of different technologies and different climate conditions to precisely decide the convenient technology that allows the best production of the electrical energy for grid-tied PV systems. Furthermore, this study can contribute in giving a clear vision of the implementation of upcoming large-scale solar PV power plants in Algeria within the studied area and other areas.

Enhancing self-consumption for decarbonization: An optimization strategy based on a calibrated building energy model

To face the challenge of climate change and achieve the decarbonization target set by the European Union, the current trend is to electrify building services, replacing the use of fossil fuels for renewable energy sources. The installation of grid-connected photovoltaic (PV) systems is becoming a popular strategy. However, the widespread application of PV solutions carries certain concerns about grid-network security and stability, since intermittent renewable energy excess pouring into the grid may exceed voltage limits. Therefore, an optimization of the consumption of a building's own PV production (self-consumption) to reduce the excess output is vital. The following paper performs a demand side optimization strategy of the building's thermostatic controllable loads (heating and cooling), which represent at least 50% of the total energy consumed by the building. The process is applied in a previously calibrated building energy model (BEM) that describes a fully operational building under a typical Mediterranean climate (Greece). The site contains a PV plant and a multi-split Variable Refrigerant Flow (VRF) system dedicated to maintain indoor comfort conditions. The technology used is simple, able to perform 15 minute time-step yearly optimizations while saving a large amount of computational time. It performs a bi-dimensional optimization of both: indoor thermal-zone set-points and ventilation air supply temperature. The optimization process performed is based on 2019 data gathered from European Project SABINA, resulting in a self-consumption improvement of 11.6% for summer scenario (reaching 69.16%) and 78.7% for winter (reaching 57.47%) in comparison to a non-optimized “business as usual” base model.

Smart Sag Detection and Reactive Current Injection Control for a PV Microgrid under Voltage Faults

Low-power photovoltaic distributed generators are increasing rapidly in number by the grids of the modern era, but they also bring up the concern of grid stability. To maintain grid stability, it is essential for the network operators to update the grid codes at regular intervals due to the case of highly penetrated grid-connected photovoltaic systems (GCPVs). Integration of large-scale electrical grids with renewable energy sources, one being photovoltaic systems, faces the challenge of riding through low-voltage (LVRT) phases. As compared to the previous grid codes for power generation, recent advances require distributed generation resources to provide for such capabilities under grid faults. This work contributes to the ongoing investigation of this specified and destabilizing fault condition. Various simulations of a PV microgrid system are carried out with the ability to ride through low-voltage faults, with the help of a DC-chopper circuit to absorb DC-link over-voltage, and the current is also maintained within acceptable limits according to the required standards. The fundamental contribution of this research is a proposed neural network (NN) control framework. This framework effectively detects voltage sags, comprehends their characteristics, and provides support to the system by injecting reactive current in accordance with the demands imposed by designated grid codes. This NN control model has been systematically developed utilizing data gathered from a vast array of testing simulations conducted under different and dynamic conditions. The proposed strategy, on the other hand, is compared with the RMS fault detection method in combination with the conventional LVRT algorithm. The NN control approach showed better results as compared to the conventional methods in terms of accuracy and robustness, especially when confronted with difficult sag situations.

Design of a 15-Level Non-Modular Multilevel-Inverter in a Grid-Connected Solar PV System: A Hybrid ZOA-SNN Technique

In this paper, a hybrid strategy is proposed for an innovative 15-level inverter method for the grid-connected photovoltaic (PV) system. The proposed technique is combined with the Zebra Optimization Algorithm (ZOA) plus the spiking neural network (SNN) and is called the ZOASNN method. The major goals of the ZOASNN approach are to fulfill the power demand of load, lessen harmonics, and improve the PV system power regulation or maximal energy conversion. The multilevel inverter (MLI) is used to operate at symmetrical and asymmetrical configurations aimed at utilizing reduced power components. The proposed ZOASNN controller develops the operating modes of two-generation methods to determine the converter switching states for this purpose. Using this control method, load demands are optimally fulfilled while external disturbances and fluctuations in system parameters are minimized. The proposed strategy is implemented in MATLAB and its performance is estimated with other algorithms, such as the Grasshopper Optimization Algorithm (GOA), the Random Forest Algorithm (RFA), and the Cuckoo Search Algorithm (CSA). The proposed method shows high efficiency, low total harmonics distortion (THD), and lower cost than other existing methods.

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters.

The Single-Stage Grid-Connected Solar Photovoltaic (SSGC-SPV) topology has recently gained significant attention, as it offers promising advantages in terms of reducing overall losses and installation costs. We provide a comprehensive overview of the system components, which include the photovoltaic generator, the inverter, the Incremental Conductance Maximum Power Point Tracking (IC-MPPT) algorithm, and the PI regulator for DC bus voltage control. Moreover, this study presents detailed system configurations and control schemes for two types of inverters: 2L-3PVSI and 3L-3PNPC. In order to perform a comparative study between the two structures, we subjected them to the same irradiation profile using the same grid configuration. The Photovoltaic Array (PVA) irradiance is increased instantaneously, in 0.2 s, from 400 W/m2 to 800 W/m2, is kept at 800 W/m2 for 0.2 s, is then gradually decreased from 800 W/m2 to 200 W/m2 in 0.2 s, is then kept at 200 W/m2 for 0.2 s, and is then finally increased to 1000 W/m2 for 0.2 s. We explain the operational principles of these inverters and describe the various switching states involved in generating output voltages. To achieve effective control, we adopt the Finite Set-Model Predictive Control (FS-MPC) algorithm, due to the benefits of excellent dynamic responsiveness and precise current tracking abilities. This algorithm aims to minimise the cost function, while taking into account the dynamic behaviour of both the PV system and the inverter, including any associated delays. To evaluate the performance of the FS-MPC controller, we compare its application in the three-level inverter configuration with the two-level inverter setup. The DC bus voltage is maintained at 615 V using the PI controller. The objective is to achieve a Total Harmonic Distortion (THD) below 5%, with reference to the IEEE standards. The 2L-3PVSI inverter is above the threshold at an irradiance of 200 W/m2. The 3L-3PNPC inverter offers a great THD percentage, meaning improved quality of the power returned to the grid.

One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations

In recent years, photovoltaic (PV) energy production has witnessed overwhelming growth, which has inspired the search for more effective operations. Nevertheless, different PV faults may appear, which leads to various degradation stages. Furthermore, under different irradiance levels, these faults may be misclassified as a healthy mode owing to the high resemblances between them, thus provoking serious challenges in terms of power losses and maintenance costs. Hence, interposing the irradiance variation in grid-connected PV (GCPV) systems modeling is important for monitoring tasks to ensure the effective operation of these systems, to increase their reliability and to prevent false alarms. Therefore, in this paper, a fault detection and diagnosis (FDD) method for the GCPV systems using machine learning (ML) based on principal component analysis (PCA) is proposed in order to ensure the reliability and security of the whole system under irradiance variations. The proposed strategy consists of three main steps: (i) introduce the irradiance variations in PV system modeling because of its great impact on power production; (ii) feature extraction and selection through PCA; and (iii) fault classification using ML techniques. In this study, we generate a database that is used to compare the proposed strategy with the standard strategy (considering a fixed irradiance during FDD), to make, at first, a complete and significant comparative assessment of fault diagnosis and to demonstrate the efficiency of the proposed strategy. The achieved results show the high effectiveness of the proposed one-class classification-based approach to detect and diagnose PV array anomalies, reaching an accuracy up to 99.68%.

A Review of Multilevel Inverter Topologies for Grid-Connected Sustainable Solar Photovoltaic Systems

Solar energy is one of the most suggested sustainable energy sources due to its availability in nature, developments in power electronics, and global environmental concerns. A solar photovoltaic system is one example of a grid-connected application using multilevel inverters (MLIs). In grid-connected PV systems, the inverter’s design must be carefully considered to improve efficiency. The switched capacitor (SC) MLI is an appealing inverter over its alternatives for a variety of applications due to its inductor-less or transformer-less operation, enhanced voltage output, improved voltage regulation inside the capacitor itself, low cost, reduced circuit components, small size, and less electromagnetic interference. The reduced component counts are required to enhance efficiency, to increase power density, and to minimize device stress. This review presents a thorough analysis of MLIs and a classification of the existing MLI topologies, along with their merits and demerits. It also provides a detailed survey of reduced switch count multilevel inverter (RSC-MLI) topologies, including their designs, typical features, limitations, and criteria for selection. This paper also covers the survey of SC-MLI topologies with a qualitative assessment to aid in the direction of future research. Finally, this review will help engineers and researchers by providing a detailed look at the total number of power semiconductor switches, DC sources, passive elements, total standing voltage, reliability analysis, applications, challenges, and recommendations.

Improvement of power quality of a 200 kW grid-connected PV system

<span lang="EN-US">In order to enhance electricity quality, a grid-connected photovoltaic (PV) system simulation is presented in this study. A 200 kW photovoltaic system is integrated to a utility grid and loads. A 25 kV 3-level insulated gate bipolar transistor (IGBT) bridge converter is used. Through this system integration of a renewable energy source with a non-renewable source is achieved. This system also wins over the intermittent nature of the renewable source (solar energy) and non-reliability of conventional sources (utility grid). Power and signal quality for various signals such as ripple factor and total harmonic distortion has improved. The suggested system is investigated using MATLAB/Simulink environment. Total harmonic distortion (THD) in voltage and current, ripple factor correction is also incorporate in the MATLAB model.</span>

A Grid-Connected PV System Based on Quasi-Z-Source Inverter With Maximum Power Extraction

This paper proposes an approach to link photovoltaic arrays with the AC grid using Z-source inverter (ZSI) and quasi-Z-source inverter (QZSI) topologies. These topologies boost the DC-link voltage and invert it to AC voltage in one stage, resulting in a reduction in the overall system size and cost. The paper presents a control technique that fixes the DC-link voltage on the inverter bridge terminals, tracks the maximum power point (MPPT), and controls the amount and quality of power injected into the grid using unity power factor. The proposed control algorithm utilizes two independent control variables, shoot-through duty cycle (ST) and modulation index, to decouple the control of the DC and AC sides. The effectiveness of the proposed control technique is verified through simulations of a grid-connected PV system. The results demonstrate that the proposed system can efficiently extract maximum power from PV arrays while regulating the DC-link voltage and ensuring unity power factor operation. Additionally, experimental testing of ZSI/QZSI further confirms the results of analysis and simulation.

Research on Energy Management Strategy for GCPBS

Integrating solar power into the power grid is significant for the rapid acceleration of energy transition. As a result, it is urgent to make appropriate operational strategies for grid-connected photovoltaic battery systems (GCPBS). At present, a variety of energy management strategies have been proposed, while the influence of battery degradation and energy flow distribution hasn’t been considered, which also makes a huge difference to grid-connected photovoltaic-battery system operation. Therefore, a novel energy management strategy is proposed in this paper to provide optimal energy utilization. Moreover, a case study using data from California is utilized to investigate the performance of the proposed method, where a widely used operation strategy is utilized to be compared. Results prove that the proposed method can control flexibly energy flow to achieve the minimum cost of GCPBS operation with reasonable battery charging and discharging process, and the battery is charged at tough prices and discharged at high prices, where coupling effects of all hours and price are considered and the battery is fully utilized.

Performance Analysis and Comparison of a 93.6 kW Grid-Connected Rooftop Photovoltaic System in South Tangerang

Performance Analysis and Comparison of a 93.6 kW Grid-Connected Rooftop Photovoltaic System in South Tangerang