Grid-connected Solar Photovoltaic System Research Articles

Improve The Conversion Efficiency and Electrical Output of The Solar PV System by Developing and Implementing a Cooling System

The performance of the grid-connected solar photovoltaic (PV) system in the present study is improved using cooling technology. This technology reduces the temperature of solar PV modules and thus increases their efficiency and lifespan. The current solar PV system has been installed in northern Baghdad in the city of Taji. This study included improving electrical power, conversion efficiency, and performance ratio. All of these improvements are achieved by cooling using water. The performance ratio (PR) and efficiency maximum values of the reference (unimproved) and improved solar PV modules are at 10:10 am at 88.30% & 96.30% and 13.4% & 14.6%, respectively. Improved and reference PV modules maximum solar radiation values occur at 12:00 pm at 1.91 W and 1.566 W, respectively. The maximum solar radiation values of the reference and improved PV modules are at 12:00 pm at 922.9 W/m2 and 1012.22 W/m2, respectively. The maximum daily gain of the power and solar irradiance are 22% and 10%, respectively. The significant innovation in the study is the successful performance optimization of the grid-connected solar PV system. Using different equations to study performance by following different behaviors. Along with improving and studying CIGS PV module performance.

A Comparative Analysis of Various Simulation Software for grid-connected Residential Building: A Case Study at Jeddah, Saudi Arabia

Saudi Arabia receives significant energy daily from solar radiation, making it one of the best places to use solar energy to meet its energy needs. Grid connected Photovoltaic (PV) systems provide the electrical energy available from the solar radiation into the grid. Different simulation tools are currently available used to assess the performance of solar PV systems. The present study was conducted to design and evaluate the performance of 15 kW grid-connected solar PV system under climatic conditions of in Jeddah, Saudi Arabia. Two simulation models, Helioscope and PVsyst were used in the analysis and the results were then compared. The annual energy production, specific production, energy injected into grid, and annual performance ratio (PR) are evaluated by the software’s around a whole year at the same climatic conditions. Results showed that the maximum annual solar irradiation in May was 2212 kWh/m2. The PR based on PVsyst simulation (78.04 %) was higher than that of the Helioscope simulation (74.8%) by about 4.15%. The maximum energy production in May was about 6.1 kWh. The energy production from the PVsyst and Helioscope is 28.98 and 25.9 MWh/year with 289.8 and 259.13 MWh/year energy injected into the grid, respectively.

A novel dynamic/adaptive K-nearest neighbor model for the prediction of solar photovoltaic systems’ performance

The inherent variability in solar Photovoltaic (PV) systems motivates developing dynamic and adaptive data-driven models capable of accurately predicting the systems’ performance. A dynamic/adaptive K-Nearest Neighbor (K-NN) model to estimate the conversion efficiency of a solar PV system is proposed in this work. The proposed model estimates the conversion efficiency of a test pattern based on three steps: (i) identifying the similar historical operational conditions experienced by the system, (ii) assigning a weight to each condition based on its similarity to the test pattern at hand, and (iii) computing the weighted average conversion efficiency of the test pattern. Thus, the proposed model is adaptive and dynamic as the nearest neighbors and the associated weights vary from one test pattern to another. The proposed model is applied to a 7.98 kWp rooftop grid-connected solar PV system at the Hashemite University in Jordan. Its effectiveness is evaluated by resorting to the Mean Square Error (MSE), Accuracy, Coefficient of Determination (R2), and Adjusted R2 (R2adjusted), and comparing the results with the traditional K-NN and other techniques adequately developed in the same context, such as Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Multiple Linear Regression (MLR). Results show that the proposed model exhibited superior performance across all metrics, with a performance gain reaching up to 45.2 % and 6.8 % for the MSE and R2adjusted, respectively, compared to the base MLR. Further, the simplicity and computational efficiency of the proposed model validate its practical deployment in predicting PV system performance.

Sustainable solar energy development in Zakho, Iraq: a techno-economic and environmental assessment

This study presents a solar energy roadmap aimed at attracting investors to capitalize on the abundant solar resources in Zakho, Iraq, for clean energy technology. The objective is to mitigate global warming effects caused by fossil fuel combustion and promote sustainable technological development. To end this, the study employs RETScreen Expert software to validate the techno-economic and environmental viability of a grid-connected solar photovoltaic system utilizing climatic data from the Astronaut Office Information System (NASA) Database. Additionally, the Photovoltaic Geographical Information System (PVGIS) modeling method determines optimal angles for installing residential rooftop Photovoltaic (PV) systems across selected locations. Results indicate that all selected locations are suitable for solar photovoltaic projects, with Rezgari identified as the optimal district due to its highest annual solar radiation of 1863.117 kWh/m2 and the highest annual electricity production at 7905.38 kWh. The capacity factor (CF) values fall within the range of 17.395-17.574%. The study reveals an annual greenhouse gas (GHG) emissions reduction ranging from 6.9876 to 7.1123 (tCO2) across four districts. Considering the financial and environmental indicators, Rooftop Photovoltaic Systems emerge as a sustainable and efficient solution to enhance the environment and prove economically viable in Zakho City.

Optimum ground-mounted on-grid connected photovoltaic system

This research endeavors to enhance grid-connected solar photovoltaic systems by refining the methodology used to select suitable geographical photovoltaic sites. The prevalent criterion in existing literature for choosing sites emphasizes proximity to power transmission lines. However, this criterion overlooks essential grid requirements, such as approved bus bars and permissible sizes for new photovoltaic systems. To overcome this limitation, the study introduces a novel criterion. Instead of relying solely on proximity to transmission lines, the proposed approach replaces this with the concept of optimal geographical bus bar locations. Additionally, the generated findings can be fine-tuned through consideration of the specified system size of the grid, thereby improving the identification of geographically suitable sites more effectively.The proposed methodology lies in the incorporation of five distinct approaches. The primary approach employs the Artificial Bee Colony (ABC) algorithm to ascertain the most suitable bus bars and sizes for three Photovoltaic Distributed Generation (PV-DG), with a focus on minimizing power loss within Aden's electric grid. The second approach involves utilizing the Analytic Hierarchy Process-Weighted Linear Combination (AHP-WLC) method in conjunction with Geographic information systems (GIS) to accurately select appropriate PV sites. The third approach is an initial design to eliminate small geolocations that do not align with the PV system's size. Fourthly, the Least Costly Path Model (LCPM) is harnessed to choose a single geolocation. The final approach is dedicated to performing techno-economic analysis.The key findings reveal that the optimal bus bars are located at 30, 46, and 64 in the grid, with a total injected active power of 26 MW. Simultaneously, the identified optimal geolocations cover areas of 0.88, 0.48, and 1.37 km^2 for each bus bar, respectively. In the comparative analysis, the utilization of the proposed criterion involving optimal bus bars results in a larger geographical coverage (97.99 km2) compared to the traditional criterion (76.28 km2). Furthermore, the techno-economic evaluation indicates the viability of implementing PV systems in Aden.The authors have a strong conviction that the framework and outcomes of this study will garner significant attention and prove valuable in assisting decision-makers to improve the planning of grid-connected solar photovoltaic systems.

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

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

Analysis of the efficiency of 300 kw grid-connected solar photovoltaic systems at Bukhara state university

Uzbekistan has a great potential for solar radiation in its vast regions, averaging from 5.9 kWh/m2. In order to increase the contribution of solar energy to the national grid, it is extremely important to evaluate the performance of existing solar photovoltaic systems and further assistance in the design and installation of new power plants. This study presents an analysis of the technical characteristics of a 300 kW solar photovoltaic system at Bukhara State University, which was monitored during the year from February to October 2023. An economic analysis of the solar photovoltaic system is carried out. In the study period of 2023, this solar power plant produced 486.8 MWh. According to the economic analysis, the cost of energy (LCOE) is 0.099 kW∙h and the payback period is 9.7 years, respectively. The study shows that the performance of the Bukhara State University system is similar to the characteristics of other solar power plants around the world

Analysis of the efficiency of a 300kw solar photovoltaic system in the climate of uzbekistan

In this research work, the efficiency of a 300 kW grid-connected solar photovoltaic system was analyzed over a year under the climatic conditions of different regions of Uzbekistan. It has been established that the maximum power value that a 300 kW solar photovoltaic installation can produce per year is 0.477 MW, and the minimum is 0.452 MW. Taking into account regional climatic conditions, it was determined that the shortest payback period for photovoltaic systems from generated electricity is 8.6 years, and the shortest is 9 years. It is estimated that between 90.4 and 95.5 tons of CO2 gases could be prevented in the region by 300 kW solar photovoltaic systems.

Smart Grid Resilience for Grid-Connected PV and Protection Systems under Cyber Threats

In recent years, the integration of Distributed Energy Resources (DERs) and communication networks has presented significant challenges to power system control and protection, primarily as a result of the emergence of smart grids and cyber threats. As the use of grid-connected solar Photovoltaic (PV) systems continues to increase with the use of intelligent PV inverters, the susceptibility of these systems to cyber attacks and their potential impact on grid stability emerges as a critical concern based on the inverter control models. This study explores the cyber-threat consequences of selectively targeting the components of PV systems, with a special focus on the inverter and Overcurrent Protection Relay (OCR). This research also evaluates the interconnectedness between these two components under different cyber-attack scenarios. A three-phase radial Electromagnetic Transients Program (EMTP) is employed for grid modeling and transient analysis under different cyber attacks. The findings of our analysis highlight the complex relationship between vulnerabilities in inverters and relays, emphasizing the consequential consequences of affecting one of the components on the other. In addition, this work aims to evaluate the impact of cyber attacks on the overall performance and stability of grid-connected PV systems. For example, in the attack on the PV inverters, the OCR failed to identify and eliminate the fault during a pulse signal attack with a short duration of 0.1 s. This resulted in considerable harmonic distortion and substantial power losses as a result of the protection system’s failure to recognize and respond to the irregular attack signal. Our study provides significant contributions to the understanding of cybersecurity in grid-connected solar PV systems. It highlights the importance of implementing improved protective measures and resilience techniques in response to the changing energy environment towards smart grids.

Performance evaluation of three grid-connected monocrystalline silicon solar photovoltaic systems in Nepal

This study evaluates three grid-connected solar photovoltaic (PV) systems using four criteria: final yield, performance ratio, capacity utilization factor, and system efficiency. The PV systems were installed on the rooftop of the Nepal Telecom office at Sundhara, Pokhara, and Biratnagar. The generation data was collected through the web interface of the data logger installed at the sites. Long-term comparison (four years) of the plant at Sundhara and the same system comparison of plants at Pokhara and Biratnagar were performed. Long-term analysis of the plant at Sundhara found that the final yield decreased from 2.91 kWh/kWp in 2019 to 2.21 kWh/kWp in 2022. Similarly, the performance ratio decreased from 91.0% in 2019 to 67.3% in 2022, capacity utilization factor decreased from 12.1% in 2019 to 9.2% in 2022, and system efficiency decreased from 16.8% in 2019 to 12.5% in 2022. The same system installed at Pokhara and Biratnagar had slightly varied performance owing to the variation in meteorological parameters. The average air temperature at Biratnagar (26.7°C) was higher than at Pokhara (13.1°C) while the amount of solar radiation at Biratnagar was just higher by 4%. This led to the reduction of final yield by 9% at Biratnagar than at Pokhara as higher temperatures at Biratnagar caused more reduction in the power output from the solar panels. Finally, this study provides insights to researchers, PV installers, and panel manufacturers on the actual performance of grid-connected solar PV in Nepal.

Optimal peak-shaving for dynamic demand response in smart Malaysian commercial buildings utilizing an efficient PV-BES system

Reducing peak demand on the utility grid benefits both grid operators and consumers. However, achieving this goal while maintaining human comfort presents a significant challenge. This study proposes the deployment of an energy-efficient grid-connected solar photovoltaic (PV) and battery energy storage (BES) system to perform peak shaving. We employ an optimal rule-based control strategy that considers day-ahead PV power and load demand predictions, along with a daily load profile limit, specifically designed for the C1 tariff in Malaysian Commercial Buildings. The peak shaving control strategy proactively determines optimal schedules for battery charging and discharging, aiming to effectively minimize peak demand. To regulate the daily demand accordingly, we employ a root-finding algorithm to determine the optimal demand limit in conjunction with the formulated rule-based peak-shaving control strategy. The required optimal inputs for the rule-based control strategy are determined using a genetic algorithm, which minimizes peak grid power. We verify the effectiveness of the rule-based peak-shaving method using MATLAB. The results demonstrate significant reductions in both energy consumption and peak demand for different load profiles. Specifically, we achieve a reduction of 19.02%, 20.9%, 4.72%, and 0% in energy consumption, and 42.46%, 49.82%, 40.44%, and 33.1% in peak demand for case studies (CS) 1, 2, 3, and 4, respectively. Furthermore, we observe that the application of the rule-based peak shaving method maintains a state-of-charge (SOC) of the battery at 50% by the end of the day, allowing for day-to-day management flexibility.

Techno-Economic Feasibility Study of a 1.5 MW Grid-Connected Solar Power Plant in Bangladesh

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degradation, limited fossil fuel reserves, and volatile product costs, renewable energy sources are gaining momentum globally. Our research focuses on a grid-connected solar PV system model at Char Jazira, Lalpur, Natore, Rajshahi, Bangladesh. Through PVsyst 7.1 simulation software, we assess the performance ratio (PR) and system losses, revealing an annual solar energy potential of 3375 MWh at standard test condition (STC) efficiency. After considering losses, the system generates 2815.2 MWh annually, with 2774 MWh exported to the grid. We analyze an average PR of 78.63% and calculate a levelized cost of energy (LCOE) of 2.82 BDT/kWh [1 USD = 110 BDT]. The financial assessment indicates a cost-effective LCOE for the grid-connected PV system, with an annual gross income of 27,744 kBDT from selling energy to the grid and operating costs of 64,060.60 BDT/year. Remarkably, this initiative can prevent 37,647.82 tCO2 emissions over the project’s 25-year lifespan.

Solar insolation and PV module temperature impact on the actual grid connected solar photovoltaic system in German Malaysian Institute

German Malaysian Institute (GMi) has a great potential for utilising solar photovoltaic (PV) electricity. In this study, the performance of a 3kW grid-connected solar PV (GCSPV) system is monitored using Sunny Portal web in conjunction with Sunny SensorBox. The daily solar energy that strikes a PV array cannot be totally transformed into grid-compatible electricity. As a result of power losses, the production of solar PV systems falls short of expectations. The primary objective of this study is to examine the influence of solar insolation and PV module temperature on the actual output of the GCSPV system compared to the predicted output system by considering elements that affect the conversion of solar power to electrical power in GCSPV systems. The study prediction method based on calculations was carried out. Prediction of output system considered all losses, including efficiency of PV module, solar insolation, temperature of PV module, dirt, manufacturer’s tolerance and module mismatch, the voltage drop in DC cables to inverter, and inverter efficiency. The data indicate that the average power generation is 7.26% lower than predicted by the system. The GCSPV system’s performance efficiency is diminished by the temperature increase and somewhat enhanced by an increase in solar insolation. Temperature has a crucial impact in the energy conversion process of PV module. The effectiveness of the PV module in generating energy and power is dependent on its operating temperature.

Machine learning based cascaded ANN MPPT controller for erratic PV shading circumstances

Power generation is challenged to meet energy demand during peak hours. As a result of limited non-renewable energy resources, power utilities heavily rely on fossil fuels. Therefore, scientists and researchers are looking for some distributed generators to provide additional power during peak hours. During such period, load demand is solved using solar power. As a consequence, grid-connected solar Photovoltaic (PV) systems are catching the attention owing to their ability to significantly reduce the use of fossil fuels. Under Partial Shading Condition (PSC), this paper utilizes Luo Converter along with Cascaded Artificial Neural Network (ANN), which is a Machine Learning Based Maximum Power Point Tracking (ML-MPPT) approach for tracking optimal power from PV system. The gained DC supply is converted into AC voltage using VSI attached to the system. In addition, PI controller engaged controls the voltage at grid side and results in effective grid synchronization. Furthermore, MATLAB Simulink analysis is carried out and the outcomes reveal the effectiveness of proposed system with 98% efficiency under different PV circumstances.

A robust variable-λ least logarithmic hyperbolic cosine function adaptive filtering-based control algorithm for grid-connected solar PV system

This paper implements a robust variable-λ least logarithmic hyperbolic cosine function adaptive filtering (VLlncosh)-based control algorithm for a grid-connected solar photovoltaic system (GCPVS) which includes DSTATCOM features while integrating PV power to the grid at enhanced power quality (PQ). In this paper, a single-stage topology of GCPVS is considered, which can reduce the complexity, cost, and maintenance associated with DC-DC converter employed in two-stage topology. In GCPVS, the nonlinear loads, which deteriorate the quality of the power, are attached to the point of common coupling (PCC), and maximum extracted PV power is allocated between these loads and the grid. The power transfer at the grid takes place at unity power factor (UPF). The proposed VLlncosh algorithm estimates the fundamental weight components of the distorted load current for grid reference currents generation with a variable learning parameter to ensure fast initial convergence and more steady-state accuracy. The control algorithm operates GCPVS such that it provides compensation to the load current harmonics, unbalanced load current, and load reactive power while complying with IEEE-519–2022 standard. The VLlncosh-based control algorithm is implemented for GCPVS modeled in MATLAB/Simulink to evaluate its performance through simulation results. Further, the control algorithm is validated using the experimental prototype setup of GCPVS in the laboratory. The comparative assessment of the VLlncosh algorithm is provided to confirm its superior performance over LMS, sign-error LMS, and LMF algorithms.

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.

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.

Fault analysis in grid-connected solar photovoltaic systems based on multi-objective grey wolf optimisation

The renewable energy resources depend on environmental concerns. Photovoltaic (PV) systems are an interesting form of renewable energy resource. The two main problems associated with PV systems are nonuniformity of irradiation and temperature change. PV achieving maximum power point tracking are dependent on power electronics converters. Emissions are a key challenge for the future of conventional renewable energy resources, which play an important role in the generation system. It is crucial in applications and industry because the primary goal of optimization is to discover specific research. We offer using the population of search agents based on fitness, the multi-objective grey wolf optimizer, a more modern meta-heuristic based on the hunting habits and social structure of grey wolves, for optimizing adaptive controllers for failures in grid analysis and power system integration various faults in the grid cause several power quality issues. Harmonics, voltage sag and swell imbalances, and many power conditioning topologies. This was carried out using MATLAB/Simulink, and the results showed a superior and flexible performance in reducing voltage sag and voltage swell problems and imperfections at various loading situations.

Performance Evaluation of Burkina Faso’s 33 MW Largest Grid-Connected PV Power Plant

This study conducted an in-depth analysis of the performance of the largest Grid-Connected Solar Photovoltaic System in Burkina Faso from 2019 to 2021. The research utilized measured data and simulated the plant’s performance using the PVGIS database. The results revealed that the months with high solar radiation were the most energy-productive, indicating a direct correlation between solar irradiance and energy generation. During the rainy season (July and August), the PV plant exhibited the highest conversion efficiency. Conversely, the hot season (March and April) was associated with the lowest conversion efficiencies, with module temperatures reaching approximately 47 °C. Efficiency decreased from 12.29% in 2019 to 12.10% in 2021. The system’s performance ratio ranged from 80.73% in 2019 to 79.36% in 2021, while the capacity factor varied from 19.89% in 2019 to 19.33% in 2021. The final yield, measured in hours per day, was 4.89 h/d in 2019, 4.61 h/d in 2020, and 4.92 h/d in 2021. These findings highlight the deterioration in the performance of the Zagtouli PV plant over time. The study emphasizes the utility of using PVGIS-SARAH2 to forecast solar radiation and estimate energy output in PV systems. A semi-automatic cleaning system is used to clean the modules. This cleaning mechanism is inefficient because it is inconsistent. To increase the PV plant’s effectiveness, improved cleaning systems with more advanced mechanisms are required. This research, the first of its kind on the largest PV power plant connected to Burkina Faso’s national grid, serves as a valuable model for other power plants currently under construction or in the planning stages.

Investigation of Transient Stability in Power System with Improved FRT Capable Solar PV Inverters

Most regions of the world have seen tremendous growth in distributed energy resources, especially photovoltaic (PV) generation. The penetration of PV generation into the power grid is high and hence it has a major impact in power system. The integration of solar PV plant into the power system has both negative as well as positive impacts on transient stability of the power system during faults. In this work, the proposed current amplitude limiting based Fault Ride Through (FRT) control algorithm is implemented in large scale power systems and enhancement of transient stability is also studied in terms of synchronous machine side and grid side parameters namely rotor speed deviation, load angle, relative rotor angle, active and reactive power. The transient stability of large-scale Grid Connected solar PV (GCPV) system is discussed in this article, when the system is incorporated with the proposed FRT control strategy. The large scale GCPV system is said to be stable, if the angular displacement between the machines in the system stays within predetermined limits. The parameters considered for transient analysis on the synchronous machine side are the rotor angle, rotor speed, and Critical Clearing Time. The proposed model is simulated in MATLAB Simulink and validated in 1 kW hardware setup of grid connected PV system which performs satisfactorily in terms of system parameters and transient stability improvement.