Performance Of Proposed System Research Articles

Performance analysis of voltage profile improvement in AVR system using zebra optimization algorithms based on PID controller

Many problems are associated with dynamic changes in the power system, mainly because the number of switching loads has increased. These loads induce ineffective system behaviour, reduce the system voltage profile, and lead to improper voltage regulation. The proposed Automatic Voltage Regulator (AVR) system is intended to eliminate the above problem and preserve the system voltage at the generator terminal within the allowable range. In this work, Proportional-Integral-Derivative (PID) controllers are applied to enhance the dynamic behaviour of the AVR system. The Zebra Optimization Algorithm (ZOA) and Osprey Optimization Algorithm (OOA) algorithms are implemented to optimize the system's transient response by modifying the gain of PID controller parameters, which include proportional, integral, and derivative parameters, to preserve a stable voltage profile. To achieve an optimal solution, the mathematical model of the proposed system is realized in the Internet of Things (IoT). The connected loads in the system can operate at the nominal voltage level through PID controller tuning using ZOA and OOA. The proposed system's performance is evaluated and verified with MATLAB R2023a. ZOA showed an improvement of 18.5 % with a peak overshoot of 0.01 % during the system-transient condition. Compared to conventional optimization algorithms implemented in AVR systems, ZOA provides faster responsiveness and enhanced system stability.

Performance investigation on an agricultural photovoltaic thermal system based on spectral separation

Based on cascade utilization of full spectrum solar energy, a spectral splitting agriculture photovoltaic thermal hybrid system has been proposed in this study. The red and blue light is assigned to farmland for plant photosynthesis, while the photovoltaic active light is absorbed by solar cell for electricity generation, and the rest to thermal receiver for high temperature production. To maximize the power output of proposed system, redundant waste heat is recovered through ejector and converted into electricity in organic Rankine cycle subsequently. By such incorporation, the proposed system can not only increase the overall utilization efficiency of solar energy, but also achieve the cogeneration of crops and electricity on the same land. Energy, exergy, and economic analyses are performed to evaluate the performance of proposed system. Results show that the overall utilization efficiency of solar energy reaches 39.67 % with the highest losses of 79.65 MW in organic Rankine cycle. From the point view of exergy, photoelectric conversion in solar cell is responsible for the largest exergy destruction and finally the overall exergy efficiency of proposed system reaches 40.65 %, which is 16.13 % higher than that of equivalent agricultural and photovoltaic system with separate land use.

Passive and hybrid battery thermal management system by cooling flow control, employing nano-PCM, fins, and metal foam

The performance and safety of lithium-ion batteries, especially in electric and hybrid vehicles, rely on efficient thermal management. Thus, researchers have shown interest in investigating passive, active, and hybrid approaches for battery thermal management (BTM). This study presents a novel hybrid design for a BTM system involving three cylindrical 18650 batteries encased in phase change material (PCM). The research evaluates the impact of fluid flow velocity and direction in the channels on battery temperature, seeking temperature reduction and uniformity. To optimize the proposed system performance, three methods for improving heat transfer in the PCM have been explored and compared, including using nanoparticles (3 %–9 % volume percentage), employing fins (2 and 4 fins), and implementing metal foam (porosity ranging from 85 % to 95 %). Results indicate that, for a 4C discharge rate, incorporating 9 % nanoparticles and 4 fins in the hybrid mode reduces peak temperature by 5.18 and 10.36 K, respectively, compared to the hybrid modes without nanoparticles and fins. These reductions are 10.41 and 9.20 K superior to the corresponding passive modes diminutions, respectively. Additionally, using copper foam with 90 % porosity reduces battery peak temperature by 12.57 K at a 3C discharge rate, highlighting the effectiveness of this method.

Enhanced performance of photovoltaic thermal module and solar thermal flat plate collector connected in series through integration with phase change materials: A comparative study

The decline in photovoltaic cell efficiency due to temperature elevation, coupled with solar energy's inherent intermittency, presents challenges for solar systems, including photovoltaic panels and solar thermal (ST) collectors. The integration of phase change materials (PCMs) emerges as a promising solution to enhance thermal energy storage and regulation, thereby improving system performance and sustainability. This study investigates the performance of series-connected photovoltaic thermal (PVT) and ST systems with integrated PCMs from energy and exergy viewpoints, utilizing a 3D transient numerical simulation. The proposed system's performance is compared with that lacking a PCM across various heat transfer fluid mass flow rates. Suitable PCM selection based on melting temperature is explored, along with PCM layer thickness and nanoparticle augmentation. The research highlights that PCM integration effectively regulates PV cell temperature, enhancing electrical power and exergy rate. While increasing the mass flow rate improves thermal and electrical power, it reduces the thermal exergy rate. Comparing different PCMs (RT28 HC, RT35 HC, and RT44 HC), the RT28 HC PCM provides the lowest temperature of PV cells and the best electrical performance during the day. Moreover, increasing PCM layer thickness results in lowering PV cell and outlet temperatures and storing more heat. Nanoparticles added to PCMs lead to a marginal improvement in energy and exergy, especially for the system with RT28 HC. This analysis emphasizes PCM integration's role in enhancing solar thermal system performance, highlighting the importance of considering the PCM's melting temperature for effective thermal energy management.

Real-time Sentiment Analysis on Social Networks using Meta-model and Machine Learning Techniques

Sentiment analysis is a critical task in social media analysis, enabling the understanding of user attitudes and opinions towards various topics. This paper proposes a real- time sentiment analysis system for social networks that utilizes a meta-model and machine learning techniques to accurately classify user sentiment. The proposed system integrates textual and visual data from social media posts to improve sentiment classification accuracy. The methodology includes data collection and preprocessing, feature extraction and selection, and the proposed meta-model for sentiment analysis. The system utilizes several machine learning techniques, including SVM, CNN, and LSTM networks. We evaluated the proposed system on a large-scale dataset and compared its performance with several state- of-the-art methods. The evaluation metrics, including accuracy, precision, recall, and F1-score, showed that our proposed system outperforms existing methods. The proposed system’s ability to handle multimodal data and achieve high accuracy in real- time makes it suitable for various applications, including social media monitoring and marketing analysis. The proposed system’s limitations provide opportunities for further research, such as developing more efficient algorithms and models that require less training data, and improving techniques for handling noisy and ambiguous data, such as sarcasm and irony. In conclusion, the proposed real-time sentiment analysis system using a meta-model and machine learning techniques provides a robust and efficient solution for sentiment analysis on social networks. The proposed system's performance and potential applications demonstrate its importance in the field of social media analysis.

An innovative study on a geothermal based multigeneration plant with transcritical CO2 cycle: Thermodynamic evaluation and multi-objective optimization

Renewable energy-sourced integrated plants are among the novel and interesting technologies for production of the clean and effective beneficial multiple commodities. Also, these technologies are one of the main motivations for overcoming environmental troubles. The newly designed geothermal-based multigeneration plant deals with clean and sustainable power, hydrogen, cooling, and heating generation and then comprehensive thermodynamic and economic analyses. This system is driven by a geothermal source that includes a transcritical CO2 based Rankine cycle, an ejector cooling cycle, a hydrogen generation and compression unit, and a domestic hot water preparation unit. Here, a comprehensive mathematical modeling is discussed that are energy and exergy efficiency methods along with economic cost evaluation. Furthermore, to define the optimal working conditions, a multiobjective optimization method is fulfilled based on the relationship between the developed multigeneration system's cost and exergy efficiency. Additionally, a comprehensive parametric assessment is implemented to describe how effects of various indicators on the developed model’s efficiency. In outlook of these outcomes, it is crystal clear that the quantity of clean power and hydrogen generation rate is 1283 kW and 0.009374 kgs−1, respectively. The energy and exergy efficiencies of the developed MG cycle are figured as 27.60% and 22.56%, at 152 ℃ geothermal source temperature. the optimal plant total cost rate is computed as 80.48 $h−1. Finally, it is concluded that this proposed system's performance and cost rates are compatible and applicable.

Investigation on recirculated regenerative solid desiccant-assisted dehumidification system: Impact of system configurations and desiccant materials

Desiccant wheels (DWs) have emerged as highly promising alternatives for achieving efficient dehumidification, low-carbon and energy-saving initiatives. The present work aimed to determine the optimal system configuration for dehumidifying enclosed spaces using a DW-based system with the recirculated regeneration of process air based on modelling investigation, which has been validated with experiments. Three distinct configurations of this DW-based system were compared. The impact of operational parameters and desiccant materials on the system dehumidification capacity (Dtot) and energy efficiency was thoroughly analyzed. The results indicate that the posterior diversion of regeneration air was most preferable for improved dehumidification performance, enhancing the Dtot and the improvement of moisture removal capacity (ΔMRC) by 3.7 % and 42.8 %, respectively. Moreover, posterior diversion emerged as a compelling option for energy conservation as it reduced the energy consumption by 13.3 %. Furthermore, six different desiccant materials were compared, and it was found that the synthesized metal silicate material was most suitable for the proposed systems, improving the optimal ΔMRC by 32.2 %. Furthermore, a strong correlation was identified between the maximum adsorption capacity and the proposed system performance. Overall, these findings aid in developing a promising method for enclosed space dehumidification and cooling.

Cloud forensics: A centralized cloud provenance investigation system usingMECC

SummaryAs a new development in digital forensics, cloud forensics (CF) is being utilized to combat cyber‐crimes. Nevertheless, the centralized compilation and preservation of evidence lessen the reliability of digital evidence. Also, the Cloud environment faces the challenge of securing the Provenance Information (PI) without being forged or tampered with, by either an internal or an external party. In the existing works, the researchers have made several attempts to trounce the issues of CF investigation but still, those issues have not been eliminated. Thus, by employing modified elliptic curve cryptography (MECC), an enhanced CF investigation system (CFIS) grounded on a centralized cloud provenance (CCP) system has been proposed to surpass such issues. For encrypting the data for a user and to securely upload and download the data, the MECC was proposed. For producing the group key, SHA‐512 was wielded. The proposed system's performance, when appraised, was found to exhibit enhanced performance against the prevailing methodologies.

Optimizing Electric Vehicle Charging with Moth Flame Control Algorithm of Boost-KY Converter

Electric vehicles have assumed a prominent role in future transport system due to the diminishing availability and escalating costs of fossil fuels, coupled with growing concerns about the impact of global warming. The purpose of the work consists in addressing the pressing need for efficient and sustainable solutions in the realm of electric vehicles and renewable energy integration. The tasks solved in the article to achieve the given goal are the following: an improved Boost-KY converter has been introduced to counter the inherent limitation of low PV panel voltage output. This converter effectively mitigates voltage and current ripples, thereby ensuring a stable power supply for EV charging. Additionally, the Moth Flame Optimized Proportional Integral (MFO-PI) controller has been implemented to regulate converter operation, demonstrating exceptional proficiency in mitigating PV output unpredictability. MATLAB simulation is done to validate the proposed system's performance. The most important results are the achievement of impressive maximum efficiency of 96.21% and remarkably low Total Harmonic Distortion (THD) value of 1.04%. The system maintains consistent voltage and current levels for PV panels and EV battery, ensuring dependable energy supply. The significance of the results obtained consists in their potential to revolutionize the intersection of renewable energy integration, electric vehicle (EV) adoption, and sustainable transportation practices.The PV-based EV charging system not only reduces dependence on finite fossil fuel resources but also contributes to environmental preservation, aligning with global efforts to combat climate change. Furthermore, system adheres to stringent requirements of IEEE 519 standards, positioning it as a catalyst for the adoption of clean energy solutions within the future transport system. Keywords: PV system, electric vehicle, improved Boost-KY converter, Moth Flame optimized PI controller.

Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic

Many power transformers throughout the world are nearing or have gone beyond their theoretical design life. Since these important assets represent approximately 60% of the cost of the substation, monitoring their condition is necessary. Condition monitoring helps in the decision to perform timely maintenance, to replace equipment or extend its life after evaluating if it is degraded. The challenge is to prolong its residual life as much as possible. Dissolved Gas Analysis (DGA) is a well-established strategy to warn of fault onset and to monitor the transformer’s status. This paper proposes a new intelligent system based on DGA; the aim being, on the one hand, to overcome the conventional method weaknesses; and, on the other hand, to improve the transformer diagnosis efficiency by using a four-step powerful artificial intelligence method. (1) Six descriptor sets were built and then improved by the proposed feature reduction approach. Indeed, these six sets are combined and presented to a Kohonen map (KSOM), to cluster the similar descriptors. An averaging process was then applied to the grouped data, to reduce feature dimensionality and to preserve the complete information. (2) For the first time, four direct Multiclass Support Vector Machines (M-SVM) were introduced on the Generic Model basis; each one received the KSOM outputs. (3) Dempster–Shafer fusion was applied to the nine membership probabilities returned by the four M-SVM, to improve the accuracy and to support decision making. (4) An output post-processing approach was suggested to overcome the contradictory evidence problem. The achieved AUROC and sensitivity average percentages of 98.78–95.19% (p-value < 0.001), respectively, highlight the remarkable proposed system performance, bringing a new insight to DGA analysis.

Life cycle assessment and carbon neutrality analysis of 'waste plastics - upcycling plastics' system based on adsorption carbon capture

Integrated with carbon capture and utilization (CCU), the waste plastic upcycling is a promising solution for mitigating environmental issues associated with CO2 emissions and plastic waste. However, there still exists a knowledge gap on how to assess reasonably the coupling environmental performance. In this paper, a 'waste plastics - upcycling plastics' system integrated with CCU is proposed through a three-step process: activated carbon is produced from waste PET plastics, CO2 is captured using PET-AC as the adsorbent, and upcycling plastics are synthesized through CO2 utilization. The life cycle assessment and carbon neutrality analysis are employed to evaluate the environmental performance of proposed system. Our findings show that the CO2 utilization process contributes the most to the system's global warming potential (GWP), accounting for approximately 68% of the total impact. By selecting renewable energy sources in the CO2 capture process and extending the capture time, the degree of carbon neutrality (DCN) could be increased from 0.985 to 1.235 and 0.991, respectively. The GWP and DCN are also influenced by the yield of activated carbon and the type of upcycling plastics.

Unleashing wastewater heat Recovery's potential in smart building systems: Grey wolf-assisted optimization aided by artificial neural networks

This article presents an innovative and efficient way to address residential dwellings' substantial heating needs. The primary objective is to utilize the heat from wastewater to enhance energy efficiency through a control framework based on predetermined rules. This framework aims to increase the incoming air temperature at the air handling unit. A thorough evaluation is carried out to analyze all aspects of the proposed system compared to an identical system that does not incorporate the wastewater heat recovery process. The practicality of the concept is assessed for a residential building located in Beijing, China, employing the TRNSYS software. The most optimal operating condition is achieved via the grey wolf optimizer and TOPSIS decision-making approach equipped with the artificial neural network using MATLAB. Then, the proposed system's performance under optimal conditions is compared with similar works in the literature. According to the results, compared to the conventional system, a higher performance efficiency of 6 % and lower levelized cost of heating of 15.6 $/MWh is obtained by implementing the wastewater heat recovery process. The parametric study results also demonstrate a conflicting change in techno-economic and environmental indicators when altering the primary decision variables, highlighting the necessity for multi-criteria optimization. What stands out from the optimization outcomes is that the grey wolf method increases the efficiency and CO2 saving by around 5.2 and 531.2 kg/year while reducing the levelized cost of heating by about 13.6 $/MWh, respectively. The optimization results reveal that this condition is attained by raising the heat exchanger's effectiveness and the number of residences and decreasing the wastewater temperature. According to the scatter distribution of key parameters, the energy wheel effectiveness has low sensitivity, and the optimal points of tank volume are distributed within the range of 3 m3 and 4 m3.

Efficient energy generation and thermal storage in a photovoltaic thermal system partially covered by solar cells and integrated with organic phase change materials

To address the limitations of conventional photovoltaic thermal systems (i.e., low thermal power, thermal exergy, and heat transfer fluid outlet temperature), this study proposes a photovoltaic thermal system with a solar thermal collector enhancer (PVT-STE), incorporating phase change materials for simultaneous electricity and thermal power generation and thermal energy storage. The system consists of a water based solar collector with a partially covered absorber plate utilizing photovoltaic cells, while each component is equipped with a thermal storage enclosure filled with phase change materials. The primary objectives of this research encompass the evaluation of the proposed system's performance and the identification of appropriate phase change materials for each component. To gain comprehensive insight into the effects of employing and selecting appropriate phase change materials, the study investigates various configurations under both hot and cold weather conditions of Shanghai, China. Water serves as the heat transfer fluid under real-world operational conditions. The numerical results indicate that, for the given conditions, optimal phase change material selection entails using higher melting temperatures (e.g., RT42) in the STE component and lower melting temperatures (e.g., RT31) in the PVT component. Employing the optimal phase change materials for each component, the system can store 3234 and 1802 kJ/m2 of thermal energy during the charging process in July and November, respectively. Moreover, the proposed system generates almost 1.8 and 2 times more thermal energy per square meter in July and November, respectively, compared to a standalone photovoltaic thermal system coupled with a phase change material.

An Efficient Filter and Wrapper based Selection Methods along With Random Forest and Support Vector Machines Classification Technique in Health Care System

Health care Management System (HMS) is a key to successful management of any health care industry. Health care management systems have so many research dimensions such as identifying disease and diagnostic, drug discovery manufacturing, Bioinformatics’ problem, personalized treatments, Patient image analysis and so on. Heart Disease Prediction (HDP) is a process of identifying heart disease in advance and recognizes patient health condition by applying techniques on patient heart related symptoms. Now a day’s the problem of identifying heart diseases is solved by machine learning techniques. In this paper we construct a heart disease prediction method using combined feature selection and classification machine learning techniques. According to the existing study the one of the main difficult in heart disease prediction system is that the available data in open sources are not properly recorded the necessary characteristics and there is some lagging in finding the useful features from the available features. The process of removing inappropriate features from an available feature set while preserving sufficient classification accuracy is known as feature selection. A methodology is proposed in this paper that consists of two phases: Phase one employs two broad categories of feature selection techniques to identify the efficient feature sets and it is given to the input of our second phase such as classification. In this work we will concentrate on filter-based method for feature selection such as Chi-square, Fast Correlation Based Filter (FCBF), Gini Index (GI), RelifeF, and wrapper-based method for feature selection such as Backward Feature Elimination (BFE), Exhaustive Feature Selection (EFS), Forward Feature Selection (FFS), and Recursive Feature Elimination (RFE). The UCI heart disease data set is used to evaluate the output in this study. Finally, the proposed system's performance is validated by various experiments setups.

Earlier detection of rop and rop sub-classification using TA-CHD-DNN and UD-TFCM from ultrasound digital B-Scan images

The growth of the retinal blood vessels of the baby begins at 16 weeks and even after the birth of the baby, they don’t grow completely. For the effective analysis of Retinopathy of Prematurity (ROP), diverse methodologies are employed. Owing to the insufficient growth of tangled patterns, greater retinal layer thickness, and the presence of hyperreflective material, the majority of the traditional methodologies were unsuccessful in classifying the Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and DRUSEN stages of ROP. Thus, for the classification of different stages of the ROP, an effective Twin Active- Cosine-Hausdorff Distance-Deep Neural Network (TA-CHD-DNN) is proposed in this paper. Chiefly, the image obtained from the retinal image dataset is pre-processed and fed into TA- CHD-DNN. After the extraction of critical features, the segmentation of the retinal layer, choroid layer, and vascular layer occurs in TA-CHD- DNN. The classifier is trained to make decisions for the classification of diverse stages of ROP centered on the extracted features. The normal or abnormal stages of CNV, DME, and DRUSEN are classified by the classifier during testing. By utilizing the Uniformly Distributed-Trapezoidal Fuzzy C Means (UD-TFCM) technique, the decision regarding the severity stage of CNV, DME, and DRUSEN is made in the abnormal stage. Thus, centered on the experimental outcomes, the proposed system performance will be analogized to the conventional techniques to confirm the proposed system’s efficiency.

EFF_D_SVM: a robust multi-type brain tumor classification system.

Brain tumors are one of the most threatening diseases to human health. Accurate identification of the type of brain tumor is essential for patients and doctors. An automated brain tumor diagnosis system based on Magnetic Resonance Imaging (MRI) can help doctors to identify the type of tumor and reduce their workload, so it is vital to improve the performance of such systems. Due to the challenge of collecting sufficient data on brain tumors, utilizing pre-trained Convolutional Neural Network (CNN) models for brain tumors classification is a feasible approach. The study proposes a novel brain tumor classification system, called EFF_D_SVM, which is developed on the basic of pre-trained EfficientNetB0 model. Firstly, a new feature extraction module EFF_D was proposed, in which the classification layer of EfficientNetB0 was replaced with two dropout layers and two dense layers. Secondly, the EFF_D model was fine-tuned using Softmax, and then features of brain tumor images were extracted using the fine-tuned EFF_D. Finally, the features were classified using Support Vector Machine (SVM). In order to verify the effectiveness of the proposed brain tumor classification system, a series of comparative experiments were carried out. Moreover, to understand the extracted features of the brain tumor images, Grad-CAM technology was used to visualize the proposed model. Furthermore, cross-validation was conducted to verify the robustness of the proposed model. The evaluation metrics including accuracy, F1-score, recall, and precision were used to evaluate proposed system performance. The experimental results indicate that the proposed model is superior to other state-of-the-art models.

Artificial Raindrop Algorithm-Based Load Frequency Control in PV-Thermal-Thermal Interconnected Power System

Nowadays Grid connected photovoltaic (PV) systems increases its influence in Indian power system. The current rise in load and demand means that linked power networks are essential. The efficiency of a load is significantly impacted by power quality issues, especially when it comes to frequency. The power system’s frequency and tie-line power should be as close as possible to the anticipated specifications. This article proposes artificial raindrop method with proportional- integral (PI) controller, to manage the load frequency control (LFC) of a PV-thermal-thermal interconnected three-area power system. A meta-heuristic method that takes inspiration from natural rainfall is the proposed artificial raindrop algorithm. A comparison is made between the suggested system and standard PI controller. A number of loads are simulated using Matlab to assess the full system. To validate the performance of proposed system, it is compared with the LFC using PI and fuzzy gain scheduling controller. Matlab is used to evaluate the entire system with varying loads. Take into account area 1 in 10% load perturbation: FGS reduces frequency deviation undershoot by 22% compared to the PI controller, while the ARD algorithm reduces it by 32%, where PI results in an undershoot of 0.1781 Hz.

Resourcing idle privately owned autonomous electric vehicles: System design and simulation for ride-sharing in residential areas of the aging in Japan

Shared mobility services, autonomous vehicles (AVs), and electric vehicles (EVs) are potential solutions to issues triggered by suburbanization, greenhouse gas emissions, and population aging. In this study, a shared autonomous electric vehicle (SAEV) system is proposed to create a convenient and environmentally friendly shared mobility service system based on AVs for commuters and aging users in the suburbs by exploiting the idle time of park-and-ride private vehicles in Japan. The SAEV system provides mobility services to two user groups, “inbound commuters” and “elderly and disabled residents,” in two study areas. The proposed system performance was investigated using an agent-based simulation. The results show that non-EV scenarios have a better level of service owing to the improvement in average waiting time by about 20% when compared to EV scenarios which require charging. However, the SAEVs can still provide acceptable levels of mobility services. The waiting time can be maintained within 5 min when the fleet size is set at 40 trips per vehicle or less, and ride-sharing can reduce the waiting time significantly when the demand is high such as 80 trips per vehicle. Further, an average emission reduction of over 40% is anticipated when compared with the users’ previous travel modes. The system managers are advised to keep the demand trips per vehicle under 40 trips/vehicle for a low waiting time (approximate 5 min or less).

Optimising a solar-based microgrid system for urban areas: a case study in Edmonton

Nowadays, the transition from fossil fuels to renewables and solving renewable energy challenges such as their oscillating nature has become necessary. Here, the building energy model of a residential area in Edmonton, Canada, is formulated using computer tools to provide this area's required power through solar energy optimally. The meta-heuristic approach is utilised to address and optimise the proposed system performance. The results of modelling and optimising system performance for two scenarios (free and prohibited sale of generated electricity to the power grid) are reported. The most striking result is that the PV panel capacity is 756 kW, but the total area required to install these panels was 4458 m2. The total electricity produced by photovoltaics on a summer day in the base scenario was 685 kWh, and the total energy purchased from the grid was 995 kWh. These values for the winter day were 212 and 1496 kWh, respectively.

Reconfigurable intelligent surface-assisted free-space optical communication link for theSatellite-Internet of Things.

The Satellite-Internet of Things (S-IoT) is envisaged as one of the promising technological enhancements in this decade due to its outright connectivity, especially in the areas of terrestrial communication and weather forecasting. Invariably, turbulent atmospheric conditions and varied terrain profiles are hindrances for satellite based free-space optical (FSO) communication, and immensely disrupt signal levels. Recently, a new technological invention called a reconfigurable intelligent surface (RIS) empowered radio transmission environments for next generation wireless/optical technology has emerged. Elements of RIS are used to intelligently tune the striking beam towards desired locations. In this paper, we propose an RIS based FSO link to manueuver S-IoT. The challenges of pointing errors and turbulent atmospheric connectivity from a satellite to ground station and vice versa with the cooperation of RIS have been derived and substantiated with closed form solutions. The proposed system's performance has been analyzed using packet and bit error rates, and outage probability over atmospheric turbulence, pointing errors, and signal blockage scenarios. Results are evaluated for different RIS elements as a function of average signal to noise ratio. The proposed system is extremely productive, since this scheme has the capability to provide dependable connectivity in rural, urban, and suburban environments where reliable connectivity is difficult in spite of traditional infrastructures.