Journal of Science Engineering Technology and Management Science

An internal combustion engine (IC) is a type of heat engine in which the combustion of a fuel takes place with an oxidizer which is usually air in a combustion chamber. In an internal combustion engine, there is an expansion of high-temperature and pressure gases which are produced by combustion which then applies direct force to some of component of the engine. Generally the force is applied to pistons, turbine blades, rotor or a nozzle. Internal combustion engine usually refers to an engine in which combustion is intermittent. This paper presents the work done on various Internal combustion engines, their types and their performance characteristics. In this paper a review has been done on various IC engines and shows different parameters and characteristics taken by different researchers in enhancing the performance of these engines. Keywords: Internal combustion engine, Turbochargers, Fins.

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Author : DR E.L. NAGESH,DR RAMESH DARA, A VENU,T PRASANNA,CH.SHRUTHI,G.VINEELA,T.KALYAN

Title : A REVIEW ON DIFFERENT TYPES OF IC ENGINES AND THEIR PERFORMANCE

Volume/Issue : 2025;02(11)

Page No : 1-4

This research aimed at experimentally the efficacy of ferrogeopolymer jacketing for strengthening damaged reinforced concrete columns. Six column specimens (150×150×1500 mm) were subjected to axial loading 80% of their ultimate capacity to simulate varying degrees of damage. The damaged columns were then retrofitted using ferrogeopolymer jackets embedded with up to 3 layers of welded wire mesh (WWM) and expanded metal mesh (EMM). The load carrying capacity, axial deflection, and ductility indices of the retrofitted column specimens were assessed and contrasted with unconfined column specimen. The experimental ultimate load values were then corroborated by comparison with modified equations of ACI 318 and ECP 203. Regression analysis was performed to establish models between experimental and predicted results, and predicted results showed significant concordance with the experimental values. It was found that ferrogeopolymer jacketing columns substantially enhances structural integrity in terms of load carrying capacity, axial deflection and ductility indices, offering a viable alternative method for column rehabilitation.

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Author : M.RAJENDER REDDY,M.PRANAY KUMAR,G.SHASHIDHAR REDDY,CH.SHRUTHI,G.VINEELA,T.KALYAN

Title : AXIAL COMPRESSION BEHAVIOUR OF REPAIR AND STRENGTHENING OF DAMAGED REINFORCED STRUCTURAL CONCRETE COLUMNS USING HYBRID FERRO GEOPOLYMER JACKETS

Volume/Issue : 2025;02(11)

Page No : 5-18

Charging batteries of light electric vehicles require chargers with high efficiency and a highpower factor. To meet this need, this paper presents a bridgeless single-power-conversion battery charger composed of an isolated step-up AC-DC converter with a series resonance circuit. The bridgeless configuration reduces the conduction losses associated with the input diode rectifier, and the seriesresonance circuit reduces the reverse recovery losses of the output diodes by providing zero current switching. In the proposed rectifier, a back-to-back bridgeless boost PFC topology is adopted at highline conditions and a three-level bridgeless boost PFC topology Keywords is rebuilt to reduce the switching losses at low-line conditions. The bridgeless boost topology is for plug-in hybrid electric vehicle and electric vehicle battery chargers to achieve high efficiency, which is critical to minimize the charger size, charging time and the amount and cost of electricity drawn from the utility

Keywords : Boost Converter, Single phase PFC, flexible mode, high efficiency

Author : MOGILI SRIHARSHITHA,BABU RAO PADDAM

Title : HIGH EFFICIENCY BRIDGELESS SINGLE POWER CONVERSION BATTERY CHARGER FOR ELECTRIC LOAD

Volume/Issue : 2025;02(11)

Page No : 19-24

This paper presents a novel singlestage buck-boost transformerless inverter (BBTI) topology for single-phase gridconnected solar PV applications. In this topology, the input PV source shares the common ground with neutral of the grid which eliminates the leakage currents. Further, the proposed topology has the buck-boost ability which tracks the maximum power point even under the wide variation of input PV voltage. Another feature of the proposed topology is that it uses only one energy storage inductor which provides symmetric operation during both half cycles of the grid. In addition, the two out of five switches of the proposed topology operate at a line frequency, thereby, it exhibits low switching losses and the other three switches conduct in any mode of operation which incurs low conductions losses. A simple sine-triangle pulse width modulation strategy is proposed to control the proposed inverter topology is analyzed at all operating modes and explained in detail.

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Author : MITTAKOLU CHANDANAPRIYA,MALOTH LAKPATHI

Title : A NOVEL SINGLE-STAGE BUCK-BOOST TRANSFORMER LESS INVERTER FOR 1-? GRID-CONNECTED SOLAR PV SYSTEMS

Volume/Issue : 2025;02(11)

Page No : 25-30

Energy storage is the capture of energy produced for use at a time to time. Hybrid storage devices are used in microgrids to provide power backup solutions when the distributed energy resources (DERs) are unable to supply the load demands. This work deals with the design and stability analysis of a DC microgrid with battery-supercapacitor energy storage system under variable supercapacitor operating voltage. The conventional design method reported in the literature considers the rated supercapacitor voltage in the modeling and design of controllers. However, the supercapacitor unit can discharge as low as 10% of its rated voltage due to self-discharge. It is observed that the conventional method of controller design can potentially make the system unstable. This paper proposed an optimal super capacitor voltage to be considered in the design is calculated and a design method is proposed to ensure the stability of DC microgrid in all operating modes.

Keywords : Energy management, MPPT, bidirectional power converter, photovoltaic, super capcitor, battery, hybrid power unit.

Author : BONKURI SAMARPAN,BANOTH SURESH,BABU RAO PADDAM

Title : HYBRID ENERGY MANAGEMENT AND CONTROL STRATEGY OF PHOTOVOLTAIC GENERATION SYSTEMS

Volume/Issue : 2025;02(11)

Page No : 31-38

SMS spam has become one of the most persistent mobile security threats, often exploiting evasive techniques such as obfuscation, misspelling, mimicry, and content manipulation to bypass traditional filtering systems. This study investigates how these evasive strategies influence the performance of widely used machine learning models for SMS spam detection. A benchmark dataset is enhanced with synthetically generated evasive spam samples to test classifier resilience under real-world attack conditions. Multiple machine learning algorithms—including Naïve Bayes, Support Vector Machines (SVM), Random Forest, Logistic Regression, and Gradient Boosting—are comparatively evaluated based on accuracy, precision, recall, F1-score, and robustness against evasion attempts. The results show that while traditional statistical models perform well on clean datasets, their performance significantly degrades under evasion. Ensemble-based models and gradient-boosting approaches demonstrate superior resilience and adaptability. This research emphasizes the critical need for evasion-aware training strategies and adaptive learning mechanisms in modern SMS spam filtering systems.

Keywords : SMS Spam Detection,Machine Learning Models,Evasive Techniques,Obfuscation,Text Manipulation,Adversarial Spam,Classification Algorithms,Robustness Evaluation

Author : Rajasri Gabbeta,Mrs Deepa

Title : ANALYZING THE IMPACT OF EVASIVE TECHNIQUES ON SMS SPAM FILTERING USING MACHINE LEARNING MODELS

Volume/Issue : 2025;02(11)

Page No : 39-45

The efficient utilization and management of battery energy in electric vehicles (EVs) is a critical factor influencing driving range, battery lifespan, and overall power efficiency. One of the major challenges in EV battery systems is uneven discharge among battery modules, which can lead to capacity imbalances, increased power losses, thermal instability, and premature battery degradation. Traditional passive and active balancing techniques require additional circuitry, which increases system complexity and energy losses. To overcome these limitations, this paper proposes a Fuzzy Logic Controlled Cascaded Multilevel Inverter (FLCCMLI) system for real-time battery balancing in EV power transmission. The proposed system integrates a Fuzzy Logic Controller (FLC) with a Cascaded H-Bridge Multilevel Inverter (CMLI) to ensure uniform discharge of battery modules. The FLC continuously monitors State-of-Charge (SOC) variations among individual battery packs and dynamically adjusts PWM switching signals in the inverter to regulate the power drawn from each battery module. Unlike conventional controllers, FLC does not require an accurate mathematical model of the system, making it robust against nonlinearities, load fluctuations, and battery aging effects. By adapting the modulation index of the inverter in real time, the system ensures that all battery modules contribute equally to power delivery, reducing SOC imbalances and preventing excessive stress on specific cells. A comprehensive MATLAB/Simulink simulation is conducted to evaluate the effectiveness of the proposed system under various load conditions and battery states. The results demonstrate that FLC-based balancing reduces SOC variations by 20-30%, significantly enhancing the lifespan of individual battery cells. Additionally, Total Harmonic Distortion (THD) is reduced below 5%, and power utilization efficiency improves by up to 15% compared to conventional control methods. These improvements lead to better energy management, increased reliability of EV power transmission, and extended driving range. The findings of this study highlight the potential of intelligent fuzzy logicbased battery balancing techniques as a viable alternative to traditional passive resistive and active balancing circuits. The proposed FLC-CMLI framework not only optimizes power flow but also minimizes energy dissipation, improving overall system efficiency and sustainability. Future work can focus on real-time implementation using embedded controllers and the integration of AI-driven predictive balancing techniques to further enhance EV battery management systems.

Keywords : cascaded multilevel inverter (cmli), fuzzy logic control (flc), sinusoidal pulse width modulation (spwm), total harmonic distortion (thd), electric vehicles (evs), power transmission, efficiency optimization, dynamic response, switching losses, harmonic reduction, adaptive control.

Author : Addanki Nagasai,Awaar Vinay Kumar,Jugge Praveen

Title : Fuzzy logic control of cascaded multilevel inverter based power transmission in electric vehicles

Volume/Issue : 2025;02(11)

Page No : 46-51

Green finance refers to financial activities and investments that promote environmentally sustainable economic growth and development. It is a key component of the transition to a more sustainable economy, as it enables the allocation of capital towards activities that contribute to mitigating climate change and reducing other environmental impacts. Green finance has emerged as a critical tool for promoting environmentally responsible investments and supporting global sustainability goals. This research paper explores the relationship between green finance and sustainability management by examining key financial instruments, policy frameworks, and organizational strategies that enhance environmental performance. The study highlights the role of green bonds, sustainable banking practices, environmental, social, and governance (ESG) metrics, and climate?risk disclosure in guiding sustainable development. The findings indicate that green finance not only supports environmental protection but also strengthens long-term economic resilience and corporate competitiveness.

Keywords : Environment; Green finance; Green building; Green projects; Renewable energy

Author : L.CHAITANYA

Title : GREEN FINANCE AND SUSTAINABILITY MANAGEMENT: A COMPREHENSIVE RESEARCH STUDY

Volume/Issue : 2025;02(11)

Page No : 52-58

Devdutt Pattanaik is an internationally acclaimed Indian mythologist and author, known for his creative re-imagining of Hindu mythology from a modern perspective and for successfully weaving age-old stories into contemporary social observations and critiques. This research paper examines two of the most important works written by him: the absorbing works The Pregnant King, published in 2008, which explores gender fluidity and dharma, and Sati Savitri, published in 2024, a collection of myths curated by him from a feminist point of view to debunk deep-rooted patriarchal traditions. It examines closely the summaries, thematic dissections, and insightfully comparative discussions that reveal the manner in which Pattanaik engages in the discourses of gender, questions of identity, and agency in the infinite matrix of Indian mythology. Apparently, the work The Pregnant King upsets traditional notions of gender roles, but actually it subsumes them, offering a subtle critique of gender fluidity. On the other hand, Sati Savitri overtly upholds women's intrinsic agency as a dynamic cultural factor, thereby fuelling important discussions about the position of women and their empowerment within mythological narratives. Drawing from a wide array of scholarly critiques and reviews, this analysis shall underline Pattanaik's progressive trajectory toward a more overt feminism, emphasizing the crucial role mythology plays in shedding light on and engaging with contemporary discourses of gender.

Keywords : Patriarchy, Ramayana, Mahabharata, Feminism, Sati Savitri, Empowered.

Author : Livish,Shubhra Tiwari

Title : REVIEW ON DEVDUTT PATTANAIK’S WORK: THE PREGNANT KING AND SATI SAVITRI

Volume/Issue : 2025;02(11)

Page No : 59-65

Gender has always been the single most important aspect of the lives of men and women in any given culture and at any time in human history. Devdutt Pattanaik's works on Hindu mythology, such as Myth = Mithya, Sati Savitri,Sita: An Illustrated Retelling of the Ramayana and Jaya: An Illustrated Retelling of Mahabharata, enlighten one about the aspects of gender relations by using men as "mind" (culture) versus women as "matter" (body). This shows the fluidity of gender and agency for women. Of course, there will always be an argument about the tension that exists between the patriarchal system that clearly wants to restrict the role of women and the feminist themes that show women making choices against their culture. This paper thus argues that while Pattanaik challenges fixed gender binaries, it also reflects current contestations over gender in complex paradoxes. Social hierarchies are constructed by class, race, and religion, placing women in subordinate positions. These norms generate stereotypes and determine power dynamics in society. Stereotyping is based on bias and oppression and is continuously a threat to the path of progress towards achieving balance in gender roles. The gender dynamics of Indian society are always related to its mythology and traditions, enlightened by values, rituals, and power structures, giving way to deeper insights into gender roles and identities. In this paper, archetypal representations and feminist revisions can both subvert patriarchal hegemony and contribute to broader discourses on gender equity in modern India. The present research study will utilize a qualitative research approach, which will incorporate content analysis and feminist critical discourse analysis.

Keywords : Gender Dynamics, Intractability, Mythology, Patriarchy, Power Dynamics

Author : Livish,Shubhra Tiwari

Title : GENDER DYNAMICS AND INTRACTABILITY IN THE SELECTED MYTHOLOGICAL WORKS OF DEVDUTT PATTANAIK

Volume/Issue : 2025;02(11)

Page No : 66-76

This work presents a unified deep learning–based Indian Sign Language (ISL) and speech recognition system designed to bridge communication gaps between hearing-impaired individuals and the general population [1], [7]. The proposed framework integrates image-based gesture recognition with natural speech-to-text conversion to enable seamless two-way interaction, following earlier efforts in sign-tospeech and gesture-to-text translation systems [1], [2], [4]. Using convolutional neural networks (CNN), feature extraction from hand gestures is performed, while recurrent and transformer-based models handle speech recognition tasks [11], [12]. The system aims to deliver high accuracy, robustness to noise, and real-time performance, improving upon traditional ISL recognition and glove-based solutions [2], [6], [9] and making it suitable for assistive communication applications [3], [5], [8].

Keywords : Indian Sign Language, Speech Recognition, Deep Learning, CNN, Gesture Recognition, Assistive Technology

Author : Mr. N Naresh Reddy, Kanuri Sowmya Sree, Thota Deepak Shetty, K. Sri Hari Krishna, Balasani Varshith

Title : A UNIFIED DEEP LEARNING FRAMEWORK FOR INDIAN SIGN LANGUAGE INTERPRETATION AND SPEECH RECOGNITION

Volume/Issue : 2025;02(11)

Page No : 77-82

Detecting side-effects arising from adverse drug–drug interactions (DDIs) has become a crucial focus in modern pharmacovigilance, driven by the widespread use of polypharmacy and the growing need for automated, data-driven safety monitoring. Existing research demonstrates significant progress in DDI prediction, adverse drug reaction (ADR) detection, and pharmacological feature modeling through methods such as label propagation, multi-dimensional feature fusion, graph neural networks (GNNs), and deep neural architectures [1–14]. Recent studies emphasize the increasing reliance on real-world evidence, spontaneous reporting systems, electronic health records, and curated datasets such as TWOSIDES, OFFSIDES, DrugBank, and FAERS to improve the reliability of interaction-based ADR identification [4–7, 15–20]. Building upon these advancements, this work proposes an enhanced DDIbased side-effect detection framework that integrates molecular representation learning, spatiostructural drug-feature fusion, and signal-detection analysis to accurately identify harmful interaction-induced reactions. Leveraging insights from network-based inference, statistical disproportionality methods, and interpretable machine learning models, the system aims to improve prediction accuracy while reducing false positive signals. The study contributes a unified analysis of traditional pharmacovigilance techniques and contemporary AI-driven approaches, highlighting their strengths, limitations, and applicability to real-world clinical settings. The proposed model aligns with emerging trends in intelligent drug-safety surveillance and offers a scalable, explainable solution suitable for large-scale healthcare environments.

Keywords : Drug–drug interactions, adverse drug reactions, pharmacovigilance, deep learning, graph neural networks, molecular feature fusion, TWOSIDES dataset, OFFSIDES dataset, signal detection, disproportionality analysis, electronic health records, machine learning, drug safety monitoring, sideeffect prediction, healthcare analytics.

Author : Mr. N.Chandira Prakash, Dyagari Sathvika, Rachala Charitha, Kallem Supriya, Peram Bharghav Reddy

Title : MONITORING AND PREDICTION OF SIDE EFFECTS FROM POLYPHARMACY-INDUCED INTERACTIONS

Volume/Issue : 2025;02(11)

Page No : 83-91

Agricultural productivity in India is highly influenced by climatic variability and heterogeneous soil conditions, making accurate crop yield prediction a critical factor for ensuring food security and supporting farmers’ decision-making [11][18]. Traditional statistical and regression-based forecasting approaches often fail to model the complex nonlinear relationships that exist between climatic parameters, soil characteristics, and crop growth patterns [1][3]. To address this challenge, the present study proposes a deep learning-based framework for predicting crop yield across Indian states using integrated climate and soil data [2][5][6]. The dataset includes key climate indicators such as temperature, rainfall, humidity, and sunshine duration, along with soil attributes including pH, organic carbon, nitrogen, phosphorus, potassium, and soil texture [15][20]. A Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) hybrid architecture is adopted to capture both spatial and temporal dependencies in crop growth factors [6][13][14]. The model is trained and evaluated on multi-year data covering major crops cultivated across different agro-climatic zones of India [5][9]. Experimental results demonstrate that the proposed deep learning model significantly outperforms traditional machine learning methods in terms of accuracy, RMSE, and generalization capability [1][16][17]. The system offers a robust decision-support tool that can help policymakers, agronomists, and farmers predict yield in advance, optimize resource allocation, and effectively mitigate climate-induced risks, thereby contributing to sustainable agriculture and enhanced national food security [7][8][10].

Keywords : Crop Yield Prediction, Deep Learning, Climate Data, Soil Data, LSTM-CNN Hybrid Model, Precision Agriculture, Indian States, Agricultural Forecasting, Food Security, Machine Learning, Sustainable Farming, Big Data Analytics.

Author : Ms. Kanakaprabha.S, S.Bhuvaneshwar, S Aveti Ramya Kala Sai, N.Srinivas, Yadla Mahesh

Title : HYBRID DEEP LEARNING ARCHITECTURE FOR STATE-WISE CROP YIELD FORECASTING USING WEATHER AND SOIL INDICATORS

Volume/Issue : 2025;02(11)

Page No : 92-100

Predicting the length of hospital stay (LOS) is a critical factor in enhancing healthcare resource management, improving patient flow, and minimizing operational costs in hospitals [4][9]. Manual estimation of LOS is often inaccurate due to the complex and nonlinear relationships among clinical, demographic, and treatment-related variables [7][22]. In this project, an Explainable Machine Learning (XML)–based framework is developed to accurately forecast hospital stay duration while ensuring transparency and interpretability in decision-making [2][8][12]. The system utilizes a structured dataset containing patient demographic information, medical history, diagnosis records, laboratory values, and treatment details [1][7][21]. After preprocessing and feature engineering, multiple ML models—including Random Forest, XGBoost, and LightGBM—are trained and evaluated [5][6][10][13]. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) techniques are integrated to identify the influence of each attribute on predictions, enabling clinicians to understand why specific patients are likely to experience longer stays [2][8][12][25]. The proposed approach outperforms conventional statistical methods and black-box models by achieving high prediction accuracy while maintaining full explainability [3][11][16]. It provides interpretable insights such as the impact of age, comorbidities, severity of illness, diagnostic categories, surgical procedures, and laboratory findings on LOS [7][15][22]. The system is deployed in a user-friendly Flask web application featuring two modules: Admin, responsible for dataset upload, preprocessing, and model training, and User, where clinicians can input patient parameters and instantly obtain predicted hospital stay duration along with a graphical explanation highlighting key contributive factors [14][19]. This solution supports data-driven decisionmaking, improves bed allocation and discharge planning, and ultimately enhances healthcare quality [4][9][14]. The explainable ML model contributes toward a practical and transparent smart-healthcare ecosystem, enabling hospitals to operate more efficiently while improving patient outcomes [17][20][23].

Keywords : Hospital Stay Length, Explainable Machine Learning, SHAP, LIME, Healthcare Prediction, Resource Management, Patient Flow Optimization, Clinical Decision Support, Length of Stay Forecasting, Flask Web Application

Author : Mr M.Sakthivel, Ch. Jaya Chaitanya Mithra, Sama Sahith Reddy, Pathlavath Suman, Kamani Sai Sri Harsha

Title : EXPLAINABLE AI FRAMEWORK FOR FORECASTING PATIENT LENGTH OF HOSPITAL STAY USING CLINICAL AND DEMOGRAPHIC INDICATORS

Volume/Issue : 2025;02(11)

Page No : 101-109

This project involves a hybrid micro-grid system integrating a wind power plant, a solar power plant, and a diesel generator as a backup, all managed using a fuzzy logic controller. The wind and solar inputs are time-varying, meaning the wind power generation changes with wind speed and the solar power changes with irradiance levels throughout the day. Additionally, the system powers a 10 MW residential load and an electric vehicle charging station, both of which vary over time. When the combined renewable generation (from wind and solar) is insufficient to meet the load demand, the fuzzy logic controller activates the diesel generator to supply the remaining power needed. In case all else fails, the utility grid serves as an additional backup. The entire system is simulated in MATLAB, and results confirm that the fuzzy logic controller effectively balances the load and generation, ensuring stable and reliable operation.

Keywords : Hybrid micro-grid, Fuzzy logic control, Renewable energy integration, Wind and solar power, Diesel generator backup, Energy management, MATLAB simulation

Author : Ms. Boddula Sowmya,Dr. K. Ranjith Kumar

Title : HYBRID MICRO-GRID SYSTEM ENERGY MANAGEMENT WITH RENEWABLE ENERGY USING FUZZY LOGIC CONTROLLER

Volume/Issue : 2025;02(11)

Page No : 110-120

The increasing penetration of solar photovoltaic (PV) systems into low-voltage distribution networks is limited by inverter-related constraints such as voltage rise, power quality degradation, and restricted hosting capacity. This work presents a simulation-based analysis of improved inverter control techniques aimed at enhancing the hosting capacity of grid-connected solar PV systems integrated with a Battery Energy Storage System (BESS). The proposed configuration consists of a PV array subjected to both steady-state and variable irradiance conditions to emulate realistic environmental variations. The variable PV output is processed through a DC–DC boost converter, whose output is stabilized using a DC-link capacitor. A bidirectional DC–DC converter interfaces the battery unit with the DC link, enabling controlled charging and discharging to mitigate PV intermittency. The inverter is connected to a single-phase 230 V grid through an LCL filter while simultaneously supplying local loads. Maximum Power Point Tracking (MPPT) is implemented for the PV-side DC–DC converter to ensure optimal energy extraction under varying irradiance. An Artificial Neural Network (ANN)- based controller is employed for inverter control to enhance dynamic response, voltage regulation, and power quality. Extensive MATLAB/Simulink simulations demonstrate that the proposed control strategy effectively improves power flow management, reduces grid stress, and increases PV hosting capacity under fluctuating generation conditions. The results validate the suitability of ANNcontrolled inverters with BESS for future high-penetration PV-integrated distribution systems.

Keywords : Solar Photovoltaic System, Battery Energy Storage System, Inverter Control, Hosting Capacity, Artificial Neural Network, MPPT Algorithm, Grid-Connected PV

Author : Ms. Domala Vennela,Mr. P. Purna Chander Rao

Title : SIMULATION ANALYSIS TO IMPROVED INVERTER CONTROL TECHNIQUES IN TERMS OF HOSTING CAPACITY FOR SOLAR PHOTOVOLTAIC ENERGY WITH BATTERY ENERGY STORAGE SYSTEM

Volume/Issue : 2025;02(11)

Page No : 121-132

The increasing penetration of photovoltaic (PV) systems into modern power grids has introduced significant challenges related to voltage instability, power fluctuations, and reduced grid reliability due to the intermittent nature of solar energy. To address these issues, this paper presents a fuzzy logic-based hybrid energy storage control strategy aimed at enhancing the operational stability of grid-connected photovoltaic systems. The proposed framework integrates a hybrid energy storage system composed of batteries and supercapacitors, enabling effective management of both long-term energy balancing and short-term power transients. A fuzzy logic controller (FLC) is employed to intelligently regulate the charging and discharging operations of the hybrid storage system based on key system parameters such as photovoltaic power variation, grid voltage deviation, and load demand dynamics. Unlike conventional linear control methods, the fuzzy logic approach does not rely on an accurate mathematical model, making it highly suitable for handling nonlinearities and uncertainties inherent in renewable energy systems. The control strategy ensures smooth power injection into the grid, mitigates voltage fluctuations, and improves overall system stability under varying irradiance and load conditions. Simulation studies carried out in MATLAB/Simulink demonstrate that the proposed control scheme significantly enhances voltage regulation, reduces power oscillations, and improves dynamic response compared to traditional control techniques. The results confirm that the fuzzy logic-based hybrid energy storage system provides a robust and efficient solution for stabilising grid-connected PV systems, thereby supporting higher renewable energy penetration and improved grid resilience.

Keywords : Photovoltaic systems, Hybrid energy storage, Fuzzy logic controller, Grid-connected inverters, Voltage stability, Renewable energy integration, Power quality enhancement.

Author : Mr.Dubasi Nithin, Mr.Nagaraju Budidha

Title : A FUZZY LOGIC-BASED HYBRID ENERGY STORAGE CONTROL STRATEGY FOR ENHANCING STABILITY IN PHOTOVOLTAIC GRID-CONNECTED SYSTEMS

Volume/Issue : 2025;02(11)

Page No : 133-142