KEC Journal of Science and Engineering is a joint publication of the KEC Faculty of Electronics and Communications, Computer Engineering and Electrical Engineering, Kalimati, Nepal. This journal publishes original, empirical and innovative works in basic, functional and support areas of science, technology and engineering disciplines.
A Comparative Analysis of Transformer-less Inverter Topologies for Grid-Connected PV Systems: Minimizing Leakage Current and THD
The integration of distributed energy resources (DERs), particularly photovoltaic (PV) systems, into the power grids have gained major attention due to their environmental and economic benefits. Although traditional transformer-based grid-connected PV inverter provides galvanic isolation for leakage current, it suffers from major drawbacks of high cost, lower efficiency, and increased size. Transformer-less grid-connected PV inverter (TLGI) has emerged as a prominent alternative as this achieves higher efficiency, compact design, and lower cost. However, due to a lack of galvanic isolation, TLGI is highly affected by the leakage current caused by the fluctuation of common mode voltage (CMV). This paper investigates three topologies —H4, H5, and HERIC—with the comparisons between their CMV, differential mode voltage (DMV), total harmonic distortion (THD), and leakage current. A simulation was done for each topology in MATLAB(Simulink) R2023a and the results from the simulation demonstrate that the H5 topology achieves a balance between low leakage current, reduced THD, and optimal operational efficiency, making it suitable for practical application.
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A Numerical Technique of Modelling Mortality Rate Intensity for Life Insurance Implementation
Numerical estimation technique of mortality intensities is integrated into actuarial modelling to ensure cost effective approximations and ascertain benchmarks against which accuracy of mortality table production is verified for applications in life insurance product development. However, some of the governing functional mortality laws analytically formulated as an integral part of underwriting process in life table constructions may not readily have closed form solutions because they are completely intractable. This accounts for why many extant actuarial literatures avoid investigating their asymptotic properties at extreme ages. In this study, the method of successive differencing was deployed to even out errors associated with source vital statistics in order to track the level of any momentous change. The objectives of this study are to produce the instantaneous mortality rate intensities using the Generalized Makeham’s mortality law, to confirm the age at which the probability of death will be 1 and to compute the curve of death. Computational evidence from our result shows that the probability of death for a life aged x approaches 1 at age 120 hence the model supports longevity up to 120 as the omega age. The mortality rate computed is based on the age attained by the insured such that the rate for any given policy at a defined age is independent of the year an insurance
AI (Artificial Intelligence) content detection is the task of predicting if the given content is written by humans or AI. This project is a detection tool aimed at eliminating issues created by AI-generated text content such as fake academic reports and papers, articles, news, misinformation, and propaganda by combining multiple detection methods. Three models, LSTM (Long short-term memory), BERT (Bidirectional Encoder Representations from Transformers), and distilBERT (distilled Bidirectional Encoder Representations from Transformers) were fine-tuned on a small labelled dataset of 2492 rows. After comparing their performances, distilBERT was selected for further refinement. Then, a pre-trained distilBERT model was finetuned with 24034 rows of collected datasets to get results specific to the intended application. The language models in AI text generators (e.g. GPT-2) often plagiarize from the training datasets. So, to increase the accuracy BERT Classifier-based plagiarism detector was integrated into the system to determine the originality of input text and predict the likelihood of plagiarism or AI generation. The final model had an overall accuracy of 95% on the unseen data with it being able to detect 100% of all AI content in the unseen dataset and correctly classifying 90% of AI text in the unseen dataset.
AI in BIM and Digital Twins for Architecture: Building the Future, Preserving the Past
The integration of AI with Building Information Modeling (BIM) and Digital Twin technology is reshaping the architectural landscape, bridging the gap between cutting-edge construction practices and the preservation of cultural heritage. This article explores how AI-driven BIM streamlines construction through predictive maintenance, cost optimization, and real-time project adjustments, enabling the creation of efficient, resilient, and sustainable structures. Simultaneously, digital twin technology, powered by AI, offers new avenues for historic preservation by producing data-rich, 3D models that allow for precise monitoring and virtual restoration of heritage sites. By examining real-world applications in both modern buildings and historic landmarks, this article demonstrates how AI’s role in architecture not only advances building practices but also safeguards architectural history for future generations.
Comparison Analysis of Nepali News Classifier
With the growing volume of daily generated Nepali news content, which predominantly exists in unstructured formats, the need arises to effectively categorize and label this information. Considering this challenge, our system employs various Machine Learning algorithms such as Logistic Regression, Random Forest Algorithm, Neural Networks, etc., to automatically classify Nepali news into predefined categories and evaluate the efficiency of the model, which offers us a comparative analysis of these various algorithms.
Control of PV-Hydro Micro-Grid with Energy Storage System Controlled Through Bidirectional Converter
Traditional ELC for MHP system is designed in such a way that it could be used to maintain the speed of the MHP plant in an isolated micro-grid system where the excess of the electrical energy is dumped into the dummy load during lightly loaded condition. But this system seems ineffective and carries the limitations such as waste of the energy during the lightly loaded period, unstable system during the peak load period, system instability during connected with the grid system and so on. Thus, this report aims to suggest a measure for the limitations through the project,” Control of PV-Hydro micro-grid with energy storage system controlled through bidirectional converter”. All the components are designed, calibrated and synchronized with each other using the MATLAB Simulation Software. Initially, PV plant and MHP plant is designed with capacity of 50KW and 100KW respectively. Inside the PV plant inverter and inverter controller is designed in such a way that is always produces the 400V across the output terminal despite the variation in Temperature and the Irradiation. There by implementing the MPPT (Perturbation and observation (P&O)) algorithm we were able to design the PV System Next, is we have designed the MHP plant that produces the 100KW power at 400V whose excitation and rotor speed is maintained throughout the operation at any load condition. The bidirectional converter with the battery backup system is responsible for maintaining the rotor stability at any load condition i.e. either lightly loaded or heavily loaded condition. The main aim of this project is designing a system where the system will store the excess of the energy in the battery during the lightly loaded condition while maintaining the rotor stability whereas the system will then again act as a source during the peak period when the PV-Hydro system is unable to meet the load demand.
Detection of Intracranial Hemorrhage Using Deep Learning
Intracranial hemorrhage has always been a crucial medical condition where bleeding within the cranium region occurs, leading to severe neurological damage and sudden demise of a person. A patient's likelihood of survival in the treatment of Intracranial Hemorrhage is dependent on rapid diagnosis based on the radiologist's assessment of Computed Tomography (CT) scans. As a result, the requirement for precise and prompt identification of Intracranial Hemorrhage (ICH) is of utmost importance. Deep learning models can be used to assist this process by accelerating the time it takes to identify them. We built a deep learning model which will accelerate the time required to identify intracranial hemorrhages such that it facilitates the classification and segmentation of Intracranial Hemorrhage. We have constructed an EfficientNetB4 model using a Convolutional Neural Network (CNN) architecture which was used for the classification of images. This model comprises various layers categorized into different types, including convolution layers, normalization layers, fully connected layers, and activation layers. And we used Grad-Cam model for the image segmentation process which generated heat map for the image that contained the hemorrhage. We have used approximately 700,000 DICOM files collected from four international universities by the Radiological Society of North America (RSNA). We achieved an accuracy of 97 percent with learning rate of 0. 000125, batch size of 32 and 15 epochs of model training.
Efficient Resource Allocation in Fog Computing Using Multi Metric AIS Optimization
Cloud computing is a new era technology, which is entirely dependent on the internet to maintain large applications, where data is shared over one platform to provide better services to clients belonging to different organizations. It ensures maximum utilization of computational resources by making availability of data, software and infrastructure with at a lower cost in a secure, reliable and flexible manner. Fog computing is emerging as a powerful and popular computing paradigm, which extends cloud computing paradigm to enable the service execution in the edge network. The mobile and IoT (Internet of Things) applications could choose the computing nodes in both fog and cloud for resource provisioning. Generally, load balancing is one of the key factors to achieve resource efficiency and avoid bottleneck, overloaded and low-load resource usage. Load balancing is an important aspect of fog networks that avoids a situation with some under-loaded or overloaded fog nodes. Quality of Service (QoS) parameters such as resource utilization, cost, response time, performance, and energy consumption can be improved with load balancing. Recently, several studies have been conducted on load balancing in fog computing. This paper introduces an Artificial Immune System (AIS)-based load balancer for efficient resource allocation in fog computing, utilizing a multi-metric fitness function incorporating CPU and RAM availability, energy efficiency, and proximity. A mutation mechanism enhances adaptability and prevents allocation bias. the results reveal that the AIS achieves 41.67% better resource allocation efficiency compared to Random Load Balancer and up to 30.77% improvement over Least connection and Round Robin strategies. The AIS model effectively balances CPU utilization, latency, and energy consumption, providing a robust and scalable solution for dynamic fog networks.
Electricity Consumption Analysis and Prediction
In our rapidly evolving world, the demand for electricity has surged due to its vital role in driving national development across various sectors. Accurate forecasting of electricity demand is crucial for effective energy resource planning and management. The Electricity Consumption Analysis and Prediction System utilized a comprehensive dataset and employed KNN, linear regression, and XGBoost algorithms to predict electricity consumption. The data for our project has been collected from the Grid Office in Baneshwor. After meticulous data cleaning, feature engineering, and integration, XGBoost emerged as the optimal model, showcasing superior accuracy with Mean Squared Error (MSE) of 0.03, Root Mean Squared Error (RMSE) of 0.18, and an impressive R2-score of 0.95. The system effectively visualizes analyzed data and model predictions using charts and graphs, providing users with intuitive insights. The system generates predictions for electricity consumption on a monthly, daily, and yearly basis for the Baneshwor Area.
End-to-End Steering Prediction and Object Detection for Self-Driving Car using Machine Learning
In general, Self-Driving car uses a combination of numerous sensors, cameras, radars, LIDAR, high-performance processors and artificial intelligence (AI) technology to travel between destinations without the need of a human operator. This research utilizes a Raspberry pi 4 (2GB) as the processing element and a camera module connected to it acts as the input for the car. Machine learning models are used for lane detection, steering angle prediction, and object detection, focusing on traffic light and stop sign detection, to make various decisions for the car. Raspberry pi is the main part of the system with the task of processing the video provided by the camera by extracting individual image frames, detecting the road lanes and the objects in front of it, and finally taking various decisions for the car such that the car follows the lane lines and doesn't go off-track, maintains appropriate speed and turning angles, and follows traffic rules by recognizing the status of traffic lights and detecting stop sign. For the lane detection and steering angle prediction model, lane images were collected by manually controlling the car through the track lane. Those thousands of lane images were transferred to the computer, and after pre-processing, the model was trained using Google Colab. Similarly, for the object detection model, images were collected for the required classes and then used, to train a pre-trained YOLOv8 model for detecting only the few required classes of object. Finally, both the machine learning models were saved in Raspberry Pi. In the inferencing process, the latest image frame from the camera was fed into the raspberry pi after conversion into appropriate format as required by the steering prediction model (YUV Format) and object detection model (RGB Format). After interpretation of the output of these models, the decision for the car movement was sent to the motor driver module which then controlled the rotation and speed of the Battery-Operated (BO) motors. In the end, the car prototype successfully followed the predefined lane tracks and detected objects within its view, achieving near real-time inferencing at low speeds. In conclusion, this paper presents the integration of two machine learning models to develop a miniature self-driving car prototype within a resource-constrained environment capable of autonomously navigating road lanes and making precise decisions.
Extractive Nepali Question Answering System
There is a noticeable gap in language processing tools and resources for Nepali, a language spoken by more than 17 million people [1] yet significantly underrepresented in computational linguistics. We present an Extractive Nepali Question Answering System designed to generate precise, contextually accurate responses in Nepali. Addressing the lack of high-quality training data, we contribute three key datasets: a Nepali and Hindi translation of SQuAD 1.1, a Nepali translation of XQuAD for benchmarking, and a curated Nepali QA dataset derived from Belebele’s MCQ data. To mitigate translation-induced answer span loss, we utilize translation-invariant tokens, improving span retention from 50% to 93%, and evaluate translation quality using human assessment and GPT-4, confirming a faithful answer span distribution. We evaluate our models on XQuAD and our curated dataset, demonstrating the effectiveness of fine-tuning multilingual models for Nepali QA. Our best-performing model achieves an exact match (EM) score of 72.99 and an F1 score of 84.13 on XQuAD-Nepali. These results establish a strong baseline for Nepali QA and highlight the impact of utilizing cross-lingual transfer from same language family data. All datasets and code are publicly available, encouraging further advancements in Nepali NLP research.
Image Manipulation Detection and Localization
Image manipulation along with image splicing imposes a significant problem in the real world as it involves the act of maliciously combining parts from different images to create a forged image. This can lead to the spread of misinformation, deceptive visual narratives, and the decline of trust in authentic visual media. To address this challenge, a solution has been implemented by us which explores and evaluates various pre-processing methods such as ELA, JPEG compression, for the manipulation detection part and use of RGB, DCT channels along with CNN for the manipulation localization with the help of CASIA dataset. Our emphasis lies in the innovative application and integration of these methodologies to achieve a seamless and effective solution. The system takes in data in the form of an image file (JPG, JPEG, PNG), resizes it and feeds it to the machine learning algorithm. Experimental results are demonstrated that include training accuracy, validation (test) accuracy and loss.
Lumber Spine MRI Super-Resolution Using SRGAN’s
Medical imaging is essential to modern healthcare because it provides accurate visual information for diagnosis and treatment planning. However, low-resolution images are often created due to inherent limitations of imaging technology, which impairs the ability of healthcare professionals to see small details. To overcome this difficulty, super-resolution - a method of improving image resolution - has become popular. This project investigates the use of Super-Resolution Generative Adversarial Networks (SRGAN) for medical image enhancement. In practice, adversarial training SRGANs, a method based on deep learning, produces high-resolution images of their low-resolution counterparts. In several fields, including medical imaging, the model's ability to learn complex mappings between low and high-resolution image spaces has produced impressive results. In this work, we specifically discuss how SRGANs can improve the resolution of medical images to help doctors diagnose patients with greater accuracy and detail. In this paper, we have discussed about our system that increases the resolution of low-resolution MRI imaging of Lumber Spine with high accuracy.
Machine Learning in Modern Drug Discovery: Applications, Advances, and Future Horizons
The integration of machine learning (ML) into drug discovery is reshaping pharmaceutical development, addressing high costs exceeding $2 billion per drug, decade-long timelines, and success rates below 10%. This review categorizes ML applications into four domains: (1) Target Identification and Validation, where ML algorithms mine genomic and proteomic data to uncover novel therapeutic targets and validate their relevance; (2) Molecular Design and Screening, utilizing deep learning to design and optimize lead compounds, significantly reducing physical screening; (3) Predictive Toxicology and Pharmacokinetics, leveraging ensemble learning to predict safety profiles and ADMET properties with precision; and (4) Clinical Trial Optimization, employing patient stratification and real-time monitoring to streamline trials. Although ML offers transformative potential, challenges persist, including data quality inconsistencies, model interpretability, and regulatory hurdles. Emerging technologies like quantum computing, with its unparalleled molecular simulation capabilities, and generative AI, which excels in creating novel molecular entities, show promise in overcoming these obstacles. Drawing on recent advances, this review emphasizes the necessity of interdisciplinary collaboration among computational scientists, pharmacologists, and clinicians. Future directions include the adoption of federated learning for secure multi-institutional collaborations, explainable AI for greater transparency, and hybrid classical-quantum algorithms. Together, these innovations position ML as the cornerstone of next-generation drug discovery, accelerating timelines, reducing costs, and improving success rates.
Net Zero Energy Buildings (NZEBs) have emerged as a cornerstone in sustainable development, aiming to balance energy consumption with renewable energy production. This paper explores the principles, technological strategies, and practical implementations of NZEBs. By integrating insights from case studies and theoretical frameworks, the study provides a comprehensive understanding of the challenges and solutions in achieving energy efficiency in buildings. The findings aim to inform architects, engineers, and policymakers about advancing the built environment’s sustainability.
Power Generation and Storage by Piezoelectric Cells
Energy consumption is an indicator of development of the universe. Modern world needs a huge amount of electrical power to meet up the current energy demand whereas the resources of conventional energy sources are diminishing steadily in response of vast consumption of energy. So, alternate sources of energy are required to fill up the large gap between demand and supply. This paper covers the production of energy through piezoelectric effect in which certain materials have the ability to build up electricity from having pressure or strain applied to them; this can be achieved through a design of piezoelectric tile/s. When people walk, their energy gets wasted, by using piezo electric transducers (PZT) like Lead Zirconium Titanate, we can convert this wasted energy produced by the pressure of moving people into electrical energy which can be used for different purposes such as auto street lighting, emergency lighting in commercial buildings, etc.
Rainfall Prediction using Wavelet Transform and Transformers
Rainfall prediction is crucial for various applications, yet time series data in meteorological datasets poses a significant challenge. This research proposes a novel approach that combines the power of wavelet transform and transformer architecture to develop an accurate rainfall prediction model. By decomposing time series data and leveraging self-attention mechanisms, this model captures complex temporal dependencies and spatial patterns. Through extensive evaluation and comparison using different metrics, the proposed algorithm demonstrates the superiority over existing methods in terms of predictive accuracy. The proposed model has been compared with LSTM model to evaluate its effectiveness in rainfall prediction and has measured a loss of 0.060, mean absolute error of 0.05, mean absolute percentage error of 0.05 and root mean square error of 0.10. The proposed model empowers decision-makers with reliable rainfall predictions, aiding improved planning and preparedness.
Reawakening Dapcha: Bridging Past and Present for Better Future
Dapcha, a historically rich yet underdeveloped town in Nepal, faces significant socio-economic and infrastructural challenges despite its cultural heritage. This research paper, examines the current scenario and existing conditions of Dapcha through a comprehensive analysis of its historical context, urban fabric, socio-economic dynamics, and architectural heritage. The study highlights issues such as deteriorating traditional structures, inadequate infrastructure, and declining cultural practices due to urban migration and modernization pressures. By identifying critical areas of concern, the paper aims to provide a foundation for sustainable revitalization strategies that balance cultural preservation with modern development. The findings serve as a guide for policymakers, urban planners, and architects striving to create a future where Dapcha’s historical essence coexists harmoniously with contemporary progress.
Reduction of Losses and Improvement of Voltage Profile in Radial Distribution Network by Interconnection of Feeders
High penetration of photovoltaic systems is creating various issues in the distribution network such as increased power losses and violation of voltage limits. The voltage violation occurs at the point of common coupling in the network especially at midday when the photovoltaic generation is high and the loading of the network is low. The approach considered in this paper to mitigate these issues is by the interconnection of radial feeders. The interconnection of feeders allows the direct connection of areas having high photovoltaic power output to areas with high loading at midday. When the position is properly chosen, the interconnection reduces the network losses and also mitigates the voltage violation at the susceptible areas. In this paper, the CIGRE low-voltage European distribution network has been considered and its three subnetworks have been interconnected in various configurations. The interconnection minimized the daily network losses by 17.28% at the optimum case and was also successful in mitigating overvoltage in the network. The working mechanism of this approach is straightforward while giving the desired results.
RoboSort: Automated Object Sorting Robotic Arm
This paper discloses the development and functionalities of an automated robotic arm system that eases the need for heightened precision and efficiency when sorting different objects. This is accomplished utilizing a 5-degree-of-freedom robotic arm, which reduces unnecessary handwork while increasing sorting accuracy and speed. It utilizes advanced computer vision and image processing methods based on the OpenCV library and Python programming to implement analysis of an object’s live video feed and object image capture. These images were implemented in the real-time webcam and processed through YOLOv5 for effective object detection, subsequent classification and sorting based on objects’ color and shapes using robotic arm. This method facilitates faster operations with closer to zero errors than humans, especially when critical precision is a requirement.
SmartCal: Calorie Estimation of Local Nepali Cuisine with Deep Learning-Powered Food Detection
Diet observation is a critical component of preventive healthcare, yet traditional manual calorie tracking methods are often inaccurate and time-consuming. This paper presents SmartCal, an automated system for calorie estimation of local Nepali cuisine using deep learning. The system focuses on foods like rice, lentils, spinach, and boiled eggs, which are staples in Nepali diets. It employs an ESP32 Camera Module to capture food images, which are processed by a Mask R-CNN (Region-Based Convolutional Neural Network) model trained specifically for Nepali cuisine. The model achieves a mean Average Precision at 50 (mAP50) of 99%, demonstrating high accuracy in food detection. Calorie estimation is performed based on standard cooking conditions and fixed portion sizes. Results are displayed via a web application, where users can set daily calorie targets and track their intake history. This system provides a practical, accurate, and user-friendly solution for dietary monitoring, particularly suited to the dietary habits of Nepali individuals.
SPWM Inverter with Closed Loop Control System to Maintain Constant Load Voltage at Variable Load Condition
AC is the predominant form of electrical power consumption, inverters have been widely used for residential, commercial and industrial applications such as uninterruptible power supply (UPS) and AC motor drives etc. Reliable conversion of electric power is very important in these various applications. This project, “Design and Fabrication of SPWM Inverter with Closed Loop Control System to Maintain Constant Load Voltage at Variable Load Condition” aims to build a closed loop SPWM inverter to provide a reliable conversion of electric power by providing constant load voltage at variable loading conditions. This is done by constantly monitoring the output voltage of the inverter. The change in load either increases or decreases the voltage at the output side of the inverter. This change in output voltage is monitored through a feedback mechanism. The inversion of electric power is controlled through the SPWM mechanism so that the monitored effect in the output voltage is considered and essential actions are taken to keep the output voltage of the inverter constant. The design and tuning of the system are done through simulation software MATLAB Simulink and Proteus 8 professional.
Student Information System with Face Recognition Attendance
This student information system is a platform that makes it possible for schools to efficiently and transparently manage student data. Currently, handwritten logs and paperwork are used to manage student records, which leads to various issues like data loss, data redundancy, data inconsistency, and other human errors in addition to being time-consuming and inaccurate. Our student information system can significantly reduce the majority of these issues. The system we have built is a student information system that holds a range of student data, such as academic, attendance, and personal data. This system makes use of deep learning and facial recognition to automate the attendance process. Facial recognition is chosen as the method of authentication and CNN algorithm is used for higher accuracy, robustness and in-variance. It uses the student entering time and leaving time to create a data log which is used to calculate the total time attended by the student. This system will also analyze the entry and exit times of students on a particular day and display the students that are present and absent in real time. The system will then use this information to display various charts about the student attendance and academics using a user-friendly interface with three different user modes: teacher, student and admin.
The Integrated Conservation and Tourism Masterplan of Tansen
The Integrated Conservation and Tourism Masterplan of Tansen is a comprehensive project aimed at preserving the historical, cultural and architectural heritage of Tansen, Palpa, while promoting sustainable tourism and economic growth. Tansen, a historic hill town, is renowned for its unique blend of Newari and Magar cultural influences expressed through its rich tangible and intangible heritage including traditional architecture, religious monuments, vibrant festivals and artisanal crafts. Extensive research, mapping and documentation were conducted to identify Tansen’s key assets and challenges. Guided by the concept "Tansen’s Fading Charm: A Call for Revival," the masterplan seeks to restore Tansen’s identity through heritage conservation, adaptive reuse, and sustainable tourism strategies. The design phase emphasizes the restoration of historical structures such as temples, heritage residence etc. with minimal intervention to preserve authenticity and integrity. In addition, adaptive reuse strategies are employed including converting underutilized buildings into homestays, boutique hotels, craft workshops and community spaces. Innovative public spaces like tudikhel, tin dhara and fulbari is revitalized ensuring community engagement and tourist attraction. The masterplan integrates new infrastructure design with heritage sensitivity, incorporating waste management systems, illumination plans for safety and aesthetics. By combining conservation principles with innovative tourism strategies, the project seeks to provide a comprehensive framework for balancing heritage conservation with modern economic needs, offering a sustainable model for community-driven historical preservation and regional upliftment.
Transmission Line Monitoring Using Computer Vision & AI
Unmanned aerial vehicles (UAVs)with artificial intelligence (AI) can provide a revolutionary solution for the monitoring and inspection of power transmission lines. This study employs the YOLOv5 deep learning model to detect faults from custom datasets for the context of Nepal, where rugged terrains impede the feasibility of inspections. Some major faults we are proposing to inspect are broken insulators, vegetation encroachment, conductor sag, and corona losses. We used 950 bounding boxes in 400 images annotated manually, and augmented data was used for model robustness. The evaluation demonstrated the system was precise and accurate, demonstrating the system has the potential to reliably detect a fault. First, this research advances the state of the art in AI-driven infrastructure monitoring by proposing a scalable, efficient, and context-aware system for enhancing Nepal’s energy transmission reliability.
Virtual Oscillator Control Strategy for Parallel Operated Inverter in an Isolated Microgrid
This study focuses on the application of Virtual Oscillator Control (VOC) to enhance the stability and performance of isolated microgrids in renewable energy systems. VOC operates by generating periodic signals internally using principles of nonlinear oscillation, such as the Lienard theorem and the Van der Pol oscillator. This approach eliminates the need for real-time grid measurements, enabling faster response times. The research investigates VOC's implementation in both single-phase and three-phase microgrids, analyzing its impact on system stability and performance. Simulation models include microgrids with multiple VOC inverters for power-sharing and systems combining VOC with PV inverters to evaluate compatibility. The results highlight VOC’s ability to improve stability, enable effective power-sharing, enhance transient recovery, and reduce response times, demonstrating its potential as a robust solution for grid-forming inverters in renewable energy integration.
Dear Readers, Welcome to the latest edition of the KEC Journal of Science and Engineering (KJSE), Office of KEC Research and Publication (OKRP), a vital resource for researchers, practitioners, and enthusiasts alike. As an Editor-in-Chief, I am both privileged and honored to oversee a platform dedicated to bridging the gap between theoretical advancements and practical applications in the dynamic field of engineering. Engineering is at the forefront of progress, shaping the world through innovative technologies and sustainable solutions. From artificial intelligence and structural integrity to nanotechnology, engineering influences nearly every facet of modern life. Our mission is to showcase the vast potential of engineering in addressing global challenges, enhancing quality of life, and inspiring future generations. Each edition of our journal reflects the dedication and intellectual rigor of our contributors, who constantly push the boundaries of what is possible. Through comprehensive research, thought-provoking reviews, and compelling editorials, we aim to spark meaningful discussions, foster interdisciplinary collaboration, and set new benchmarks for excellence. As engineers, we shoulder the significant responsibility of creating solutions that are not only groundbreaking but also ethical and inclusive. Our journal is committed to promoting these principles, ensuring that the knowledge we share contributes to advancements that benefit society as a whole. In this edition, you will find a diverse range of articles that reflect the multifaceted nature of engineering. Our contributors have crafted thoughtful works across various disciplines, including mathematics. Each article embodies the collaborative spirit and intellectual curiosity that define our institution’s scholarly pursuits. Our authors have skillfully navigated complex landscapes, offering valuable insights that resonate with both academia and industry. We are also excited to feature an exclusive section on student projects, highlighting the creativity and ingenuity of our talented engineering students. These initiatives not only demonstrate their technical prowess but also showcase their ability to think critically and collaborate effectively. The passion for learning and the drive for excellence shine through in every contribution, illustrating the immense potential of our student body. I extend my sincere gratitude to all our faculty advisors, editorial board members, and all contributors who have brought this publication to life. Your unwavering support and active engagement are essential to advancing the journal’s mission. As we continue to explore the new horizons, challenge conventions, and innovate, I encourage you to engage with the content, share your feedback, and offer your insights. Together, let us continue to inspire and motivate one another in our collective pursuit of engineering excellence. I invite you to immerse yourself in the wealth of knowledge within these pages and share this journal with your peers and colleagues. Thank you for being an integral part of our journey toward engineering a brighter future. Thank You. Sincerely, Pratima Pradhan, Phd Editor-in-Chief Office of KEC Research and Publication (OKRP) April 2025