This study presents a smart grid architecture addressing challenges in renewable energy integration, including limited data use, fragmented analysis, and weak real-time control. The proposed multi-layer framework combines data input, multidimensional analysis, AI optimization, and output evaluation to improve performance. A MATLAB simulation models interactions among load demand, solar and wind generation, battery storage, and grid supply over 24 hours. Results show renewables contribute 60–75% of power, reducing reliance on fossil fuels. Battery storage balances supply and demand, while real-time monitoring, demand response, and automation enhance stability. Energy efficiency reaches about 92%, with lower transmission and distribution losses. Reliability indices (SAIDI 0.02–0.05 hours per customer and SAIFI 0.01–0.03 interruptions) indicate strong system resilience. Economic analysis shows savings of $60,000–$80,000 from reduced fuel use despite higher upfront costs. Environmental benefits include major carbon emission reductions. Socioeconomic impacts include job creation and improved energy access. Overall, integrating renewable energy with advanced smart grid technologies offers a sustainable, efficient, and economically viable solution that supports long-term energy resilience and climate objectives. This approach also enhances system flexibility by enabling adaptive control strategies under varying load and generation conditions, ensuring scalability and future integration of emerging energy technologies and digital grid innovations worldwide adoption.
This article examines the behavior of the quadri spectral method in the design of a pyrometer applicable to the heat treatment of metals. The quadri spectral pyrometer incorporates four different optical filters that filter the four spectra to be used and converge them towards the four detectors of the device. The light energy from these spectra will be converted by the detectors into a processable electrical signal. The application of the nonlinear model known as Temperature by Nonlinear Model (TNL) will calculate and select these four wavelengths. This method applies inverse calculus, exploiting Planck's relation for thermal radiation by setting the temperature and then determining the wavelengths using ordinary least squares. With this model, the four wavelengths will be selected sequentially by modeling the emissivity of the metal as a second-degree polynomial. The obtained wavelengths will be subjected to various criteria to choose the best groups for a suitable pyrometer intended for high-temperature metal treatment. Those criteria are flux sensitivity to wavelength and temperature, standard deviation at temperature, and the minimum difference between two successive wavelengths. The various tests against the criteria, given the non-linearity of the emissivity of metals, characterize the model in high temperatures in order to proceed with such a pyrometer design.
Optimizing alternating current (AC) power flow under uncertainty remains a major challenge in modern power systems, particularly with the increasing penetration of variable renewable energy sources. This paper proposes a hybrid two-stage framework that integrates long short-term memory (LSTM) networks for load forecasting with an artificial immune system (AIS)-based optimization approach, embedded within a Monte Carlo simulation scheme to explicitly account for uncertainty. The methodology is validated on the IEEE 30-bus test system. In the first stage, the LSTM model captures temporal dependencies to generate short-term load forecasts, while in the second stage, these forecasts are incorporated into an AIS-based AC optimal power flow (AC-OPF) formulation. Monte Carlo simulations are employed to model stochastic variations and assess system performance across multiple scenarios. The results show that, although the reduction in operational cost is relatively marginal compared to deterministic approaches, the proposed framework significantly enhances the robustness and stability of OPF solutions under forecasting uncertainty, improving the system’s ability to maintain feasible and consistent operating points despite variability in load predictions. However, the forecasting performance of the LSTM model is sensitive to noise and out-of-distribution inputs, which may affect the overall optimization quality. Overall, the main contribution of this work lies in the development of an integrated forecasting–optimization framework that strengthens the reliability and resilience of power system operation under uncertainty.
This article investigates the limitations of pyrometric technology in the visible and ultraviolet spectral bands. These studies rely on a theoretical analysis of thermal radiation, Planck's fundamental laws, and multispectral processing methods based on nonlinear models. Accurate high-temperature measurement is a major challenge in many scientific and industrial fields, including thermal processes, metallurgy, energy, and fundamental research. Uncertainty in emissivity is the main source of error in conventional pyrometric measurements. To reduce the influence of emissivity and improve measurement reliability, several approaches have been developed, including monochromatic, bichromatic, and multispectral pyrometry based on Planck's law. The chromatic luminance characteristics in the visible and ultraviolet bands, obtained from a temperature range across both spectral domains, highlight the high potential of pyrometry for measuring high temperatures in complex environments. These characteristics will be applied with different wavelengths in each visible and ultraviolet spectral band. Comparative studies of the results will be able to highlight the limitations for each band. Compared to traditional approaches, this pyrometry technology offers a small advantage for detecting very high temperatures despite variations in emissivity and environmental uncertainties. The luminance for these two spectral bands exhibits a very low flux almost at temperatures below 1900K.
Grid-connected renewable energy systems often suffer from power quality (PQ) issues such as harmonic distortion and poor voltage regulation due to the integration of power electronic interfaces and non-linear loads. This paper proposes a hybrid control and optimization approach to enhance PQ in a grid-tied renewable system using an Active Power Filter (APF) integrated with fuel cell technology. A Proton Exchange Membrane Fuel Cell (PEMFC) provides a clean DC source to support the APF, supplying real power for harmonic and reactive compensation. The APF is controlled via a two-level hybrid strategy: an intelligent controller (adaptive neuro-fuzzy or fuzzy-PI) maintains the DC-link voltage and coordinates fuel cell output, while a fast inner-loop current control (based on synchronous reference frame theory and hysteresis PWM) injects compensating currents. A Particle Swarm Optimization (PSO) algorithm is employed offline to fine-tune controller parameters for optimal Total Harmonic Distortion (THD) reduction and dynamic response. Simulation case studies demonstrate that the proposed system significantly improves PQ: source current THD is reduced from about 25% (without compensation) to under 3% with the hybrid APF, complying with IEEE-519 standards. The fuel cell-integrated APF also corrects power factor to ~0.99 and provides voltage support during disturbances. The results highlight the effectiveness of combining fuel cell distributed generation with advanced control and optimization techniques for maintaining high power quality in renewable-rich grids.
Road Traffic Accidents (RTA) have been a major cause of death and life-threatening injuries globally. The delay in Emergency Response Services (ERS) has heightened the number of death casualties in the event of motorcycle accidents. To address these life-threatening issues, the Smart Helmet for Accident Detection and Res communication device (SHADR) was developed to automatically detect accident, notify the registered emergency contact about the incident and disclose the location of the incident. This concept is designed for rural communities where motocycling activities are predominant. The design was actualized by deploying an accelerometer to detect accident, a load sensor to define when the helmet is being worn, GPS module to ascertain the exact location of the incident, GSM module for call activation and delivering short message service (SMS) of the emergency immediately after the incident has occurred. It leverages the user the opportunity to register the emergency contact by sending information as a coded text to the SHADR. This therefore eliminates the need for interface components and reduces the power consumption level of the device. The outcome of the design implementation demonstrated an efficient operation, with a fast response time for the GPS and GSM communication. Its major contribution stems from the fact that the response time was adequate since it was not affected by network delays and failures associated with communication systems in rural communities. It is a cost effective device which operates with minimum power consumption, the SMS delivery time was adequate and the call functionality was good, at minimum network connectivity. The implementation of SHADR on motorcyclists will greatly reduce casualties from road traffic accidents, provide more data for road traffic studies and give more confidence to road users. Future improvements will require the implementation of this device using 5G technology to improve communication speed and reduce latency for emergency services in urban communities.
A detection system is an electronic device that uses electromagnetic waves to determine the altitude, range, direction, or speed of objects, whether moving or stationary. In contrast, ultrasonic waves are used instead of electromagnetic waves in ultrasonic detection system. It has many advantages. Its low power consumption, low cost, and ease of implementation and use make it well-suited for various applications, including security systems, object detection and avoidance systems in robotics. In this paper, a low-cost ultrasonic detection system using Arduino microcontroller and processing software was developed. This system makes measurements of distance, direction or speed of both moving and fixed object. The ultrasonic sensors measure the distance to target objects using non-contact technology. They provide accurate distance measurement without causing damage and are easy to use. The sensor receives signals in analog form, which are then converted to a digital format and processed by a microcontroller. The detection distance of the proposed system is tested up to 400 cm for different types of objects: fabrics and aluminum. The distance error between the system and the objects were determined. The results obtained for all types of objects prove that a very low error can be achieved using the proposed design.
This research is based on a theoretical analysis of thermal radiation, the fundamental laws of Planck and Kirchhoff, and multispectral processing methods using nonlinear models. Accurate high-temperature measurement is a major challenge in many scientific and industrial fields, including thermal processes, metallurgy, energy, and fundamental research. Planck’s law gives de relation between radiation, temperature ant wavelength. That low can be used to determine temperature by pyrometry method. Besides Planck’s law for the black body multiplied by the emissivity will gives the expression of the real body. Uncertainty in emissivity is the main source of error in conventional pyrometric measurements. In our case, the polynomial model of emissivity only goes up to the second order. To reduce the influence of emissivity and improve measurement reliability, several approaches have been developed, including monochromatic, bichromatic, and multispectral pyrometry based on Planck's law. The characteristics of chromatic luminance in the near and mid-infrared bands highlight the high potential of pyrometry for measuring high temperatures in complex environments. Compared to traditional approaches, this pyrometry technology offers greater robustness to variations in emissivity and environmental uncertainties. Luminance across the infrared spectral band and a temperature range provides improved linearity. This linearity highlights the strength of using infrared radiation for remote temperature sensing. The mid-infrared zone offers greater stability and a closer relationship between luminance, temperature, and wavelength in temperature detection for oxidized steel.
Road accidents are considered one of the major factors for injuries and deaths due to the delayed response of emergency services and driving conditions. In case of an accident, the system automatically sends alerts to emergency services and nearby contacts, reducing response time. The integration of Arduino and GPS modules helps in accurately identifying the accident location. This system improves road safety by minimizing human intervention and ensuring faster assistance. Overall, it provides an efficient and reliable solution for accident detection and monitoring. This is paper proposes an IoT-based smart vehicle accident detection and driver alcohol monitoring system using an Arduino microcontroller. The proposed IoT-based smart vehicle accident detection and driver alcohol monitoring system uses an accelerometer sensor for detecting accidents and an alcohol sensor for monitoring the driver. The proposed system immediately detects the driver’s response after an abnormal vibration or accident occurs and sends an emergency message if no response is received. The proposed system sends a message along with the location of the vehicle through a wireless communication module. Recent advances in Internet of Things (IoT), wireless communication, and embedded systems have led to the development of advanced accident detection and monitoring systems to automatically detect accidents occurring on the roads. Accidents occurring on the roads can be detected through various technologies such as accelerometers, vibration sensors, GPS modules, and communication devices. The proposed system displays the current status of the vehicle using a 16×2 LCD display. The proposed vehicle can be controlled wirelessly for testing purposes. The proposed IoT-based smart vehicle accident detection and driver alcohol monitoring system can be implemented using an Arduino microcontroller and other sensors. Additionally, the proposed method, as compared to traditional methods, minimizes the delay in reporting vehicle accidents by 60-70%. Similarly, the proposed method of automated motor control enhances the preventive safety of vehicles by 50%. Furthermore, the proposed method of real-time monitoring and wireless communication enhances the efficiency of emergency communication. The proposed IoT-based smart vehicle accident detection and driver alcohol monitoring system is compact and can be implemented for various purposes.
In light of the rapidly increasing global demand for energy, accurately forecasting short-term electricity consumption has become a critical yet challenging task for modern power system operation and planning, as traditional methods often struggle to handle the variability and uncertainty of load demand. To address these limitations, this study proposes an integrated and data-driven framework that combines an Alternating Current Optimal Power Flow (ACOPF) model with a Long Short-Term Memory (LSTM) recurrent neural network in order to simultaneously enhance forecasting accuracy and operational efficiency. The LSTM model, trained on historical load data, is used to generate reliable 24-hour ahead electricity demand forecasts, which are then dynamically incorporated into the MATPOWER simulation environment using the IEEE 30-bus test system. The results demonstrate that the proposed approach achieves a high level of predictive performance, with a root mean square error (RMSE) of 0.3794, indicating its effectiveness in capturing temporal load patterns. More importantly, the integration of these forecasts into the ACOPF framework enables more proactive and informed decision-making in power system operations, leading to a significant improvement in economic dispatch by reducing the hourly generation cost from $576.89/h under conventional approaches to $490.91/h, corresponding to an approximate cost reduction of 14.9%. Overall, this study highlights the strong synergy between deep learning techniques and optimization models for smart grid management, showing that the proposed framework not only improves forecasting precision but also enhances system efficiency, reduces operational costs, and supports more reliable, flexible, and cost-effective power system operations.
The article presents and analyzes the results of the development and application of the methodology for structural analysis and the CompressorWI-2S program for modeling the process of "wet compression" of air in gas turbines and axial compressors, taking into account the multifactorial characteristics of this process. This includes the selection of the location of the liquid flow path in the compressor. liquid separation on the inner walls of its housing, the presence of an internal or external bypasses (recovery) of the two-phase compressible pressure fluid section, the possibility of injecting "super-heated" liquid, incomplete wetting of the surfaces of the blades of the impellers blades (IB) and stage guide vanes (SGV) of individual stages, control of the compressor operating modes at low speeds using SGV rotary blades of some stages. The simulation results of the CompressorWI-2S were based on the 14-stage axial compressor of the AL-21F-3 gas turbine engine, for which experimental and bench test data were published in the scientific literature. We were able to determine the most significant and minor factors based on the results of the calculation, which varied a few compressor parameters and the conditions of the water injection into the flow path. The characteristics of the axial compressor under wet compression have been determined by combining it with other heat and gas dynamic calculation programs for gas turbine engines. In terms of performance and flow characteristics at the compressor outlet, the simulation results are in line with the bench test and experimental data for this axial compressor. As a result of, the research, it was established that the determining role in the process of moisture evaporation in the flow section of an axial compressor is played by thermodynamic factors, such as changing pressure and temperature of the working fluid (two-phase mixture or steam-air mixture), corresponding changes in the heat of vaporization in the flow section of the compressor, as well as the liquid injection flow rate.
This study proposes an algorithmic approach for the development of a bio-inspired prosthetic hand system controlled by surface electromyographic (EMG) signals, aiming to achieve natural, adaptive, and continuous motion in upper-limb prostheses. The proposed framework integrates biomedical signal processing, machine learning–based motor intention decoding, and embedded mechatronic control within a unified system. Multi-channel surface EMG signals were acquired from the forearm and processed through a dedicated pipeline including amplification, physiologically relevant filtering, feature extraction, and normalization. To infer motor intention, two learning paradigms were investigated and compared: a classical Support Vector Machine (SVM) using handcrafted EMG features, and a Long Short-Term Memory (LSTM) neural network designed to perform continuous regression of finger joint angles corresponding to the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints. While the SVM provided a baseline for gesture-related decoding, the LSTM demonstrated a clear advantage by explicitly modeling temporal dependencies and non-linear relationships in sequential EMG data, resulting in more accurate and temporally coherent kinematic predictions. Experimental validation was carried out on a custom bio-inspired prosthetic prototype equipped with potentiometric joint feedback, showing that the LSTM-based controller achieved higher prediction accuracy and smoother real-time control during representative gestures such as flexion, extension, and grasping. Furthermore, deployment using TensorFlow Lite confirmed the feasibility of embedding deep sequential models on low-power hardware platforms. Overall, this work highlights the importance of temporal modeling for EMG-driven control and establishes a robust foundation for neural-controlled prosthetic systems that combine signal intelligence, physiological relevance, and embedded optimization, contributing to the advancement of human–machine interfaces aimed at restoring dexterity and autonomy in amputee patients.
Maintaining power quality in the power distribution network is a major concern due to the increasing penetration of nonlinear and complex equipment. Unified Power Quality Conditioners (UPQCs) have been widely used as effective compensating devices to mitigate voltage instability and current distortions. However, the major challenge lies in selecting the optimal location and rating of the UPQC in the distribution network. Proper placement of the UPQC significantly improves overall system efficiency by enhancing the voltage profile, reducing active power losses, and improving cost effectiveness. In this study, the Hunter-Prey Optimization (HPO) algorithm is employed to determine the optimal location and rating of the UPQC in the distribution network. The objective function combines active power loss, voltage deviation, and UPQC installation cost while satisfying network and control constraints. The proposed framework is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate that the HPO algorithm efficiently identifies the optimal UPQC placement and rating, resulting in a significant reduction in active power losses of 60% for the IEEE 33-bus system and 93.5% for the IEEE 69-bus system, along with a notable improvement in voltage profiles compared to the system without UPQC.
This paper investigates the transition of traditional electricity transmission systems into modern, low-carbon network essential for mitigating climate change and ensuring energy sustainability. The electricity sector remains a major contributor to global greenhouse gas emissions, making transmission modernization critical for large-scale integration of renewable energy sources such as solar, wind, and hydro. This study proposes a comprehensive carbon-aware control framework that integrates smart grid technologies, energy storage systems, and dynamic optimization models to enhance grid efficiency, reliability, and emissions performance. Using Ghana's power system as a case study, the research develops a MATLAB-based simulation of a 10-bus transmission network incorporating real-world generation data, load forecasting, and geographical analysis of renewable potential. Results indicate that integrating renewable energy with energy storage can reduce CO2 emissions by up to 50%, from 238,000 kg to 119,000 kg, though economic viability remains challenging without policy support, subsidies, or carbon credits. The simulation also highlights the role of energy storage in smoothing intermittent generation and maintaining system stability. Financial analysis and load growth projections reinforce the need for scalable investment models and regulatory reforms to support long-term de-carbonization. The proposed framework bridges the gap between emissions metrics and grid operations, offering a robust tool for policy makers, utilities, and researchers. The findings demonstrate that a low-carbon grid is both technically feasible and environmentally necessary for a sustainable energy future.
As a critical component of railway systems, existing trackside equipment relies on centralized indoor power supply screens for power. However, this configuration suffers from inherent drawbacks such as excessive cable lengths, high deployment costs, and significant voltage fluctuations. To address these issues and adapt to the distributed control scenarios of urban rail transit, this paper proposes a safety-oriented, digitally controllable AC/DC conversion circuit design tailored for trackside installation and miniaturization. Adhering to the "fault-safety" principle and "two-out-of-two" redundancy architecture, the circuit converts mains AC220V to adjustable DC output ranging from 24V to 200V. The module integrates a dual-processor control unit, power conversion circuit, voltage/current acquisition circuit, and weak current voltage conversion circuit. Key design features include electrical isolation via a high-frequency transformer, enhanced power conversion efficiency through phase-shifted full-bridge control, real-time monitoring of input/output voltage, output current, and board temperature, and bidirectional real-time communication with external devices. Notably, the circuit is designed to fail safely: in the event of abnormal acquisition signals or hardware malfunctions, the system automatically switches to a safe state with no power conversion output. To validate the design feasibility, a 1kW experimental prototype was fabricated and tested, with results confirming the effectiveness of the proposed solution.
Energy theft poses a significant challenge to modern power systems, leading to economic losses, reduced efficiency, and compromised reliability in smart grids. Detecting such anomalies requires robust, scalable analytical frameworks that can accurately distinguish normal consumption, marginally increased usage, and patterns indicative of electricity theft across diverse operating conditions. This study investigates the application of machine learning techniques for energy theft detection using a dataset of recorded consumption values. Two numerical features, energy used by theft in per unit and normal energy, were employed as predictors. At the same time, the target variable comprises three categorical conditions: Theft detected, Normal, and Energy slightly higher. Four classifiers were implemented and compared: Decision Tree, Support Vector Machine (SVM) with Error-Correcting Output Codes (ECOC), Random Forest, and k-Nearest Neighbors (kNN). The models were trained and evaluated using MATLAB with an 80/20 hold-out validation approach. Performance was assessed using accuracy metrics and confusion matrices. Results demonstrated that SVM achieved the highest accuracy (86.67%), followed closely by Random Forest (83.33%) and kNN (82.33%), while Decision Tree yielded the lowest accuracy (73.33%). Confusion matrix analysis showed that all classifiers detected theft-based cases with high accuracy, whereas most classification errors arose from overlap and ambiguity between normal consumption and elevated energy usage conditions. The study adds to the expanding literature on data-driven energy management by providing practical evidence of how machine-learning techniques can strengthen grid security, minimize financial losses, and enhance overall operational efficiency.
Reverberation chamber (RC) is a new testing environment in the field of electromagnetic compatibility measurement. Compared with the traditional testing environments, the RC has obvious advantages in improving testing efficiency and generating high E-field strength with relatively low input power. However, there are inconsistent problems in the test results of the radiation sensitivity threshold of the equipment in the reverberation chamber and the uniform field environment obtained by using the test methods in the current standards. Therefore, studying the equivalence of radiation sensitivity thresholds between the two has become a hot issue at present. Since most frequency-using devices in the electromagnetic environment can be equivalent to an antenna, in this paper, the dipole antenna under the compound field of the boundary deformation reverberation chamber and the uniform field of the anechoic chamber (AC) is taken as the research object to study the equivalence of antenna coupling power under these two conditions. Firstly, the coupling models in the anechoic chamber and reverberation chamber environments were established. The formulas for the antenna coupling power and the spatial radiation field strength under different radiation conditions were derived, and the power correlation coefficients of the two under the same sensitivity threshold were obtained. Subsequently, the electromagnetic environment of the reverberation chamber was constructed by using the Monte Carlo method, and the influence law of random plane waves with different column numbers on the coupling power of the antenna was simulated and analyzed. Finally, the equivalence factor of the coupling power of the two when the threshold of the radiation sensitivity test is the same in the reverberation chamber and the anechoic chamber is obtained.
As the core power supply unit of the railway communication signal system, the operational stability and performance reliability of the IPU power supply are directly related to railway transportation safety. The railway signal field has extremely stringent requirements for high reliability, high stability, and adaptability to extreme operating conditions of power supply equipment. Addressing the technical bottlenecks of traditional IPU power supply testing, such as complex environmental setup, high manual intervention, time-consuming non-coplanar interface docking (5-8 minutes for single device preparation), incomplete test coverage (lack of voltage/load boundary scenario testing), and poor result consistency, this paper proposes a fully automated testing method for IPU power supplies tailored to railway signal scenarios. This method innovatively adopts a technical architecture of "moving spring probe docking + programmable excitation + multi-dimensional monitoring". Relying on the automatic alignment and elastic fitting characteristics of customized moving spring probes, combined with the bidirectional fixing mechanism of electric cylinders, it achieves high-speed and precise docking of multiple interfaces. Through programmable power supply/load generation of rated and boundary voltage and multi-load combination excitation, coupled with a 16-bit high-precision ADC acquisition circuit, a data acquisition system is constructed. Integrating image recognition technology based on HSV color threshold segmentation, it completes visual monitoring of lamp position status and screen parameters, and integrates a "recognition-recording-retry-alarm" fault adaptive processing mechanism to enhance the stability of the testing process. Experimental verification results show that the testing time for a single IPU power supply is reduced from 15 minutes to 4 minutes, a 60% reduction compared to traditional methods. The fault recognition accuracy rate is over 98%, supporting parallel testing of 10 devices, with a batch testing efficiency increase of 73.3%. It comprehensively covers key performance indicator testing scenarios. This solution effectively eliminates manual operation errors, improves the standardization and traceability of the testing process, and provides efficient and reliable technical support for the quality control of IPU power supply mass production, meeting the stringent application requirements of railway signal equipment.
The growing number and sophistication of cyberattacks against financial institutions have underscored the need for a more robust cybersecurity framework in Nigeria’s banking industry. This research examines the implementation of Zero Trust Architecture (ZTA), a contemporary security model that focuses on identity authentication, the principle of least privilege, and micro-segmentation to mitigate threats. The primary objectives were to assess the level of adoption of ZTA principles in Nigerian banks, identify the challenges associated with ZTA implementation, and evaluate the impact of ZTA on cybersecurity resilience and regulatory compliance. A quantitative research approach was employed, and structured questionnaires were used to gather data from IT and security professionals, banking personnel, and regulators. The collected data were analyzed using statistical techniques, such as SPSS, to generate descriptive statistics. The findings indicate a moderate level of implementation, with high adoption of identity verification practices but low adoption of more advanced practices, such as micro-segmentation and real-time authentication. Notable barriers include high implementation costs, difficulties integrating with legacy systems, and a shortage of cybersecurity professionals. However, despite these difficulties, it was found that ZTA had a positive impact on banks' ability to detect and address cyber threats, as well as enhance compliance with regulatory standards, such as the NDPR, PCI DSS, and SWIFT CSP. The research highlights the need to adopt a phased, strategic approach to ZTA integration, bolster regulatory support, and enhance capacity building. This research contributes to the growing body of knowledge in the field of cybersecurity in emerging economies, providing practical recommendations for policymakers and financial institutions.
The increasing demand for efficient power regulation in embedded systems, renewable energy, and portable electronics has elevated the importance of DC-DC converters in modern power electronics. Among them, the buck converter a type of step-down converter is widely favored due to its high efficiency, compactness, and suitability for low-voltage applications. This study was undertaken to explore the design, simulation, and hardware implementation of a buck converter capable of stepping down a 12 V DC input to output levels of 3 V, 6 V, and 9 V based on duty cycles of 25%, 50%, and 75%, respectively. The objective was to validate theoretical predictions using both simulation and physical testing. The circuit design incorporated essential power electronic components such as IRF9530 MOSFETs, IR2110 driver IC, inductors, capacitors, and freewheeling diodes, assembled on a Vero board for prototype development. Proteus simulation results closely followed expected voltage levels, while hardware testing showed minor deviations due to non-idealities like component tolerances, switching losses, and thermal effects. The comparison between theoretical, simulated, and measured outputs confirmed the operational integrity and efficiency of the design. In addition to the technical focus, this work considers safety measures, ethical responsibility, and environmental impact ensuring the converter's alignment with modern sustainable engineering practices. This project not only demonstrates the functional reliability of buck converters in real-world scenarios but also contributes to students’ hands-on learning and fosters innovation in scalable energy systems.
