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Eco-friendly innovation: green energy empowered by IoT

10.11591/ijape.v14.i4.pp903-911
Nikita Amoli , Jitendra Singh , Rahul Mahala , Rajesh Singh , Anita Gehlot , Mahim Raj Gupta
Energy demand is high globally, impacting daily life and promoting sustainable modernization. Goal 9 aims to build an elastic framework for economies, while Goal 15 of the Sustainable Development Goals (SDGs) emphasizes the preservation of terrestrial environment, sustainable woodland management, and biodiversity conservation. The International Energy Agency predicts a significant increase in global renewable capacity, with solar PV being two-third of this growth. Green technology is crucial to combat global warming and Industry 4.0, a digital transformation that aims to create a strong framework for sustainable modernization. The growth of the smart grid is vital, involving energy sources, control techniques, computation, generation, transmission, distribution, and more. Supercapacitors store and deliver energy at high capacity, while green energy transforms fossil fuels into eco-friendly sources using natural resources like hydro, solar, wind, thermal, and biomass. This study explores the efficient use of microprocessors in solar and wind energy, as well as the application of actuators in the green energy sector. Green energy is a sustainable solution to increasing energy needs, reducing dependence on fossil fuels. IoT technologies, including sensors, actuators, microprocessors, and microcontrollers, are used in energy generation, transmission, distribution, and composition.
Volume: 14
Issue: 4
Page: 903-911
Publish at: 2025-12-01

Efficient design of approximate carry-based sum calculating full adders for error-tolerant applications

10.11591/ijict.v14i3.pp1189-1198
Badiganchela Shiva Kumar , Galiveeti Umamaheswara Reddy
Approximate computing is an innovative circuit design approach which can be applied in error-tolerant applications. This strategy introduces errors in computation to reduce an area and delay. The major power-consuming elements of full adder are XOR, AND, and OR operations. The sum computation in a conventional full adder is modified to produce an approximate sum which is calculated based on carry term. The major advantage of a proposed adder is the approximation error does not propagate to the next stages due to the error only in the sum term. The proposed adder was coded in verilog HDL and verified for different bit sizes. Results show that the proposed adder reduces hardware complexity with delay requirements.
Volume: 14
Issue: 3
Page: 1189-1198
Publish at: 2025-12-01

Frequency control of hybrid power system with fractional order secondary controller using improved biogeography-based krill herd algorithm

10.11591/ijape.v14.i4.pp816-825
Kukkamalla Kiran Kumar , Gobinathan Balaji , Kanta Rao Pedakota , Majahar Hussain Mahammad , Syed Suraya
To meet the demand of electrical power, structural changes of the power system from the generation side are necessary by integrating the renewable sources into the existing system. In the presence of renewables, the active power imbalances caused by both generation and demand are reduced with the classical units (like thermal) since the wind speed and irradiance (inputs of wind and solar plants) are volatile and nonlinear in nature. The frequency deviations triggered by such active power imbalances of the hybrid power system integrated with both conventional and renewable energy plants are minimized with better secondary control schemes. Therefore, this article suggests fractional order secondary controller (FOSC) for conventional units of the interconnected power system to strengthen the frequency stability of the system during the demand perturbations. The optimal gains of the FOSC are identified with an improved biogeography-based krill herd optimizer with the help of the performance indicator integral square error. To elevate the improvements of FOSC, comparisons are provided with classical controllers during the simple, random load perturbations with and without generation changes. Furthermore, sensitivity analysis on system parameters is performed to show the robustness of the FOSC over classical control strategies.
Volume: 14
Issue: 4
Page: 816-825
Publish at: 2025-12-01

Power smoothing in electrical distribution system using covariance matrix adaptation evolution strategy of aquila optimization

10.11591/ijape.v14.i4.pp842-858
Smrutirekha Mahanta , Manoj Kumar Maharana
This study introduces a novel hybrid optimization approach covariance matrix adaptation evolution strategy of aquila optimization (CMAESAO) to enhance power smoothing and minimize power losses in electrical distribution systems through the optimal allocation of D-STATCOMs. The method is tested on standard 33-bus and 69-bus systems. The CMAESAO algorithm efficiently identifies optimal locations and sizes of D-STATCOMs to achieve system performance improvements under constant power (CP), constant current (CC), and constant impedance (CI) load models. The results show that, for the 69-bus system, installing two D-STATCOMs yields optimal performance, reducing real power loss from the base value to 149.6368 kW, while three D-STATCOMs yield a slightly better voltage profile and VSI but only marginal additional power loss reduction (147.8951 kW), making two units more cost-effective. For the 33-bus system, three D-STATCOMs provide the best improvement in power quality and loss minimization. Voltage and current profiles confirmed improvement in voltage stability and reduced branch currents with optimized placements. Compared to other optimization techniques, CMAESAO demonstrates faster convergence and superior accuracy in minimizing losses, establishing its effectiveness for such multi-objective optimization problems. The study's novelty lies in integrating CMA-ES with aquila optimization to combine strong global search with adaptive exploration, resulting in robust and efficient power system enhancement. The proposed methodology contributes to smarter, more reliable distribution systems, supporting grid resilience and energy efficiency.
Volume: 14
Issue: 4
Page: 842-858
Publish at: 2025-12-01

PD characteristics of polymer insulation for inverted-fed drives under sine and square waveforms

10.11591/ijape.v14.i4.pp893-902
S. Narasimha Rao , Elanseralathan Kasinathan , Ramanujam Sarathi
In recent years, adjustable speed drives power by power electronic converters have caused insulation failure in the electrical motors with stator windings. The repeating impulse voltages produced by IGBTs created insulation reliability problems in the stator winding. Overvoltage can cause partial discharge (PD), which can rapidly result in insulation system failure. To address this issue, IEC standards and technical specifications (TS) necessitate that a PD test on the motor insulation system is done at sinusoidal and square voltages. The PD characteristics obtained are used to evaluate insulation performance, enhancing insulation design. This work focuses on the PD characterization of twisted pair samples using high frequency sine and square waveforms at room temperature. The PD characteristics were investigated at 50 Hz, 1 kHz, 2.4 kHz, and 5 kHz. The result shows that there are fewer PD events with lower PD magnitudes and shorter delay times at higher frequencies. Further, at different temperatures of 30 °C, 60 °C, and 90 °C, the partial discharge inception voltage (PDIV) of twisted pair insulation was investigated using high-frequency sine and square waveforms. The results show that the corona inception voltage (CIV) (kV) decreases as ambient temperature increases. Furthermore, the conditions for PD occurrence in the insulation system were analyzed at higher switching frequencies. The electric field distribution of twisted pairs with a 0 mm air gap was modeled from 50 Hz to 5 kHz switching frequency using COMSOL software.
Volume: 14
Issue: 4
Page: 893-902
Publish at: 2025-12-01

AI-based federated learning for heart disease prediction: a collaborative and privacy-preserving approach

10.11591/ijict.v14i3.pp751-759
Stuti Bhatt , Surender Reddy Salkuti , Seong-Cheol Kim
People with symptoms like diabetes, high BP, and high cholesterol are at an increased risk for heart disease and stroke as they get older. To mitigate this threat, predictive fashions leveraging machine learning (ML) and artificial intelligence (AI) have emerged as a precious gear; however, heart disease prediction is a complicated task, and diagnosis outcomes are hardly ever accurate. Currently, the existing ML tech says it is necessary to have data in certain centralized locations to detect heart disease, as data can be found centrally and is easily accessible. This review introduces federated learning (FL) to answer data privacy challenges in heart disease prediction. FL, a collaborative technique pioneered by Google, trains algorithms across independent sessions using local datasets. This paper investigates recent ML methods and databases for predicting cardiovascular disease (heart attack). Previous research explores algorithms like region-based convolutional neural network (RCNN), convolutional neural network (CNN), and federated logistic regressions (FLRs) for heart and other disease prediction. FL allows the training of a collaborative model while keeping patient info spread out among various sites, ensuring privacy and security. This paper explores the efficacy of FL, a collaborative technique, in enhancing the accuracy of cardiovascular disease (CVD) prediction models while preserving data privacy across distributed datasets.
Volume: 14
Issue: 3
Page: 751-759
Publish at: 2025-12-01

Techno-economic optimization of hybrid renewable energy systems for household energy management

10.11591/ijape.v14.i4.pp1035-1043
Faisal Irsan Pasaribu , Suwarno Suwarno , Surya Hardi , Ahmad Taufik , Albert Panjaitan , Aimil Musfi Andri , Muhammad Reza Aulia
Housing is a private palace that is safe, comfortable, and private. Techno-economic optimization of hybrid renewable energy systems and energy management for realizing green energy is a fundamental concept for ensuring security, comfort, and privacy in green housing for its residents, enabling them to carry out activities in their environment. The application of techno-economic optimization and renewable energy management to manage electrical energy so that it can be saved so that electricity costs can be reduced as one of the energy efficiency models. The problem of waste emissions and environmental pollution cannot be avoided. Therefore, a techno-economic optimization model for integrated power generation is needed, which is environmentally friendly and related to the housing problem discussed in this study. This study supports the concept that hybrid housing development is the best way to address environmental pollution, emissions, and waste in future housing and can be used as a benchmark for future housing development. In addition, the techno-economics of renewable energy used in households was also discussed.
Volume: 14
Issue: 4
Page: 1035-1043
Publish at: 2025-12-01

A novel WSSA technique for multi-objective optimal capacitors placement and rating in radial distribution networks

10.11591/ijape.v14.i4.pp934-950
Omar Muhammed Neda
Minimizing power loss while keeping the voltage profile within acceptable limits is a great challenge for the distribution system operators. Properly sized and optimally placed shunt capacitors (SCs) in radial distribution networks (RDNs) can enhance system efficiency and offer both technical and economic benefits. This paper presents a novel meta-heuristic technique, the weight salp swarm algorithm (WSSA) as a modified version of the original SSA algorithm by incorporating an inertia weight parameter to improve precision, speed, and consistency in solving the optimal capacitor placement (OCP) problem. The proposed method minimizes power loss, annual total costs, and improves the voltage profile of RDNs, ensuring practical applicability. Two RDNs, IEEE 33-bus and a real Iraqi 65-bus in Sadat Al-Hindiya, Babel Governorate, Iraq, were used to evaluate WSSA's performance. Comparative analysis with recently published approaches demonstrates WSSA’s superiority in reducing power loss, lowering costs, and improving voltage profiles. For the IEEE 33-bus, power loss is decreased by 34.81%, and the total cost is lessened by 29.08% (savings of $30,965.33). For the Iraqi 65-bus, WSSA reduces power loss by 32.03% and decreases the total cost by 29.51% (savings of $69,201.57). These results confirm WSSA’s effectiveness in achieving OCP with enhanced technical and economic benefits.
Volume: 14
Issue: 4
Page: 934-950
Publish at: 2025-12-01

Optimize the position of the distributed generator and capacitor bank in the distributed grid to minimize the generation cost

10.11591/ijape.v14.i4.pp970-979
Ngoc An Luu , Dinh Chung Phan
In this paper, we focus on determining the optimal position and size of multi-distributed generators and capacitor banks to minimize the generation cost of a distributed grid. The optimal position and size of distributed generators and capacitor banks are determined using a hybrid of conventional loss sensitivity factor and an improved one. The proposed algorithm has two stages. For each distributed generator, we prioritize its position and size. After that, we find the optimal position and size of the capacitor banks corresponding to this distributed generator installation to minimize the power loss. After considering all distributed generators, the optimal number, position, and size of the distributed generators and capacitor banks are determined based on the minimum generation cost value. This idea is developed in MATLAB and verified via sample distributed grids, including the IEEE-69 bus and IEEE-85 bus. The verifying results are evaluated and analyzed. By comparing those results to those of other methods, the performance of the newly introduced method is proven.
Volume: 14
Issue: 4
Page: 970-979
Publish at: 2025-12-01

Classification of breast cancer using a precise deep learning model architecture

10.11591/ijict.v14i3.pp933-940
Mohammed Ghazal , Murtadha Al-Ghadhanfari , Fajer Fadhil
Breast cancer is an important topic in medical image analysis because it is a high-risk disease and the leading cause of death in women. Early detection of breast cancer improves treatment outcomes, which can be achieved by identifying it using mammography images. Computer-aided diagnostic systems detect and classify medical images of breast lesions, allowing radiologists to make accurate diagnoses. Deep learning algorithms improved the performance of these diagnoses systems. We utilized efficient deep learning approaches to propose a system that can detect breast cancer in mammograms. The proposed approach adopted relies on two main elements: improving image contrast to enhance marginal information and extracting discriminatory features sufficient to improve overall classification quality, these improvements achieved based on a new model from scratch to focus on enhancing the accuracy and reliability of breast cancer detection. The model trained on the digital database for screening mammography (DDSM) dataset and compared with different convolutional neural network (CNN) models, namely EfficientNetB1, EfficientNetB5, ResNet-50, and ResNet101. Moreover, to enhance the feature selection process, we have integrated adam optimizer in our methodology. In evaluation, the proposed method achieved 96.5% accuracy across the dataset. These results show the effectiveness of this method in identifying breast cancer through images.
Volume: 14
Issue: 3
Page: 933-940
Publish at: 2025-12-01

Digital control of plant development through sensors and microcontrollers in Kosova

10.11591/ijict.v14i3.pp1072-1084
Ragmi M. Mustafa , Kujtim R. Mustafa , Refik Ramadani
The plant monitoring system aims to develop an automated solution for optimizing plant growth. Using the Arduino Uno ATMEGA328P microcontroller module and various sensors, this system regulates environmental conditions to promote optimal plant development. It requires adequate software to operate effectively, enabling the microcontroller to monitor and regulate climatic conditions. The primary goal of this paper is to present a comprehensive system that continuously measures parameters such as light intensity, air humidity, and soil moisture in real time within a vegetable greenhouse or a plastic-covered plant environment. This scientific paper provides an in-depth description of the hardware components used, their electronic connections, and the implementation of program code written in C++. Based on the measured physical parameters, the plant monitoring system performs specific actions, such as watering the plants and regulating the ambient temperature. In conclusion, this system effectively supports healthy plant growth and enhances the quality and yield of plant products. The paper serves as a practical example for improving plant cultivation in the agricultural sector in the Republic of Kosova.
Volume: 14
Issue: 3
Page: 1072-1084
Publish at: 2025-12-01

Optimal placement and sizing of DG and DSTATCOM in order to mitigate power losses in electrical distribution system

10.11591/ijape.v14.i4.pp826-841
Smrutirekha Mahanta , Manoj Kumar Maharana
The emphasis is now shifting away from conventional methods of power generation and towards unconventional distributed energy resources (DERs) located at distribution voltage level due to the rapid depletion of fossil fuel supplies and significant environmental pollution. Emphasis on research into the applications of DERs found scope in microgrids and active distribution networks. The placement of DERs close to load centers aids with providing clean, reliable power to additional customers, reduce electricity losses along transmission and distribution lines and in event of faults it allows to operate in islanded mode. This manuscript focuses on power smoothing, which implies reduction of power loss, improved voltage levels, and voltage stability. This study aims to optimize the capacities and placements of distributed generations (DGs) and distribution static compensators (DSTATCOMs) in order to reduce real power loss and improve the voltage profile. The problem of voltage from undistributed energy resources can best be solved by DSTATCOM. The goal function of the direct load flow technique, which also makes use of voltage deviation and the loss sensitivity factor, is used in this study to pinpoint the ideal placement for the DG and DSTATCOM on the MATLAB platform. The method is tested using the 33 and 69 bus routes. When the results are compared to recent methodologies, they show encouraging results.
Volume: 14
Issue: 4
Page: 826-841
Publish at: 2025-12-01

A hybrid approach using VGG16-EffcientNetV2B3-FCNets for accurate indoor vs outdoor and animated vs natural image classification

10.11591/ijict.v14i3.pp903-913
Meghana Deshmukh , Amit Gaikwad , Snehal Kuche
The paper introduces a hybrid approach that synergistically combines the strengths of VGG16, EfficientNetV2B3, and fully connected networks (FCNets) to achieve precise image classification. Specifically, our focus lies in discerning between basic indoor and outdoor scenes, further extended to distinguish between animated and natural images. Our proposed hybrid architecture harnesses the unique characteristics of each component to significantly enhance the model’s overall performance in fine-grained image categorization. In our methodology, we utilize VGG16 and EfficientNetV2B3 as the feature extractors. During evaluation, we examined various classification algorithms, such as VGG16, EfficientNet, Feature_Aggr_Avg, and Feature_Aggr_max, among others. Notably, our hybrid feature aggregation approach demonstrates a remarkable improvement of 0.5% in accuracy compared to existing solutions employing VGG16 and EfficientNet as feature extractors. Notably, for indoor versus outdoor image classification, feature_aggr_avgachieves an accuracy of 98.51%. Similarly, when distinguishing between animated and natural images, Feature_Aggr_Avgachieves an impressive accuracy of 99.20%. Our findings demonstrate improved accuracy with the hybrid model, proving its adaptability across diverse classification tasks. The model is promising for applications like automated surveillance, content filtering, and intelligent visual recognition, with its robustness and precision making it ideal for realworld scenarios requiring nuanced categorization.
Volume: 14
Issue: 3
Page: 903-913
Publish at: 2025-12-01

Navigating predictive landscapes of cloud burst prediction approaches: insights from comparative research

10.11591/ijict.v14i3.pp1146-1155
Anil Hingmire , Sunayana Jadhav , Megha Trivedi , Karan Sankhe , Omkar Khanolkar , Yukta Patil
Cloud burst forecasting remains an evolving field that grapples with the complexities of atmospheric phenomena and their impact on local environments. Cloud bursts in hilly regions demand robust predictive models to mitigate risks. This study addresses the challenge of imbalanced cloud burst occurrences, emphasizing the need for accurate predictions to minimize damage. It develops and evaluates a machine learning-based forecasting approach that includes several weather factors such as temperature, humidity, wind speed, and atmospheric pressure. The study also tackles the imbalance in cloud burst data. A dual-axis chart visually merges cloud burst occurrences with weather parameters, providing insights into their relationships over time. The model’s overall accuracy is 0.68, with precision and recall for cloud burst events at 0.25 and 0.07, respectively, and an F1-score of 0.11. However, when it comes to forecasting non-cloud burst occurrences, it shows a high precision of 0.72. This study evaluates machine learning models for cloud burst prediction, highlighting random forest as the top performer with an accuracy of 85.43%, effectively balancing true positives and true negatives while minimizing misclassifications. This research contributes to cloud burst prediction, offering performance insights and suggesting avenues for future exploration.
Volume: 14
Issue: 3
Page: 1146-1155
Publish at: 2025-12-01

Design of a half-bridge inverter with digital SPWM control for pure sine wave output

10.11591/ijape.v14.i4.pp803-815
Jalil Akaaboune , Bouazza El Mourabit , Mohamed Oulaaross , Mohamed Benchagra
To foster the widespread adoption of solar power, especially that produced by photovoltaic (PV) systems, we must move beyond the mere utilization of renewable energy sources. Prioritizing cost-effective approaches through innovative grid integration is essential. This strategic transformation significantly contributes to the global expansion of electrical energy production. One pioneering approach involves the implementation of inverters operating at high frequencies to efficiently filter and eliminate undesirable current harmonics, thus enhancing system performance. This innovative technique relies on the generation of rapid complementary digital pulse width modulation (PWM) signals, complete with built-in dead time, to manage a half-bridge inverter with a single phase. The paper recommends employing the IR2110 driver, an often-used component for MOSFET switch management, to execute this strategy. The entire system is controlled by high-frequency PWM signals, meticulously programmed for precision, generated by a microcontroller driver board. With its adaptability to various renewable energy conversion devices, this methodology extends its utility beyond solar energy. Practical tests have confirmed the efficacy of this strategy. Future research in this field should scrutinize the effect of PWM on system stability and harmonic distortion, explore advanced modulation methods, align PWM approaches with upcoming power electronics technologies, and work towards improving system efficiency.
Volume: 14
Issue: 4
Page: 803-815
Publish at: 2025-12-01
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