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Harnessing speed breakers potentials for electricity generation: a case study of Covenant University

10.11591/ijece.v15i3.pp2669-2680
Hope Evwieroghene Orovwode , John Amanesi Abubakar , Olutunde Oluwatimileyin Josiah , Ademola Abdullkareem
The global imperative to transition towards sustainable energy sources has sparked innovative solutions for energy generation and environmental conservation challenges. As fossil fuel usage for power generation continues to raise environmental concerns, converting kinetic energy from vehicular motion via speed breakers presents a unique avenue for renewable power production. This study explores the concept of utilizing speed breakers as a means of electricity generation to power little power-consuming but critical load, with Covenant University serving as a pertinent case study. This research investigates the technical, economic, and environmental implications of implementing speed breaker-based electricity generation within Covenant University. Analyzing the university's energy consumption patterns showed that some loads do not require much power but are critical. Street lighting is one of such loads. This study discerns the potential contribution of speed breaker-generated electricity to address energy demands by simulation and constructing a prototype. Advanced engineering tools, such as simulation software Fusion 360 and Proteus 8.0, were employed to model and integrate the roller speed breaker mechanism with the electrical infrastructure. The findings offer valuable insights into the viability of speed breaker-generated electricity as an alternative energy source, paving the way for sustainable energy practices in educational institutions and beyond.
Volume: 15
Issue: 3
Page: 2669-2680
Publish at: 2025-06-01

Adaptive hybrid particle swarm optimization and fuzzy logic controller for a solar-wind hybrid power system

10.11591/ijape.v14.i2.pp498-512
G. B. Arjun Kumar , M. Balamurugan , K. N. Sunil Kumar , Ravi Gatti
This paper presents the best modeling and control strategies for a grid-connected hybrid wind-solar power system to maximize energy production. For variable wind speeds, determine the optimal power point using fuzzy logic control, adopt an adaptive hill climb searching method, and compare it with an optimal torque control method for large inertia wind turbine (WT). The role of fuzzy logic controller (FLC) is to adjust the hill climbing search (HCS) technique's step-size according to the operating point. The doubly-fed induction generator (DFIG) control system has two subsystems: rotor-side and grid-side converters. The active and reactive power have been indirectly regulated by adjusting the current on the d-q axis. The rotor side converter (RSC) controllers are responsible for controlling the WTs rotational speed to achieve the maximum power output. The grid side converter (GSC) manages the voltage at the DC link and keeps a unity power factor between the grid and GSC. Optimal hybrid power point tracking technique for use with photovoltaic systems in both constant and variable shade circumstances, based on particle swarm optimization (PSO) and perturb and observe (P&O). The optimal power point tracking (OPPT) approach is compared to three other methods: PSO, P&O, and hybrid P&O-PSO. The model has a total capacity of 2.249 MW, with wind capacity of 2 MW and solar capacity of 0.249 MW, and its efficiency is analyzed.
Volume: 14
Issue: 2
Page: 498-512
Publish at: 2025-06-01

Effective assessment of power transformer insulation using time-varying model without temperature constraints

10.11591/ijape.v14.i2.pp373-381
Sachin Yashawant Sayais , Chandra Madhab Banerjee
This paper proposes a method for insulation diagnosis using the time-varying model. The parameters of the stated model are unique and can be recognized by the polarization current. Several methodologies have been reported for insulation diagnosis using various insulation models. However, moisture information without considering the effect of measurement temperature is bound to provide inaccurate result. Temperature significantly affects the dielectric response of materials. As the temperature rises, various important changes take place. Increased temperatures can boost molecular mobility, resulting in higher polarization, which in turn affects the material's dielectric constant and loss. Additionally, higher temperatures typically raise conductivity due to improved charge carrier mobility, further influencing the dielectric response. Hence, the effect of measurement temperature on insulation diagnosis is discussed in this paper so that responses recorded at different temperatures can be effectively compared. The proposed methodology for determining insulation state is tested using data from real-life power transformers.
Volume: 14
Issue: 2
Page: 373-381
Publish at: 2025-06-01

Investigating power quality issues in electric buggy battery charger systems: analysis and mitigation strategies

10.11591/ijece.v15i3.pp2534-2544
Wan Muhamad Hakimi Wan Bunyamin , Samshul Munir Muhamad , Wan Salha Saidon , Rahimi Baharom
This paper investigates power quality issues in the battery charger system of an electric buggy. Key power quality parameters such as total harmonic distortion (THD), power factor (PF), input voltage, and input current, were measured and analyzed during the charging process. The findings reveal significant power quality challenges, with THD levels exceeding IEEE 519 standards, indicating inefficiencies and potential risks such as increased heating and stress on charger components. Power factor readings reveal a substantial reactive power component, further contributing to inefficiency. To address these issues, the study recommends implementing harmonic mitigation techniques, such as passive and active filters, to reduce THD levels, using power factor correction methods, and optimizing charging algorithms to manage power demand more effectively. Continuous monitoring of charging parameters is essential for maintaining optimal performance and reliability. Adhering to standards is crucial for the efficient and reliable operation of electric vehicle (EV) charging systems, with regular compliance testing and benchmarking necessary to identify improvement areas and maintain a high-quality charging infrastructure. The proposed solutions aim to develop a sustainable and efficient charging system for electric buggies, providing valuable insights and recommendations for future research and development in power electronics and drive systems for EV applications.
Volume: 15
Issue: 3
Page: 2534-2544
Publish at: 2025-06-01

Grid connected solar water pumping system

10.11591/ijape.v14.i2.pp412-420
Mula Sreenivasa Reddy , Banda Srinivas Raja , Movva Naga Venkata Kiranbabu , Muzammil Parvez , Syed Inthiyaz , Nelaturi Nanda Prakash , Bodapati Venkata Rajanna , Guntukala Surendher
A grid-connected solar water pumping system (SWPS) uses solar power to pump water while simultaneously drawing power from the grid when necessary. These systems can benefit farmers in a variety of ways, including reliable power, lower electric bills, increased income, and improved economic viability. This study explores a solar photovoltaic (SPV) water pumping system designed to function with a single-phase distribution network. It utilizes an induction motor drive (IMD) and incorporates an advanced power-sharing technique for optimal performance. In addition to transferring power from SPV to IMD, a DC-DC boost converter functions as a grid interface and power factor adjustment device. Maximizing the power extracted from the SPV array is critical for optimizing its utilization. To do this, a control mechanism based on incremental conductance is implemented to track maximum power points. Simultaneously, the IMD connected to the power source inverter is regulated using a simple volt/frequency approach. The suggested system, which includes standalone, grid-interfaced, and mixed-mode situations, is developed and validated in a lab.
Volume: 14
Issue: 2
Page: 412-420
Publish at: 2025-06-01

Optimal placement of DGs in a multi-feed radial distribution system using actor-critic learning algorithm

10.11591/ijape.v14.i2.pp319-327
Neelakanteshwar Rao Battu , Surender Reddy Salkuti
Multi-feed radial distribution systems are used to reduce the losses in the system using reconfiguration techniques. Reconfiguration can reduce the losses in the system only to a certain extent. Introduction of distributed generators has vastly improved the performance of distribution systems. Distributed generators can be used for reduction of loss, the improvement of the voltage profile, and reliability enhancement. Distribution generators play a vital role in reducing the losses in the distribution system. Placement of distributed generators in a multifeed system is a complex task to be solved using classical optimization methods. Classical optimization techniques may sometimes fail to provide a converged solution. Installation of distributed generators at suitable locations in a multi-feed system is found in this paper using the actor-critic learning algorithm. Actorcritic learning approach uses temporal difference error as a signal in making judgements regarding actions to be taken for future states in accordance with rewards that have been obtained by applying the present policy. The approach is applied to a standard 16-bus distribution system for reduction of system losses, and the results are discussed.
Volume: 14
Issue: 2
Page: 319-327
Publish at: 2025-06-01

Assessment of thermal characteristics in diverse lithium-ion battery enclosures and their influence on battery performance

10.11591/ijape.v14.i2.pp275-281
Mahiban Lindsay , M. Emimal
Battery technology is an emerging research domain within the automotive sector, with a focus on various battery chemistries such as Li-ion, LiFePO4, NMC, and NaCl, as well as specialized cells like LiSOCl2. These chemistries are crucial in advancing electric vehicle (EV) battery technology. Batteries are available in different packaging formats, including prismatic, cylindrical, and pouch designs, each tailored to diverse operational environments. This study investigates the impact of these various battery packages on overall battery performance. Additionally, it assesses the influence of temperature on battery efficiency, aiming to identify the optimal temperature range for maximum performance. A significant part of the research focuses on the development of efficient battery thermal management (BTM) systems, which are designed to control and maintain battery temperature within the desired range, thereby enhancing efficiency. The outcomes of this study provide valuable insights for improving the reliability and efficiency of EV batteries. These findings are crucial for ensuring optimal battery performance and safety across different field conditions. Automotive manufacturers and battery suppliers can leverage these insights to refine their product designs, ensuring the dependability and safety of EV batteries. By enhancing battery performance through improved packaging and effective thermal management, this research contributes significantly to the advancement of EV technology, making electric vehicles more reliable and efficient for consumers.
Volume: 14
Issue: 2
Page: 275-281
Publish at: 2025-06-01

Review on optimal planning and operation of charging stations for electric vehicles

10.11591/ijape.v14.i2.pp359-372
M. S. Arjun , N. Mohan , K. R. Satish , Arunkumar Patil , D. P. Somashekar
Several factors need to be taken into account while planning the locations of electric vehicle charging stations. The thoughtful design and arrangement of charging stations, as a crucial component of the infrastructure supporting electric vehicles, is essential for the advancement of these kinds of vehicles. However, a number of intricate aspects, including policy economics, charging demand, user comfort when charging, and traffic circumstances, influence the design and arrangement of charging stations. With the goal to uncover competing interests and opportunities for collaboration in the operation and development of charging infrastructure, this study intends to assist researchers and technology developers in investigating cutting-edge techniques from the viewpoint of each constituent. Additionally, only a strong electric vehicle charging station (EVCS) infrastructure may provide some of the answers to the most basic EV concerns, like EV cost and range. The literature claims that several sorts of techniques, objective functions, and constraints for issue formulation have been used by the scholars. In addition, sensitivity analysis, vehicle to grid strategy, integration of distributed generation, charging kinds, objective functions, restrictions, EV load modelling, uncertainty, and optimization methodologies are examined for the most recent research publications. Discussions occur as well regarding the effects of the EV load on the distribution network, the environment, and the economy.
Volume: 14
Issue: 2
Page: 359-372
Publish at: 2025-06-01

Speed control of induction motor using fuzzy logic based on internet of things

10.11591/ijape.v14.i2.pp488-497
Charles Ronald Harahap , F. X. Arinto Setyawan , Desi Budiati
The aim of this research was to propose an innovative method of controlling the speed of an induction motor (IM) using fuzzy logic, integrated with internet of things (IoT). To achieve this aim, fuzzy logic was used to increase the performance of IM in order to obtain stable speed and high system response even in the presence of disturbances. Moreover, fuzzy logic relied on rules that used linguistic variables, and its main advantage was simple yet highly accurate, enabling the system to be efficient for determining parameters compared to the time-consuming and inefficient trial-and-error method. In this research, IoT implementation used Blynk platform to control and monitor IM speed remotely. Additionally, the components used in this research included an inverter, gate driver, Arduino Mega 2560, and NodeMCU ESP8266. Pulse width modulation (PWM) was required to obtain rotational speed of the motor through MOSFET switching process. The gate driver amplified PWM signal from Arduino Mega 2560, allowing MOSFET to operate. As a result, IM achieved a stable speed, and the system response followed the reference using fuzzy logic. In addition to this process, the system could be controlled and monitored remotely. Finally, the control system was successful, and the results were presented to show the viability of the proposed method.
Volume: 14
Issue: 2
Page: 488-497
Publish at: 2025-06-01

Hardware implementation of safety smart password based GSM module controlling circuit breaker

10.11591/ijape.v14.i2.pp441-448
Rakesh G. Shriwastava , Pawan C. Tapre , Rajendra M. Rewatkar , Swapna M. Choudhary , Ramesh K. Rathod , Sham H. Mankar , Hemant R. Bhagat Patil , Salim A. Chavan
This research work highlights the hardware implementation of safety smart password-based GSM module controlling circuit breaker. Safety is the major concern in daily life for domestic activities. In current scenario, accidental death of a lineman are the major issues and to protect operators for the same. To control circuit breakers, passwords security is essential for lineman. Due to that electrical accident’s ratio is increased day to day life at the time of repairing the lines. It is also done due to lack of communication and coordination between maintenance and substation. For safety of lineman, on and off line turning operation is proposed. Secure password is for breaker operation and maintenance. In the proposed system, password is sent to the line operator's mobile phone and GSM module by automatic voltage regulator (AVR) microcontroller. Entered password and password received by the GSM receiver is match so circuit breaker will be smoothly operated. If password is incorrect, message will appear on the LCD display for security purposed and message sent to control room regarding unauthorized access to the system.
Volume: 14
Issue: 2
Page: 441-448
Publish at: 2025-06-01

Crop prediction using an enhanced stacked ensemble machine learning model

10.11591/ijeecs.v38.i3.pp1840-1850
D. Madhu Sudhan Reddy , N. Usha Rani
In India, agriculture is a major sector that fulfils the population's food requirements and significantly contributes to the gross domestic product (GDP). The careful selection of crops is fundamental to maximizing agricultural yield, thereby elevating the economic vitality of the farming community. Precision agriculture (PA) leverages weather and soil data to inform crop selection strategies. Conventional machine learning (ML) models such as decision trees (DT), support vector classifier, K-nearest neighbors (KNN), and extreme gradient boost (XGBoost) have been deployed to predict the best crop. However, these model's efficiency is suboptimal in the current circumstances. The enhanced stacked ensemble ML model is a sophisticated meta-model that addresses these limitations. It harnesses the predictive power of individual ML models, stratified in a layered architecture to improve the prediction accuracy. This advanced model has demonstrated a commendable accuracy rate of 93.1% prediction by taking input of 12 soil parameters such as Nitrogen, Phosphorus, Potassium, and weather parameters such as temperature and rainfall, substantially outperforming the accuracies achieved by the individual contributing models. The efficacy of the proposed meta-model in crop selection based on agronomic parameters signifies a substantial advancement, fortifying the economic resilience of India's agriculture.
Volume: 38
Issue: 3
Page: 1840-1850
Publish at: 2025-06-01

Exploring the effectiveness of hybrid artificial bee PyCaret classifier in delay tolerant network against intrusions

10.11591/ijece.v15i3.pp3149-3161
Rajashri Chaudhari , Manoj Deshpande
In challenging environments with intermittent connectivity and the absence of end-to-end paths, delay tolerant networks (DTNs) require robust security measures to safeguard against potential threats. This study addresses these issues by implementing an intrusion detection system (IDS) enhanced with machine learning techniques. Common threats such as distributed denial-of-service (DDoS) and flood attacks are tackled using datasets like network intrusion detection (NID) and flood attack datasets. Multiple machines learning methods, including k-nearest neighbors (K-NN), decision trees (DT), logistic regression (LR), and others, are utilized to improve detection accuracy. A PyCaret-based approach is developed to increase efficiency while preserving attack detection accuracy in DTNs. Comparative research demonstrates that PyCaret outperforms Scikit-learn models, and the artificial bee PyCaret classifier (ABPC) optimizes hyperparameters to improve model performance. NS2 simulation shows the system's ability to thwart attacks, offering useful insights into DTN security and improving communication reliability in various situations.
Volume: 15
Issue: 3
Page: 3149-3161
Publish at: 2025-06-01

A deep learning-based myocardial infarction classification based on single-lead electrocardiogram signal

10.11591/ijaas.v14.i2.pp352-360
Annisa Darmawahyuni , Winda Kurnia Sari , Nurul Afifah , Bambang Tutuko , Siti Nurmaini , Jordan Marcelino , Rendy Isdwanta , Cholidah Zuhroh Khairunnisa
Acute myocardial infarction (AMI) carries a significant risk, emphasizing the critical need for precise diagnosis and prompt treatment of the responsible lesion. Consequently, we devised a neural network algorithm in this investigation to identify myocardial infarction (MI) from electrocardiograms (ECGs) autonomously. An ECG is a standard diagnostic tool for identifying acute MI due to its affordability, safety, and rapid reporting. Manual analysis of ECG results by cardiologists is both time-consuming and prone to errors. This paper proposes a deep learning algorithm that can capture and automatically classify multiple features of an ECG signal. We propose a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) for automatically diagnosing MI. To generate the hybrid CNN-LSTM model, we proposed 39 models with hyperparameter tuning. As a result, the best model is model 35, with 86.86% accuracy, 75.28% sensitivity and specificity, and 83.56% precision. The algorithm based on a hybrid CNN-LSTM demonstrates notable efficacy in autonomously diagnosing AMI and determining the location of MI from ECGs.
Volume: 14
Issue: 2
Page: 352-360
Publish at: 2025-06-01

Impedance matching and power recovery in response to coil misalignment in wireless power transmission

10.11591/ijece.v15i3.pp2556-2566
Lunde Ardhenta , Ichijo Hodaka , Kazuya Yamaguchi , Takuya Hirata
The improper alignment of the coils between the transmitter and receiver has a significant impact on wireless power transfer. If designers carefully calculate the parameters of inductance, capacitance, coupling coefficient, and working frequency and precisely implement these parameters into actual components, the system can optimize power transfer. However, it is evident that such a precise realization is often unachievable. This paper proposes a symbolic condition to maintain significant power despite the misalignment of transmitter and receiver coils. These symbolic conditions constrain parameters by simplifying some variables. This matching condition develops in the inequality of coupling coefficient, working frequency and quality factor, which are a crucial reference for maintaining power transfer. This condition is considered an additional one to the well-known impedance matching condition.
Volume: 15
Issue: 3
Page: 2556-2566
Publish at: 2025-06-01

Powering the future of electrical load forecasting using a regression learner in machine learning

10.11591/ijape.v14.i2.pp264-274
Sushama D. Wankhade , Babasaheb R. Patil
The primary intent of the present research was to design and execute an electrical load forecasting system using machine learning (ML) techniques. The implementation of an advanced predictive method, specifically an ML algorithm, helped in accurate load forecasting, which is crucial for efficient power grid management, and optimizing resource allocation. Electricity load fluctuates due to various complex factors, making traditional forecasting methods struggle. This is where ML shines. ML algorithms can learn from historical data, identifying intricate patterns and relationships that influence electricity demand. This allows them to make more accurate predictions than static models. In this work, regression learning models in ML are used with the MATLAB platform. Three years of real-time data from the Wavi substation in India are used. Considering day, date, hour of day, max and min temperature of the day, and voltage and current are taken as input parameters to test fourteen different models of assorted regression algorithms. The performance of these models is evaluated using commonly used metrics, root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE), along with a few other parameters. The optimized trained model is then tested with real data to obtain the forecasted load. The correlation between the Actual load and forecasted load is found to be 0.999962.
Volume: 14
Issue: 2
Page: 264-274
Publish at: 2025-06-01
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