cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Applied Power Engineering (IJAPE)
Published by Institute of Advanced Engineering and Science
ISSN : 22528792     EISSN : 27222624     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
International Journal of Applied Power Engineering (IJAPE) focuses on the applied works in the areas of power generation, transmission and distribution, sustainable energy, applications of power control in large power systems, etc. The main objective of IJAPE is to bring out the latest practices in research in the above mentioned areas for efficient and cost effective operations of power systems. The journal covers, but not limited to, the following scope: electric power generation, transmission and distribution, energy conversion, electrical machinery, sustainable energy, insulation, solar energy, high-power semiconductors, power quality, power economic, FACTS, renewable energy, electromagnetic compatibility, electrical engineering materials, high voltage insulation technologies, high voltage apparatuses, lightning, protection system, power system analysis, SCADA, and electrical measurements.
Arjuna Subject : -
Articles 26 Documents
 1   2   3 
Search results for , issue "Vol 14, No 2: June 2025" : 26 Documents clear
Power quality enhancement for a grid connected wind turbine energy system with PMSG Rajasri, Kasula; Kiranbabu, Movva Naga Venkata; Raja, Banda Srinivas; Parvez, Muzammil; Reddy, Govulla Ravi Kumar; Prakash, Nelaturi Nanda; Ahammad, Sk. Hasane; Rajanna, Bodapati Venkata
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp392-400

Abstract

This project investigates the burgeoning potential of gearless wind turbine systems as a pivotal clean energy resource. Unlike conventional gearbox-based turbines, which grapple with issues like frequent breakdowns, intricate repairs, and prolonged downtimes, gearless systems present a suite of advantages. Chief among these is heightened reliability, diminished maintenance costs, and augmented efficiency. By circumventing the need for a gearbox, gearless turbines shed weight, bolster reliability, and demand less upkeep. The incorporation of permanent magnet generators further elevates their efficiency and renders them well-suited for offshore deployment. The emergence of gearless wind turbines heralds a promising frontier for effectively and efficiently harnessing wind power. Their streamlined design and robust performance potential position them as a transformative force in the renewable energy landscape, poised to catalyze substantial advancements towards sustainable energy goals. As research delves deeper into their capabilities and optimization, gearless turbines are poised to emerge as a cornerstone technology in the global pursuit of clean energy solutions.
Fault diagnosis of electric motors using vibration signal analysis Singh, Mandeep; Saggu, Tejinder Singh; Dhingra, Arvind
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp300-307

Abstract

In industrial applications, especially in manufacturing environments, electric motors are employed practically everywhere. They are necessary for many different sectors, which can sometimes make it challenging to prevent malfunctions and keep them operating at their best. Numerous defects can affect how well they work, but bearing-related errors are the most frequent reasons for motor failures. This research uses temporal and frequency domain analysis of vibration signals to identify motor faults. A public domain database has been used for the investigation and analysis. The findings show that electric motor problems, including inner raceway, outer raceway, and rolling element fault, can be identified and diagnosed using the time and frequency domain features extracted from the vibration signals. The effectiveness of the proposed technique is shown by comparing it with both the time domain and frequency domain techniques. The accuracy of the time domain and frequency domain techniques is 85.4% and 91.6% respectively. However, the proposed hybrid technique has a far better accuracy of 95.8% as compared to the two techniques.
Hardware implementation of safety smart password based GSM module controlling circuit breaker Shriwastava, Rakesh G.; Tapre, Pawan C.; Rewatkar, Rajendra M.; Choudhary, Swapna M.; Rathod, Ramesh K.; Mankar, Sham H.; Patil, Hemant R. Bhagat; Chavan, Salim A.
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp441-448

Abstract

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.
Methodology for incisive foraging of high-risk junctions and elimination of injected false data in smart grid Ghosh, Poulami; Biswas, Subrata; Purkait, Prithwiraj
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp347-358

Abstract

The present work represents a method for identification of the vulnerable nodes in smart grid as well as assessment of the performance of voltage stability indicator technique with the help of weighted least square scheme. in today’s smart grid system, false data injection (FDI) is the major issue to supply uninterruptedly at demand side in advanced metering infrastructure (AMI). The recent blackouts are the consequence of non-identifying FDI as research on FDI is not considered under power system analysis. In our research, vulnerable nodes of a power system network have been identified and a state estimation method was used to eliminate superfluous data for those identified nodes. Voltage stability indicator (VSI) based state estimation have been used successfully to make the smart grid system error free as possible. VSI method has been used first to find the vulnerable nodes of the grid after that the efficient state estimation method i.e. optimal weighted least square (optimal WLS) have been employed to get refined result. Results show that VSI based technique in concurrence with optimal WLS has potential to eliminate undesirable data with sensible level of precision.
Powering the future of electrical load forecasting using a regression learner in machine learning Wankhade, Sushama D.; Patil, Babasaheb R.
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp264-274

Abstract

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.
Speed control of induction motor using fuzzy logic based on internet of things Harahap, Charles Ronald; Setyawan, F. X. Arinto; Budiati, Desi
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp488-497

Abstract

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.

Page 3 of 3 | Total Record : 26

 1   2   3 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 14, No 4: December 2025 Vol 14, No 3: September 2025 Vol 14, No 2: June 2025 Vol 14, No 1: March 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 3: December 2020 Vol 9, No 2: August 2020 Vol 9, No 1: April 2020 Vol 8, No 3: December 2019 Vol 8, No 2: August 2019 Vol 8, No 1: April 2019 Vol 7, No 3: December 2018 Vol 7, No 2: August 2018 Vol 7, No 1: April 2018 Vol 6, No 3: December 2017 Vol 6, No 2: August 2017 Vol 6, No 1: April 2017 Vol 5, No 3: December 2016 Vol 5, No 2: August 2016 Vol 5, No 1: April 2016 Vol 4, No 3: December 2015 Vol 4, No 2: August 2015 Vol 4, No 1: April 2015 Vol 3, No 3: December 2014 Vol 3, No 2: August 2014 Vol 3, No 1: April 2014 Vol 2, No 3: December 2013 Vol 2, No 2: August 2013 Vol 2, No 1: April 2013 Vol 1, No 3: December 2012 Vol 1, No 2: August 2012 Vol 1, No 1: April 2012 Vol 10 More Issue