Volume-13 Issue-6, March 2025 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org Exploring Innovation | ISSN:2277-3878(Online) | A Periodical Journal | Reg. No.: C/819981 | Published By BEIESP Tue, 24 Jun 2025 05:04:27 +0000 en-US hourly 1 https://www.ijrte.org/wp-content/uploads/2021/02/cropped-IJRTE-Logo-WS-32x32.png Volume-13 Issue-6, March 2025 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org 32 32 F820613060325 https://www.ijrte.org/portfolio-item/f820613060325/ Thu, 27 Feb 2025 10:55:38 +0000 https://www.ijrte.org/?post_type=portfolio&p=49694 The post F820613060325 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Real-Time Control for Wind-Hydrogen Systems: Prioritizing Hydrogen Generation and Ancillary Services with Site Data
Arpita Banerjee1, Rashmi Srivastava2

1Arpita Banerjee, Research Scholar, Department of Chemistry, IES University, Bhopal (M.P.), India.

2Dr. Rashmi Srivastava, Associate Professor, Department of Chemistry, IES University, Bhopal (M.P.), India.      

Manuscript Received on 03 January 2024 | First Revised Manuscript Received on 08 January 2025 | Second Revised Manuscript Received on 01 March 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 24-30 | Volume-13 Issue-6, March 2025 | Retrieval Number: 100.1/ijrte.F820613060325 | DOI: 10.35940/ijrte.F8206.13060325

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Green hydrogen has emerged as a cornerstone in achieving carbon neutrality, especially within the power and industrial sectors. This study focuses on integrating wind energy and electrolyser-based hydrogen production to enhance grid resilience and support frequency regulation. A novel control strategy for managing the DC link voltage in electrolyser-assisted wind turbines is proposed, enabling a fast frequency response under varying wind conditions. Utilizing real-world wind speed and grid frequency data from Jamogadrani, Madhya Pradesh, this work evaluates the hydrogen production potential and frequency correction capabilities of the hybrid system. Key findings include the economic and operational viability of prioritizing hydrogen production while supporting grid stability. This paper outlines a pathway for integrating such hybrid systems into India’s renewable energy framework, addressing challenges in frequency regulation and maximizing the utility of renewable resources. The study provides a foundation for policy shifts and technological advancements needed for the successful deployment of wind-hydrogen systems.

Keywords: Wind farms, Green Hydrogen, Frequency Regulation, Electrolyzer Control, Renewable Energy Grid Integration.
Scope of the Article: Mechanical Engineering and Applications

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]]> F820513060325 https://www.ijrte.org/portfolio-item/f820513060325/ Thu, 27 Feb 2025 10:50:18 +0000 https://www.ijrte.org/?post_type=portfolio&p=49691 The post F820513060325 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Production of Green Hydrogen from Wind Energy in India
Arpita Banerjee1, Rashmi Srivastava2

1Arpita Banerjee, Research Scholar, Department of Chemistry, IES University, Bhopal (M.P.), India.

2Dr. Rashmi Srivastava, Associate Professor, Department of Chemistry, IES University, Bhopal (M.P.), India.      

Manuscript Received on 03 January 2024 | First Revised Manuscript Received on 09 January 2025 | Second Revised Manuscript Received on 05 February 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 20-23 | Volume-13 Issue-6, March 2025 | Retrieval Number: 100.1/ijrte.F820513060325 | DOI: 10.35940/ijrte.F8205.13060325

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The report describes India’s capacity to produce green hydrogen from wind energy, how it generates power, and whether it could eventually replace grey hydrogen in industrial settings. India is expected to make more than 5 million metric tonnes of hydrogen annually to meet industrial demand. On the other hand, 13 million metric tonnes of CO2 are released into the atmosphere for every million metric tonnes of hydrogen produced. In addition to a state-of-the-art evaluation of the green hydrogen technology value chain, the study will include a case study on the production of green hydrogen from a 3-MW wind turbine located in the coastal region of Tamil Nadu. This study indicates that 125 gigawatt-hours of capacity are required for a wind farm. According to this research, wind farms with a capacity of 125 gigawatt-hours can substitute for grey and blue hydrogen to meet India’s current annual industrial hydrogen consumption of 5 million metric tonnes. The study focuses on hydrogen storage methods for wind turbine electricity and green hydrogen-based storage facilities. The primary focus of this study is on the possibility of green hydrogen to meet India’s needs for industrial and other hydrogen-related applications.

Keywords: Green Hydrogen, Wind Energy, PEM, Electrolysis.
Scope of the Article: Mechanical Engineering and Applications

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]]> F820013060325 https://www.ijrte.org/portfolio-item/f820013060325/ Thu, 27 Feb 2025 10:44:36 +0000 https://www.ijrte.org/?post_type=portfolio&p=49689 The post F820013060325 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Enhancing Grid Stability-Bi-Directional V2G Integration in IEEE Distribution Networks with PSO
Jasim Ahmed Jasim Alateya1, Ebrahim Abdulrahman Hasan2, Khaled Zehar3

1Jasim Ahmed Jasim Alateya, Department of Electrical and Electronics Engineering, University of Bahrain, College of Engineering, Arad (Muharraq), Bahrain.

2Dr. Ebrahim Abdulrahman Abdulla, Assistant Professor, Department of Electrical and Electronics Engineering, University of Bahrain, College of Engineering, Arad (Muharraq), Bahrain.

3Dr. Khaled Zehar, Department of Electrical and Electronics Engineering, University of Bahrain, College of Engineering, Arad (Muharraq), Bahrain.     

Manuscript received on 26 December 2024 | Revised Manuscript received on 02 January 2025 | Second Revised Manuscript received on 28 January 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 10-19 | Volume-13 Issue-6, March 2025 | Retrieval Number: 100.1/ijrte.F820013060325 | DOI: 10.35940/ijrte.F8200.13060325

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper investigates the integration of bidirectional Vehicle-to-Grid (V2G) technology into the IEEE 33- bus distribution network to enhance grid stability, voltage regulation, and peak load management. V2G technology enables electric vehicles (EVs) to act as distributed energy resources, contributing to reactive power compensation and supporting voltage levels within the acceptable range of 0.95–1.05 p.u. Using a Particle Swarm Optimisation (PSO) algorithm, the study identifies the optimal placement and sizing of electric vehicle (EV) batteries based on a 24-hour load forecast, with a focus on highdemand scenarios. The methodology incorporates load flow analysis, system constraints, and the dynamic interplay of EV charging and discharging cycles. The results demonstrate that strategically placed V2G-enabled electric vehicles (EVs) can significantly reduce peak demand by discharging energy during high-load periods, thereby decreasing the reliance on additional generation capacity. Additionally, EV integration enhances voltage stability and minimizes infrastructure stress, contributing to more resilient grid operations. However, the PSO algorithm exhibited certain limitations, including sensitivity to parameter settings and a tendency toward premature convergence, which may impact the optimization’s accuracy. These findings highlight the potential for hybrid optimization approaches to address such limitations and refine system performance. This research presents a scalable and practical framework for integrating V2G technology into modern power systems, aligning with the growing demand for renewable energy and sustainable energy management. By addressing critical challenges in EV deployment and grid stability, this study provides actionable insights for grid operators, paving the way for future advancements in optimisation techniques and real-time control strategies for high-EVpenetration networks.

Keywords: Vehicle-to-Grid (V2G), Electric Vehicles (EVs), Particle Swarm Optimization (PSO), IEEE 33-Bus Distribution Network, Reactive Power Compensation, Peak Load Shaving.
Scope of the Article: Electrical and Electronics

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]]> E818713050125 https://www.ijrte.org/portfolio-item/e818713050125/ Thu, 27 Feb 2025 10:37:16 +0000 https://www.ijrte.org/?post_type=portfolio&p=49685 The post E818713050125 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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An Algorithm for Detecting Brute Force Attacks on FTP and SSH Services Utilizing Deep Learning with Probabilistic Neural Networks (PNN)
Hanadi Alosimy1, Jawaher Alzaidi2, Samah Alajmani3, Ben Soh4

1Hanadi Alosimy, Department of Information Technology, College of Computer and Information Technology, Taif University, Saudi Arabia.

2Jawaher AlZaidi, Department of Information Technology, College of Computer and Information Technology, Taif University, Saudi Arabia.

3Samah H. Alajmani, Department of Information Technology, College of Computer and Information Technology, Taif University, Saudi Arabia.

4Ben Soh, Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Australia;     

Manuscript received on 12 November 2024 | Revised Manuscript received on 28 December 2024 | Second Revised Manuscript received on 16 January 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 1-9 | Volume-13 Issue-6, March 2025 | Retrieval Number: 100.1/ijrte.E818713050125 | DOI: 10.35940/ijrte.E8187.13060325

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Brute force attacks remain one of the most prevalent and effective methods cybercriminals use to gain unauthorized access to networks and systems. These attacks involve systematically attempting various password or key combinations until the correct one is identified, often targeting critical services such as FTP (File Transfer Protocol) and SSH (Secure Shell). The consequences of these attacks can be severe, including data breaches, financial losses, and reputational damage. Intrusion Detection Systems (IDS) play a crucial role in mitigating these threats by monitoring network traffic and identifying malicious activities. However, traditional IDS methods—such as signaturebased detection and anomaly detection—struggle to detect emerging and evolving threats. To address these challenges, this study presents an advanced detection model that utilises deep learning techniques, specifically a Probabilistic Neural Network (PNN), to identify brute-force attacks on FTP and SSH protocols. The model is trained and evaluated using the CICIDS2018 dataset, with the Bat Optimization Algorithm employed to fine-tune parameters and enhance performance. The proposed model achieves remarkable results, with an accuracy of 99.968%, precision of 99.949%, recall of 99.986%, and an F1-score of 99.968%. These findings highlight the model’s potential as a highly effective tool for strengthening network security and preventing unauthorized access.

Keywords: BAT Optimization Algorithm, Brute Force, Deep Learning, IDS, PNN.
Scope of the Article: Information and Data Security

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]]> D7832118419 https://www.ijrte.org/portfolio-item/d7832118419/ Thu, 27 Feb 2025 10:31:07 +0000 https://www.ijrte.org/?post_type=portfolio&p=49682 The post D7832118419 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Personalized and Intelligent Agent Based Context-Aware Mobile Learning Framework
Alaa Edein M. Qoussini1, Shaima M. Al-Tabib2

1Alaa Edein M. Qoussini, Department of Humanities & Basic Sciences, English Language Scientific College of Design, Muscat, Oman.

2Shaima M. Al-Tabib, Research Office, National College of Automotive Technology, Muscat, Oman.    

Manuscript Received on 29 October 2019 | First Revised Manuscript Received on 08 January 2025 | Second Revised Manuscript Received on 20 February 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 31-40 | Volume-13 Issue-6, March 2025 | Retrieval Number: 100.1/ijrte.D7832118419 | DOI: 10.35940/ijrte.D7832.13060325

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Mobile learning promises the freedom to learn anytime, anywhere. However, effectively integrating personalization within mobile learning environments presents a significant challenge. This research aims to address this challenge by developing a novel framework for personalized content delivery in mobile learning environments (PCDMLE). The proposed PCDMLE framework leverages three key aspects of personalisation: student preferences, academic level, and assessment unit. By dynamically adapting learning content based on these individual characteristics, the framework aims to enhance student performance and simplify the management of diverse learning needs. To achieve this objective, an in-depth literature review was conducted to identify key aspects of personalisation within the context of mobile learning. Based on this review, a framework was developed and subsequently validated through an expert review process. A prototype was then created and evaluated through a six-week experiment. Finally, participant feedback was collected through a survey to assess their evaluation and satisfaction with the framework. The results, derived from both the expert review and participant feedback, demonstrate the framework’s effectiveness in delivering personalized content based on the identified aspects. Furthermore, the findings indicate a positive impact on student performance. This research, therefore, makes a significant contribution to the advancement of personalised content delivery within the mobile learning domain.

Keywords: Mobile Learning, Personalized Mobile Learning, Context-Aware, Personalized Content-Delivery and Context Aware Content-Delivery Framework.
Scope of the Article: Mobile Computing and Applications

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