Daily Archives: January 30, 2026

Authors: Hachimenum N. Amadi, Sopakiriba Maxwell West, Richeal Chinaeche Ijeoma

Abstract: The incessant national grid collapse has become a global embarrassment; from 2015 to May 2024 the Transmission Company of Nigeria (TCN) has recorded 105 cases of grid collapse. Phasor Measurement Units (PMUs) are necessary for the extensive use and efficient running of international power networks, the present Supervisory Control and Data Acquisition (SCADA) system used in Nigeria does not provide a robust and dependable solution that improves the power grid’s real time monitoring and control capabilities. PMU will reduce the frequency of power grid breakdowns and also resolve fault location troubleshooting safely and timely. The optimal placement of Phasor Measurement Units (PMUs) is an important requirement in power systems research, particularly for the localization of transmission line faults. This research has proposed a Modified Sparsity Genetic Algorithm Optimizer (MS-GAO) for optimal placement of Phasor Measurement Units (PMUs) in Power Systems over the standard Genetic Algorithm (GA) approach used in various related studies. To further validate the performance, the time complexity studies were performed to determine the better technique considering enhanced PMU placement. The proposed approach has been applied to two IEEE power system networks – the IEEE 6-Bus and 14-bus power networks. The simulations were performed using the MATLAB software tool and results compared with the standard Genetic Algorithm (sGA) on the basis of the percentage Classification Efficiency (CE) and the number of trial iteration runs (iters) used per simulation. The results showed that the proposed MS-GAO gave comparable CEs when compared to sGA with 100% CE for 100iters. However, it was found that reducing the iterations to about 50iters resulted in a degradation of CE. Thus, a compromise should be made between the number of iterations required and the level of CE needed in the problem solution. In addition, computational run-time complexity results considering the 6-bus power network revealed that the MS-GAO will give better run-times when compared to the sGA with an average run-time reduction of about 0.5s. Thus, it is recommended that the MS-GAO be employed for a higher power bus networks as the computational demands will obviously be higher using a sGA.

DOI: https://doi.org/10.5281/zenodo.18427464

Published by: vikaspatanker

Authors: Mr. Rushikesh Kalhale, Mr. Venkatesh Bansode, Mr. Utkarsh Maske, Prof.Deepa Shivshimpi

Abstract: Mathematics is at the base of all Artificial Intelligence (AI) systems. Throughout the AI lifecycle, mathematics is the pillar for representing data at the start, learning, reasoning on behalf of the human user and adapting in the mid-section, and finally optimizing any algorithm or data driven model at the end. This paper will discuss how the main mathematics will start to emerge as critical constructs for AI – linear algebra, calculus, probability and statistics, and optimization. We will demonstrate the pertinence of mathematical models as a pathway for the development of neural networks, machine learning algorithms, and data driven decision systems. In demonstrating examples of how mathematics has evolved as part of the responsive development of Artificial Intelligence, we can clearly delineate the ongoing, sometimes inescapable, role mathematics will have in defining intelligent systems in the future.

DOI: https://doi.org/10.5281/zenodo.18427209

Published by: vikaspatanker

Authors: Hachimenum Nyebuchi Amadi, Ogadinma Agha Onya,, Richeal Chinaeche Ijeoma

Abstract: The reliability of 33kV distribution networks is crucial to the stability of Nigeria's electricity supply. Serving as the interface between the transmission grid and 11kV feeders, these networks directly affect service delivery, customer satisfaction, and operational efficiency. This paper examines the operational challenges and reliability issues of the 33kV feeders within the Port Harcourt Electricity Distribution Company (PHEDC) network, with a focus on performance assessment using standard indices such as SAIDI, SAIFI, and CAIDI. Preventive maintenance, feeder automation, and improved operational practices are identified as key measures for enhancing reliability. Results reveal major network challenges such as overloaded feeders, poor voltage profiles, high technical losses, and frequent interruptions. Reliability indices, including SAIFI, SAIDI, CAIDI, and ENS, were significantly above IEEE and NERC thresholds, indicating poor service continuity. To address these deficiencies, the study proposes targeted improvement strategies such as feeder reconfiguration, installation of automated reclosers and sectionalizers, preventive maintenance, and upgrading of aging conductors and transformers. The study concludes that targeted investment in maintenance, automation, and workforce training can significantly reduce outages and improve service continuity.

DOI: https://doi.org/10.5281/zenodo.18427035

 

Published by: vikaspatanker

Authors: Mr. Danish Ishfaq, Ms. Aasifa Jan

Abstract: Artificial Intelligence (AI) and robotic surgical systems represent transformative technologies in modern healthcare, with profound potential for enhancing surgical precision, reducing operative risk, and improving patient outcomes. In the Indian context, research and clinical practice are increasingly exploring this convergence, encompassing both academic inquiry and real-world deployments. This paper synthesizes recent literature on AI integration with robotic surgery, highlights Indian research efforts, examines clinical case developments, identifies technical and ethical challenges, and discusses future directions for advancing AI-enabled surgical robotics within India’s healthcare ecosystem.

DOI: https://doi.org/10.5281/zenodo.18426331

Published by: vikaspatanker