Daily Archives: November 12, 2025

Authors: J. Jenitta, Amulya N, Keerthi Kandakur, Nithin N, Shashank G

Abstract: As the online socialization process advances, more and more individuals are reporting their emotional conditions online and in many instances showing symptoms of misery and suicidal tendencies, which present significant obstacles to monitoring mental health. Conventional techniques of detecting such content are often untimely and ineffective and it is based on this need that automated and real time detection is important. In this paper, the author suggests a deep learning-based suicide risk prediction system based on linguistic data obtained on social media. The model achieves a high degree of accuracy in differentiating between texts that are suicidal and those that are not by learning the contextual semantics using bidirectional long short-term memory (Bi-LSTM) networks. The system is coupled with an IoT-based alert system to make sure that timely intervention is applied to prevent suicidal intent by sending alarms to caregivers or other interested parties whenever any suicidal intent is identified. The results of the testing show that the proposed method performs better in comparison to the traditional machine learning classifiers that are measured in terms of precision, recall, and F1-score. This combination related to the higher-order NLP techniques and the use of IoT-driven indicators offers a scalable and productive approach to stopping destructive online behavior. This article highlights the role of artificial intelligence and IoT in the improvement of digital mental health support systems.

DOI: http://doi.org/10.5281/zenodo.17590356

Published by: vikaspatanker

Authors: Hachimenum Nyebuchi Amadi, Henry Okechukwu Williams, Richeal Chinaeche Ijeoma

Abstract: The rapid integration of electric vehicle (EV) fast charging stations in DC micro-grids has introduced significant power quality challenges, particularly harmonic current distortion at the point of common coupling (PCC). In this study, a DC microgrid integrating photovoltaic (PV) generation, battery energy storage systems (BESS), and a Level-3 EV fast charging station was modeled in MATLAB/Simulink to examine the effect of harmonic distortion and evaluate mitigation using an Artificial Neural Network (ANN)-controlled Static Synchronous Compensator (STATCOM). Base case simulation results revealed that the EV fast charging station injected excessive harmonic distortion into the network, with dominant odd harmonics at the 11th and 13th orders, leading to a total harmonic distortion (THD) of 14.05%. This value significantly exceeds the IEEE 519-2022 standard limit of 8% for medium-voltage systems. Following the installation of an ANN-tuned STATCOM at the PCC, the harmonic distortion was substantially mitigated, reducing the 11th and 13th orders to 0.01% and 0.15% respectively. Consequently, the total harmonic distortion was minimized to 1.23%, achieving a 91.24% reduction and ensuring full compliance with IEEE standards. Furthermore, the ANN controller demonstrated excellent training performance with a best validation mean square error of 0.0034611 at epoch 20 and a regression correlation coefficient of R = 0.9879, validating its accuracy and robustness. These findings confirm that ANN-controlled STATCOM provides an effective and intelligent solution for enhancing power quality and system stability in DC micro-grids with EV fast charging integration.

DOI: http://doi.org/

Published by: vikaspatanker

Published by: vikaspatanker

Authors: Hachimenum Nyebuchi Amadi, Iyowuna Winston Gobo, Ugochi Benedicta Uche-Ibe, Richeal Chinaeche Ijeoma

Abstract: The reliance on fossil fuels and the need for effective battery management are significant challenges that renewable micro-grids seek to address. Fluctuations in supply and demand often result in higher operational costs and increased dependence on the external grid. With the urgent need to confront energy and environmental issues like global warming, transitioning to clean energy sources is becoming more viable. This study focuses on the Jetty 11kV feeder from the Abuloma 33kV injection substation in Port Harcourt, with an installed capacity of 1 x 7.5MVA. Currently, the feeder has a peak load of 3.9MW and an average load of 2.2 MW. To leverage the local abundance of water, the research aims to design a micro-grid using solar and wind energy. Using MATLAB Simulink, data from NASA meteorological sources will be simulated. The design features a 4.5MW photovoltaic (PV) array, a 2.5 MW wind energy source, and a 4 MWh battery storage unit. Despite variations in irradiance, significant improvements in power extraction were observed, with up to 4.5 MW generated by the PV array and 2.5MW by the wind turbine during peak times. The battery can be fully charged in four hours, and it was maintained at 40% capacity during low energy output periods. The State of Charge (SoC) of the battery showed dynamic behavior, enabling it to respond effectively to system imbalances and enhance microgrid resilience. A fuzzy logic controller (FLC) was used to manage charge and discharge cycles according to real-time parameters, ensuring reliable micro-grid operation even with low battery levels. The economic analysis revealed an initial cost of ₦990,251,352.19, a replacement cost of ₦414,669,375.12, a net present cost (NPC) of ₦10,813,540,000.00, and a levelized cost of electricity (COE) of ₦230.90/kWh. The low operation and maintenance (O&M) costs associated with renewable energy reduce reliance on the conventional grid and prolong infrastructure lifespan. Environmental assessments indicated a total greenhouse gas emission of 15,330,621 kg/year, significantly lower than that of conventional systems. The results confirm that the optimized hybrid renewable energy micro-grid enhances energy balance and resilience, showcasing its feasibility as a cost-effective and environmentally sustainable alternative to traditional power generation. The research aims to improve system resilience, reduce operating costs, and enhance micro-grid efficiency.

DOI: http://doi.org/10.5281/zenodo.17587580

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Authors: Hachimenum Nyebuchi Amadi, Biobele A. Wokoma, Victor Nneji Chikwendu, Richeal Chinaeche Ijeoma

Abstract: A micro-grid is a localized energy system that typically operates as part of a larger, wide-area synchronous grid but can function independently when necessary. It comprises energy generators, loads, storage units, and control systems, all highly integrated and manageable. This study presents the design and implementation of a 60,000-watt solar photovoltaic (PV) microgrid incorporating an advanced fault detection and localization mechanism, aimed at addressing the limitations of conventional reactive fault systems. These traditional systems often respond only after fault currents surpass the tolerance thresholds of grid components, leading to reduced efficiency, equipment damage, or total system failure. To mitigate these issues, a DC micro-grid consisting of six solar PV arrays was modeled using Proteus 8.15 Professional and Siemens TIA Portal. Each array comprised 32 units of 400W, 12V panels arranged in an 8×4 configuration, delivering 72V per array. The PV arrays were individually connected through dedicated contactors (MCB1–6). Fault detection and isolation were achieved using smart electronics, specifically Arduino Nano microcontrollers integrated with WCS1600 current sensors capable of sensing up to 500A. The system efficiently identified and isolated faults occurring within any array. During testing, no faults were flagged for current values of 72.33A, 90.42A, 123.69A, 117.15A, 172.02A, and 199.09A, as they remained within the safe 200A threshold. However, overcurrent values recorded at PV arrays 3, 4, 5, and 6 (235.09A, 307.43A, 412.72A, and 209.09A, respectively) due to simulation of fault (short circuit, load-related faults, battery system faults, DC bus fault or converter and distribution fault) were promptly detected, and the affected arrays were disconnected to protect the system. Compared to previous research, this approach leveraging a hybrid of Arduino Microcontroller and Siemens S7-1200 PLC (CPU1214CDC/DC/DC) demonstrated improved efficiency and reliability in proactive fault detection and localization. Ultimately, the study successfully developed a programmable, feedback-enabled microgrid system capable of anticipating and mitigating faults before component tolerance limits are breached.

DOI: https://doi.org/10.5281/zenodo.17587332

 

Published by: vikaspatanker