COVENANT JOURNAL OF ENGINEERING TECHNOLOGY

An Improvement of Load Flow Solution for Power System Networks using Evolutionary-Swarm Intelligence Optimizers

Thu, 18 Jan 2024 00:00:00 +0000

Load flow report which reveals the existing state of the power system network under steady operating conditions, subject to certain constraints is being bedeviled by issues of accuracy and convergence. In this research, five AI-based load flow solutions classified under evolutionary-swarm intelligence optimizers are deployed for power flow studies in the 330kV, 34-bus, 38-branch section of the Nigerian transmission grid. The evolutionary-swarm optimizers used in this research consist of one evolutionary algorithm and four swarm intelligence algorithms namely; biogeography-based optimization (BBO), particle swarm optimization (PSO), spider monkey optimization (SMO), artificial bee colony optimization (ABCO) and ant colony optimization (ACO). BBO as a sole evolutionary algorithm is being configured alongside four swarm intelligence optimizers for an optimal power flow solution with the aim of performance evaluation through physical and statistical means. Assessment report upon application of these standalone algorithms on the 330kV Nigerian grid under two (accuracy and convergence) metrics produced PSO and ACO as the best-performed algorithms. Three test cases (scenarios) were adopted based on the number of iterations (100, 500, and 1000) for proper assessment of the algorithms and the results produced were validated using mean average percentage error (MAPE) with values of voltage profile created by each solution algorithm in line with the IEEE voltage regulatory standards. All algorithms proved to be good load flow solvers with distinct levels of precision and speed. While PSO and SMO produced the best and worst results for accuracy with MAPE values of 3.11% and 36.62%, ACO and PSO produced the best and worst results for convergence (computational speed) after 65 and 530 average number of iterations. Since accuracy supersedes speed from scientific considerations, PSO is the overall winner and should be cascaded with ACO for an automated hybrid swarm intelligence load flow model in future studies. Future research should consider hybridizing ACO and PSO for a more computationally efficient solution model.

Low-cost Car Battery Security Alert System

Tekena Kashmony Bala, Otonye Ene Ojuka, Kinba Q. Blue-Jack , Precious E. Owhebo — Thu, 08 Feb 2024 00:00:00 +0000

This is a technical article that showcases a low-cost car battery security alert system that utilizes a multi-vibrator circuit into a dual-tone multi-frequency (DTMF) output (loud beep sound) alarm to monitor and safeguard the car battery from local theft. The entire circuitry is a simple one and low-cost in production. The incessant cases in which car batteries are stolen especially in developing countries is on the high side. And the cost of replacing car batteries is on the increase daily. Therefore, one needs to secure her car battery from street theft. Using both mechanical fastening and electronic security-based systems one could sleep with two eyes closed. The device serves as an electronic watchdog on the car battery in the car while it is parked outside the owner’s residence or elsewhere. The security system is provided with an internal rechargeable battery energizing the alarm circuitry, having a single-pole double-throw (SPDT) relay, and connected cables, with an output sound capable of alerting the neighbourhood. Whenever the car battery is disconnecting from the terminal heads or the loop cable is broken the connected alarm will be triggered and this will call the attention of the neighbourhood and the owner thereby deterring the intruder. The entire system was simulated using Circuit Wizard software with good results. The system was fabricated using discrete semiconductor devices that are relatively simple and available for operation and maintenance, packaged, and tested. The circuit voltage is 11.52 volts and draws a current of 3.79A resulting in a wattage of 44 watts. The device is affordable.

Mitigating the Event and Effect of Energy Holes in Multi-hop Wireless Sensor Networks Using an Ultra-Low Power Wake-up Receiver and an Energy Scheduling Technique

Fri, 09 Feb 2024 00:00:00 +0000

This research work presents an algorithm for extending network lifetime in multi-hop wireless sensor networks (WSN). WSNs face energy gap issues around sink nodes due to the transmission of large amounts of data through nearby sensor nodes. The limited power supply to the nodes limits the lifetime of the network, which makes energy efficiency crucial. Multi-hop communication has been proposed as an efficient strategy, but its power consumption remains a research challenge. In this study, an algorithm is developed to mitigate energy holes around the sink nodes by using a modified ultra-low-power wake-up receiver and an energy scheduling technique. Efficient power scheduling reduces the power consumption of the relay node, and when the residual power of the sensor node falls below a defined threshold, the power emitters charge the nodes to eliminate energy-hole problems. The modified wake-up receiver improves sensor sensitivity while staying within the micro-power budget. This study's simulations showed that the developed RF energy harvesting algorithm outperformed previous work, achieving a 30% improvement in average charged energy (AEC), a 0.41% improvement in average energy (AEH), an 8.39% improvement in the number of energy transmitters, an 8.59% improvement in throughput, and a 0.19 decrease in outage probability compared to the existing network lifetime enhancement of multi-hop wireless sensor networks by RF Energy Harvesting algorithm. Overall, the enhanced power efficiency technique significantly improves the performance of WSNs.

An Experimental Study on Bitumen Properties Modified with Polypropylene Polymer from Waste Disposable Cups for Flexible Pavement Applications

Tue, 27 Feb 2024 00:00:00 +0000

Waste disposal, especially plastic waste, is of great concern worldwide due to its non-biodegradable nature. Because these wastes remain on Earth for thousands of years without any degradation or decomposition, they pose health, environmental, and social concerns and occupy valuable space. Also, the significantly higher axle load and traffic volume beyond design limits result in serious pavement deterioration problems. Consequently, incorporating these waste plastics into road construction may present environmental and engineering benefits. Thus, this investigation assesses the impact of including waste plastic cups as bitumen modifiers. Firstly, FTIR analysis was carried out in the laboratory on the waste modifier material showed it mainly comprised of polypropylene. Secondly, a study conducted on the physical features of bitumen with and without the modifier discovered that the modifier decreased the penetration, specific gravity, and ductility by 69.90%, 3.40%, and 42.60%, respectively, whereas it increased the softening point by 48.0%. Therefore, a substantial enhancement in temperature vulnerability and stability of the bitumen can be achieved by incorporating the modifier. Thirdly, the FTIR analysis conducted on the pure and modified bitumen showed the presence of extra new peaks in the structure of the modified bitumen. Thus, the features of this modified bitumen with increasing modifier content resulted from the chemical change in its structure. Finally, the One-way analysis of variance (ANOVA) conducted at various bitumen contents revealed less than a 5% significant level, indicating that the modifier had a substantial impact on pure bitumen. The findings from this study present an vital reference for the improvement in hot mix asphalt properties as well as solid waste management and utilization.

Enhancing Human Identification Systems Through Bi-modal Fusion Using Negative Selection Algorithm

Monsurat O. Balogun, Bilkisu Jimada-Ojuolape, Latifat A. Odeniyi — Tue, 27 Feb 2024 00:00:00 +0000

The Negative Selection Algorithm (NSA) is a computational technique inspired by the human immune system and widely used in various fields like intrusion detection, network security, data mining, and pattern recognition. However, its effectiveness in human identification has not been thoroughly explored. This study focuses on utilizing NSA for human image classification, specifically in a bi-modal system combining physiological traits (faces and fingerprints) and behavioral traits (signatures and voices), as well as a uni-modal system using all features. The research collected 2400 images from 200 individuals, pre-processed images, and salient features selected for easy classification. NSA was used for image classification in both bi-modal and uni-modal systems. The results demonstrated NSA's effectiveness, particularly in the bi-modal system. The biometric system that fused behavioral traits exhibited high accuracy, with true positive and true negative rates of 141% and 144%, respectively, and an overall accuracy of 95%. The system is based solely on physiological traits and achieved slightly lower accuracy rates at 89%. Furthermore, among the uni-modal systems, the voice-based system stood out with a true positive rate of 131% and an accuracy of 88.33%. These findings emphasize the advantages of combining different biometric traits, showcasing the potential for increased accuracy in identification systems. The study highlights NSA's role in enhancing classification accuracy, suggesting the developed biometric systems could significantly improve the performance and reliability of various integrated identification systems.

Optimization and Predictive Models on Strengths and Durability of Reinforced Laterized Concrete

Isah Garba, Tasiu Ashiru Sulaiman, Jibrin Mohammed Kaura, Musa Abdullahi — Tue, 09 Apr 2024 00:00:00 +0000

The increasing demand for fine aggregates in turn has led to the astronomical increase in the cost of procuring construction materials thereby making it difficult to meet the shelter provision requirement of the teeming population of a country such as Nigeria. This study aims at optimizing and developing predictive models on the strengths and durability of reinforced laterized concrete using response surface methods with laterite being used as a partial replacement of fine aggregate in reinforced concrete. In this study, the optimized laterite content was determined at replacement level of laterite to fine aggregates at 0, 10, 20, 30 and 40% with a mix ratio of 1:2:4 and a water cement ratio of 0.65, while for the water absorption, the percentages replacement of laterite to fine aggregates was 0, 10 and 20%. The results of the strength and the durability obtained from the laboratory were subjected to statistical analysis using design expert software version 13(2021). The strength and the durability were found to be 19.044 N/mm², 27.304 N/mm² and 3.809% for the compressive strength, flexural strength and water
absorption at 10% laterite content at 28 days curing age. It was concluded that, to achieve the maximum strength (compressive and flexural) and durability requirements of laterized concrete should be produced by replacing fine aggregate with 10% laterite and cured at 28 days. The developed models could be used for the predictions of desired strengths and durability of the reinforced laterized concrete.