School of Electrical Engineering and Technology (SEET)

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School of Electrical Engineering and Technology (SEET)

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Start date: 2021

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    Coordination of Flexible Alternating Current Transmission Systems and Distributed Generation in a Synthetic Co-simulation of Transmission and Distribution Network
    (Turkish Journal of Electrical Power and Energy Systems (TEPES), 2024-02) Ahmad, Abubakar Sadiq; Yusuf, Latifa; Muhammad, Buhari; Sanusi Sani Adamu; James, Garba Ambafi
    In ensuring sustainable power delivery under rapid growth in demand, modern power grids are characterized by advanced solutions such as flexible alternat ing current transmission systems and distributed generation. However, flexible alternating current transmission systems and distributed generations are often planned by their respective system operators, ignoring their coordination and impacting system-wide performance. This paper develops a bi-level optimization approach for flexible alternating current transmission systems and distributed generation coordination in an integrated transmission and distribution network to improve available transfer capability, power losses, and voltage deviation. The approach comprises inner and outer optimization. Inner optimization imple ments a hybrid of particle swarm optimization and Active Power Flow Performance Index for flexible alternating current transmission systems’ planning. At the same time, the outer optimization employs multi-objective particle swarm optimization, which targets distributed generation planning at the distribution network—the integrated transmission and distribution network models’ both transmission and distribution section. To demonstrate the effectiveness of the developed approach, two models of distributed generations, only real power and real and reactive power injections, were separately coordinated with a thyristor-controlled series compensator and static synchronous series compensator. Results show superior available transfer capability enhancement with thyristor-controlled series compensator−power injectionsDG and static synchronous series compensator−power injectionsDG, compared to the non-coordinated scenario. Pareto front plots of available transfer capability, power losses, and voltage deviation are such that after some maximum available transfer capability, the slope of the Pareto approaches zero.
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    Effect of Power Factor of a Synchronous Machine on Eccentricity Faults Classification Accuracies
    (IEEE, 2024-09-12) Yusuf, Latifa; Shejwalkar, Ashwin; Ilamparithi, Thirumarai Chelvan
    The research work studies the effect of changing power factor of a Salient Pole Synchronous Machine (SPSM) on eccentricity fault classification accuracies of machine learning and deep learning models. The SPSM was subjected to static eccentricity (SE) and dynamic eccentricity (DE) with a severity of forty percent. Data was collected at different operating conditions, such as lagging, leading, and unity power factor. The data was used to train an Artificial Neural Network (ANN) and a one-dimensional Convolutional Neural Network (1D CNN) for eccentricity fault classification. Results show that the SPSM’s changing power factor significantly affected the classification accuracy of both neural networks.
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    Dynamic Eccentricity Fault Detection in Synchronous Machines Using Principal Component Analysis
    (IEEE, 2023-09) Yusuf, Latifa; Ilampari, Thirumarai Chelvan
    The paper proposes a new method for detecting dynamic eccentricity faults in a Salient Pole Synchronous Machine (SPSM). Several methods have been documented in the literature for detecting dynamic eccentricity, including using search coils, motor current signature analysis (MCSA), and data-based techniques. The former approach is invasive, thereby increasing installation cost, while MCSA is affected by load variations. A major hurdle in using data-driven methods is the selection of features. To overcome these limitations, the authors propose performing principal component analysis (PCA) on the fundamental sideband frequencies of motor current signals to detect and isolate dynamic eccentricity from static eccentricity faults. Principal Component Analysis (PCA) is a data-driven technique that can extract meaningful features in a dataset by transforming correlated variables into a reduced set of uncorrelated variables through a linear transformation. Experimental data of stator currents from a 2-kW, 208 V, 3-phase SPSM were used for the analysis. Results showed that the method isolated dynamic eccentric faults irrespective of the loading condition of the SPSM.
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    Development of an Intelligent Evaporative Cooling System for Post-harvest Storage of Tomato
    (University of Ilorin, Nigeria, 2024-06-04) Isah, O.R., Adebayo S.E., Nuhu, B.K., Umar, B.U., Maliki, D., Abdullahi, I.M., Dogo, E.M, Olaniyi, O.M., & Agajo, James
    This research developed an intelligent evaporative cooling system for post-harvest tomato preservation that adapts to its suitable temperature, humidity, and CO 2 states to store, preserve quality, and increase the shelf life of the fruits. This was accomplished through the use of transfer learning for fruit classification, the Internet of Things (IoT) for remote monitoring and shelf life tracking, and the integration of the evaporative cooling system with a CO 2 sensor, a temperature sensor, a humidity sensor, an Arduino Uno, and a Raspberry Pi 4b. The system can classify tomato fruit status as ripe or overripe with a prediction accuracy of 87.5% and a receiver operating characteristic (ROC) value of 88.89%. The developed evaporative cooling system extended the shelf life of ripe tomatoes from 5 to 14-17 days at 20℃ and 90% relative humidity and overripe tomatoes from 3 to 9-11 days at 18℃ and 95% relative humidity. These results emphasize the crucial function of evaporative cooling in fruit and vegetable storage, as it extends the shelf life of tomatoes by 180–200%, hence minimizing post-harvest loss as it also increases the farmers’ income, thereby contributing positively to the economy.
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    Food safety forecasting using internet of things and machine learning
    (ABU-Zaria, 2023) Fadeyi John Oluwole; A. U. Usman; Oyewobi S. Stephen
    Food sector is a significant part of the economy but it faces challenges with food spoilage, especially in meat, fruits, and vegetables. This issue involves food items, especially meat, fruits, and vegetables, going stale and often reaching consumers unnoticed. Additionally, during the food chain there may be instances where the food may still be within the proposed shelf life but may be spoilt before it gets to the consumer, therefore, it is important to test them and envisage when it will be inedible. This paper presents a predictive model which is used to forecast when the fruits will he inedible via the use of time series data generated from internet of things (loT) based device. The loT device developed in this research is used to monitor the decline of the freshness of the fruit to the state of inedibility. This device measures parameters such as alcohol, and ammonia around the fruit, as such large amounts of real-time data are generated. A web server is used for the storage of data values sensed in real time and also for the analysis of results. Long Short-Term Memory (LSTM) predictive model is used to forecast the time the fruit will be inedible via the use of time series data harvested from the cloud. The implementation of this technology enhances traceability. minimizes food wastage, and, most importantly, protects consumers from foodborne illnesses. Keywords: Food safety, loT, Machine learning
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    Air Gap Length and Maximum and Minimum Inductance of A Double-Salient Reluctance Machine (DSRM) using The Developed Analytical Algorithm
    (International Journal on Electrical Engineering and Informatics, 2022-03-12) Enesi Asizehi Yahaya; Ajah Victor; Emenike Chinedozi Ejiogu
    In this paper an algorithm is developed to predict the maximum and minimum inductance of an optimized pole arc of a fabricated double salient reluctance machine (DSRM). The developed analytical algorithm investigates the air gap length at the maximum and minimum inductances. These are achieved through the machine rotor pole arc, stator pole arc, bore diameter, back iron thickness, rotor yoke thickness, stack length, and rotor pole diameter. The validity of the newly developed analytical procedures for predicting the maximum and minimum inductances was evaluated using the measurements obtained from the machine prototype. There was a good correlation between the analytical and experimental results. Inductances and air gap lengths of 2/2 DSRM of equal stator and rotor pole width and stator pole width greater than the rotor pole width were investigated and analyzed using the developed algorithm. A second existing DSRM with different numbers of stator and rotor poles whose inductances have been known and compared with finite element analysis (FEA) is chosen to verify the algorithm developed. The 2/2 DSRM’s potential to sustain oscillation during energy conversion from mechanical energy to electrical energy is investigated
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    Enhanced Adaptive Threshold Median Filter For Medical Image Filtering
    (OURNAL OF SCIENCE TECHNOLOGY AND EDUCATION, 2023-06-12) Adamu, M., Jiro, A.A., Abdul-Malik, U. T
    In the field of medical image processing, mitigating the impact of noise is of paramount importance. Conventional median filters primarily target the elimination of medical image noise occurring as a single layer, characterized by a constant level of noise gray value. However, these filters encounter challenges when faced with images corrupted by noise that extends beyond a single layer. This study presents the Enhanced Adaptive Threshold Median Filter (EATMF) as a solution to naddress the aforementioned challenge. The proposed filter combines the Adaptive Median Filter (AMF) with thresholds (ATMF) and incorporates a Laplacian filter. By introducing changes in the thresholds, the EATMF achieves a balance between effectively removing both low and high density noise while preserving image quality. A comparative analysis between the EATMF and the ATMF is presented, accompanied by visual examples that showcase the performance of the newly introduced filter. The results demonstrate that the EATMF outperforms the ATMF in terms of Peak Signal-to-Noise Ratio (PSNR), indicating its superior noise reduction capabilities. This study highlights the significance of the EATMF in medical image processing, particularly in scenarios where images are corrupted by multi-layer noise. The proposed filter offers an enhanced approach to noise reduction, contributing to improved image quality and accuracy in medical diagnostics and analysis.
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    Development of Animal Health Monitoring System based on Wireless Sensor Network
    (Journal of Contents Computing, 2022-12-10) Maliki, D., Ogunbase, E. F., Abdullahi, I. M., Aliyu, I., Oh, S., & Dauda, I. A
    Clinical methods for tracking animal health are inadequate as they only include intermittent data which entail too much time and veterinarian knowledge expenditure in equipment. The animal health monitoring system which allocates equipment to be installed on the animal body does provide way of keeping the health of the animal in check. This project seeks to solve the problem of integration in system developed. Also, monitoring of psychological parameters has to be consistent (precision), accuracy and the response time of the system has to be low for a real time monitoring system. The project seeks to achieve a level of accuracy and precision to help diagnose the health situation of the animal. The system consists of two sensors (a temperature sensor, a heart rate sensor). For the implementation of the temperature node, esp 32 which has wifi capability was used while in the case of heart rate, Arduino Nano was interfaced alongside with esp32. The sensor nodes communicate with the sink node which serves as the display unit and also transmit the data to the cloud for real time monitoring. The precision and accuracy achieved by the ECG, modularity built into the system and the deep sleep energy-saving mechanism of the sensor nodes are achievement made by this work.
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    Food Safety 4.0: The Future of Food Safety Leveraging Industry 4.0 Technologies
    (Springer, 2024) Dogo, E. M.; Bokaba, T.; Makun, H. A.; Aliyu, A.; Kparbong, P. B.
    The convergence of several factors, including population growth, scarce natural resources, climate change, globalization, sustainability, and advances in modern and emerging technologies, in addition to growing demand by consumers, retailers, regulators, and stakeholders to raise the level of food protection is increasingly driving the global food safety agenda. Industry 4.0 technologies are fast infusing into all fields of human endeavor including the food safety ecosystem, serving as catalysts for innovation and sustainability. However, there is no empirical evidence regarding the extent of their application and the level of acceptance within the food safety domain. This study explored the relationship between Industry 4.0 technologies and food safety by evaluating the applications of the Fourth Industrial Revolution technologies in addressing food safety challenges. The objectives are achieved using qualitative methodology and bibliometric analysis of content. Our analysis indicates that artificial intelligence, the Internet of Things, machine learning, and big data are prominent topics related to the 4IR and food safety, while blockchain and smart manufacturing are emerging topics.
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    DEVELOPMENT OF AGILE PRODUCTIVITY METRICS OF INDIVIDUAL EXPERT JAVASCRIPT DEVELOPERS FOR SOFTWARE PROJECT MANAGERS
    (Humminbird Publications and Research International, 2024-01-29) Abdulgafar A.; Makinde J.K; Ajiboye, Johnson Adegbenga; Ajiboye M.A
    Software Project Managers require metrics to measure productivity in team work. Since agile software development require continuous improvement, metrics helps in identifying bottlenecks and inefficiencies thereby enabling teams to refine their processes iteratively. Productivity metrics also helps in effective resource allocation and optimization to ensure timely delivery of software products by Software Project Managers. Although metrics have been developed for traditional software programmers little work has been done in developing metrics for Agile Software Project Managers specifically for JavaScript Program. In this work, metrics for Individual Expert agile software programmers and specifically for JavaScript was developed. Programs in JavaScript was designed and developed to record the time spent in correcting deliberate errors introduced. Experiment was conducted among one hundred programmers' group of Individual Expert pairs with the aim of recording time spent in debugging the codes, The curve fit regression models of time spent in debugging a number of bugs in agile software written in JavaScript programming language for project managers revealed that Cubic model had the highest R squared value of 0.996 which is closely followed by the quadratic model with a value of 0.980 while the compound, growth and exponential models have the least value of 0.868.