Browsing by Author "Abdullateef , Ayodele Isqeel"

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    Evaluation of Power Loss Based on Backward Forward Sweep Algorithm: A Case Study of Igosun Feeder Offa, Nigeria
    (Ekiti State University Ado-Ekiti, 2023) Abdullateef , Ayodele Isqeel; Gbenle, Abimbola Joan; Alao, Rasaq Atanda; Raji, Bello Sunday
    Electrical energy is usually generated long distances from the load centres; therefore, it needs an extensive network of conductors to bring it to the consumers. Due to the long distance covered by these conductors, power losses occur in the distribution system. Power losses affect the system's efficiency, reducing grid reliability, voltage instability, needless switchgear tripping, and blackouts. Power losses could be technical and non-technical. Technical losses are inherent due to the component on the distribution network; thus, it is unavoidable and can only be reduced, not eliminated. This paper evaluates the power loss on the 11 kV Igosun distribution feeder, Offa, Kwara State using the backward forward sweep algorithm implemented in MATLAB platform. The data, extracted from the feeder's topology, were used to calculate the corresponding real and reactive power load at the respective buses. The results show a total real power loss of 873.5688 kW and a total reactive power loss of 504.3703 kVAR on the feeder. The knowledge of the feeder's power losses will benefit electric power utilities to improve the quality of the power supply and revenue generation.
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    IoT-Enabled Real-Time Monitoring and Loss-of-Life Estimation of Distribution Transformers
    (Universitas Sriwijaya, Indralaya, Indonesia, 2025) Abdullateef , Ayodele Isqeel; Issa, Abdulkabir Olatunji; Abdullahi, Sulaiman; Issa, Abdulrasheed Yinka; Onasanya, Mobolaji Agbolade
    A distribution transformer is required in power distribution networks to step down the voltage relevant and usable for consumers. Its failure not only disrupts electricity supply but also incurs high replacement costs, with broader economic implications. Ensuring reliable operation, therefore, requires accurate and continuous monitoring of its performance. This paper presents IoT-Enabled Real-Time Monitoring and Loss-of- Life Estimation of Distribution Transformers developed and tested on a 10 kVA, 0.415 kV prototype distribution transformer, connected to three residential loads. A dedicated data acquisition system was developed, which monitors key parameters: load current, phase voltage, transformer oil level, ambient temperature, and oil temperature in real time over 14 days. An algorithm was implemented to analyze daily load profiles and hotspot temperature data, which were then used to estimate transformer loss of life. The results show that transformer ageing is highly sensitive to load variation. During weekdays, the cumulative equivalent ageing reached 2.22 hours per day, corresponding to a daily loss of life of 0.00296%. On weekends, higher residential loads increased cumulative ageing to 4.79 hours, with a corresponding life loss of 0.0063%. A simulated one-hour peak load of 1.43 pu resulted in 25.75 hours of ageing, translating to a life loss of 0.034%, demonstrating the severe impact of overloads. These findings emphasize that peak load periods dominate insulation ageing and can substantially reduce service life if unchecked.