Electrical & Electronics Engineering
Permanent URI for this collectionhttp://197.211.34.35:4000/handle/123456789/131
Electrical & Electronics Engineering
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Item 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 AmbafiIn 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.Item Effect of Power Factor of a Synchronous Machine on Eccentricity Faults Classification Accuracies(IEEE, 2024-09-12) Yusuf, Latifa; Shejwalkar, Ashwin; Ilamparithi, Thirumarai ChelvanThe 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.Item Dynamic Eccentricity Fault Detection in Synchronous Machines Using Principal Component Analysis(IEEE, 2023-09) Yusuf, Latifa; Ilampari, Thirumarai ChelvanThe 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.