Publications by authors named "Ahmed Abu-Siada"

The insulators of overhead power lines play a crucial role in maintaining the reliability of transmission and distribution networks. Because they are exposed to harsh and dynamic environmental conditions, it is essential to investigate the impact of environmental parameters such as pollution, inclined angle with the cross arm, and temperature on the dielectric performance of the insulators of overhead lines. Conventionally, the effect of such parameters can be investigated through experimental measurements of the insulator flashover voltage.

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Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent research shows that deep neural networks (DNNs) are capable of achieving accurate STLP since they are effective in predicting nonlinear and complicated time-series data.

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Accurate interpretation of dissolved gas analysis (DGA) measurements for power transformers is essential to ensure overall power system reliability. Various DGA interpretation techniques have been proposed in the literature, including the Doernenburg Ratio Method (DRM), Roger Ratio Method (RRM), IEC Ratio Method (IRM), Duval Triangle Method (DTM), and Duval Pentagon Method (DPM). While these techniques are well documented and widely used by industry, they may lead to different conclusions for the same oil sample.

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Classic core-based instrument transformers are more prone to magnetic saturation. This affects the measurement accuracy of such transformers and limits their applications in measuring large direct current (DC). Moreover, protection and control systems may exhibit malfunctions due to such measurement errors.

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