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Ali, H. A. E. ‑S., M. H. Alham, and D. K. Ibrahim, "Big data resolving using Apache Spark for load forecasting and demand response in smart grid: a case study of Low Carbon London Project", Journal of Big Data, vol. 11, issue 1, pp. Article no. 59, 2024. Abstract

Using recent information and communication technologies for monitoring and management initiates a revolution in the smart grid. These technologies generate massive data that can only be processed using big data tools. This paper emphasizes the role of big data in resolving load forecasting, renewable energy sources integration,
and demand response as significant aspects of smart grids. Meters data from the Low Carbon London Project is investigated as a case study. Because of the immense stream of meters’ readings and exogenous data added to load forecasting models, addressing the problem is in the context of big data. Descriptive analytics are developed using Spark SQL to get insights regarding household energy consumption. Spark MLlib is utilized for predictive analytics by building scalable machine learning models accommodating meters’ data streams. Multivariate polynomial regression and decision tree models are preferred here based on the big data point of view and the literature that ensures they are accurate and interpretable. The results confirmed the descriptive
analytics and data visualization capabilities to provide valuable insights, guide the feature selection process, and enhance load forecasting models’ accuracy. Accordingly, proper evaluation of demand response programs and integration of renewable energy resources is accomplished using achieved load forecasting results.

Shafei, M. A. R., D. K. Ibrahim, A. El-Zahab, E. El-Din, and M. A. A. Younes, "Biogeography-Based Optimization Technique for maximum power tracking of hydrokinetic turbines", Renewable Energy Research and Application (ICRERA), 2014 International Conference on: IEEE, pp. 789–794, 2014. Abstract

Hydrokinetic energy, referring to the energy contained in moving water, is a renewable energy source that gained much consideration in the past years and expected to play a significant role in the future. The energy is initiated in all moving water masses, but is significantly economic to convert for water masses moving with high velocity. Nonlinear characteristics of water speed and generator model in hydrokinetic energy conversion systems require an optimal controller for achieving optimal performance and high efficiency of the system. Here, the field oriented control method is proposed to set the PI controllers which their coefficients are optimized based on Biogeography- Based Optimization technique (BBO). In order to use BBO to solve this problem, the problem has to be formulated as an optimization problem. Numerous simulation studies are carried out to verify the effectiveness of the proposed controller scheme. Achieved results for different patterns of water speed changes in time domain show the capability of the proposed control.

Atta, M. E. E. - D., D. K. Ibrahim, and M. I. gilany, "Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art", IEEE Access, vol. 10, pp. 88504 - 88526, 2022. AbstractWebsite

Motors are the higher energy-conversion devices that consume around 40% of the global electrical generated energy. Induction motors are the most popular motor type due to their reliability, robustness, and low cost. Therefore, both condition monitoring and fault diagnosis of induction motor faults have motivated considerable research efforts. In this paper, a comprehensive review of the recent techniques proposed in the literature for broken bar faults detection and diagnosis is presented. This paper mainly investigates the fault detection methods in line-fed and inverter-fed motors proposed after 2015 and published in most relevant journals and conferences. The introduced review has deeply discussed the main features of the reported methods and compared them in many different aspects. Finally, the study has highlighted the main issues and the gaps that require more attention from researchers in this field.

Atta, M. E. E. - D., D. K. Ibrahim, and M. Gilany, "Broken Bar Faults Detection under Induction Motor Starting Conditions Using the Optimized Stockwell Transform and Adaptive Time-Frequency Filter", IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. Article 3518110, 2021. Abstract

Most of the published research studies for detecting induction motor broken bar faults (BBFs) use a time–frequency (t − f ) decomposition tool to characterize the fault-related components. However, the identification and the assessment of these components in (t − f ) domain require skilled user or powerful pattern recognition technique. Moreover, a relatively long starting duration is necessary. This article introduces an automated scheme to detect BBFs and distinguish fault severity in induction motors under startup conditions regardless of the user experience and even under short starting duration and in a noisy environment. This scheme is based on the analysis of the starting current using optimized Stockwell transform (ST). An active set algorithm is applied to maximize the energy concentration of the left-side harmonic (LSH) component. Then, an adaptive time–frequency filter is applied to extract the LSH component from the (t − f ) domain, where the energy of the right part of LSH (RLSH) is utilized as an effective index for BBFs detection and for discriminating BBFs severity. Both real experimental data and simulation-based tests on 0.746- and 11-kW motors are used to extensively verify the performance of the proposed scheme. The achieved results have ensured that the proposed scheme can achieve a high accuracy with the minimum data and shortest acquisition time in comparison with some recent methods in the literature.

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