Yazar "Noori, Harith Muthanna" seçeneğine göre listele

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    Transmission line loss determination of electricity by using convolutional neural network
    (Institute of Electrical and Electronics Engineers Inc., 2022) Sadeq Al-Samkri, Elaf Hayder; Al-Jumaili, Saif; Noori, Harith Muthanna; Duru, Adil Deniz; Uçan, Osman Nuri
    Businesses are concerned about energy losses. Electronic gadgets have become more prevalent as a result of their adoption. The frequency with which home electricity usage data is collected has grown, allowing for sophisticated data analysis that was previously unavailable. Indeed, adopting Smart Grid (SG) networks, which are freshly improved networks of linked devices, may considerably enhance the existing energy infrastructure's dependability, economy, and durability. The SG involves sharing a lot of data, including information on specific users' power use. And using this information, machine learning and deep learning algorithms may be able to detect power theft users reliably. This paper presented a Convolutional Neural Network (CNN)-based model for automated network-based vulnerability scanning that has excellent classification performance in many categories. Testing from iteration two to four iterations, this study examines research to discover the ideal configuration of the sequential model (SM) for categorization. The method is validated using a two-layer design, including an efficiency of 0.92, the whole first layer is composed of 128 nodes while the second level consists of 64 nodes. This allows for the development of a higher-level classifier for electrical signals, which may be employed in a number of applications. CNN was used to create electrical signal detectors, and SM was used to extract data from an electricity usage dataset. Furthermore, the number of features in the data set can be reduced using the Blue Monkey (BM) approach, and these results are then used to develop high-performance models. In this regard, the focus of this study has been on lowering the amount of needed features in the dataset in order to establish a rising classification algorithm for electrical signals. Experiments have applied the proposed systems' fantastic performance, with just 666 characteristics required to combine the CNN and BM methods. Comparative to 1035 traits when CNN was used alone. This shows that the CNN and BM models are better than the CNN model in terms of lowering sufficient know while maintaining the same reliability.
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    Öğe
    Using a novel method to improve various stages of machines in the power system
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ali, Mohammed Abdulkareem; Al-Jumaili, Saif; Noori, Harith Muthanna; Duru, Adil Deniz; Uçan, Osman Nuri
    The aim of this research is to utilize the particle swarm approach to examine the coordinated design of the unified power flow controller (UPFC) and two power system stabilizers (PSS) in a multi-machine power system. The synchronized proposed control challenge has been presented as an optimal solution with an objective function and constraint equations to complete this goal. Period models in a multi-machine power system were used to assess the effectiveness of the proposed design under distinct operating situations. It has been shown that, even if both PSSs and UPFCs perform well in their own right, power networks stability is adversely affected by an unfavorable impact or negative interaction between controllers if they are not coordinated. Inter-Area oscillations have been efficiently dampened thanks to integrated layout.