Yazar "Mohammadpourfard, Mostafa" seçeneğine göre listele

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    Deep learning-based cyber resilient dynamic line rating forecasting
    (Elsevier, 2022) Moradzadeh, Arash; Mohammadpourfard, Mostafa; Genc, Istemihan; Şeker, Şahin Serhat; Mohammadi-Ivatloo, Behnam
    Increased integration of renewable energy resources into the grid may create new difficulties for ensuring a sustainable power grid which drives electric utilities to use a number of cost-effective techniques such as Dynamic line rating (DLR) that enable them to run power networks more efficiently and reliably. DLR forecasting is a technique devised to accurately forecast the maximum current carrying capacity of overhead transmission lines. DLR offers many advantages, including increased renewable energy penetration without system reinforcement, improved grid dependability, and lower congestion costs. So far, many solutions have been proposed for DLR forecasting, which, despite estimating the exact capacity of the DLR, have some problems, such as installing multiple sensors and measurement devices and communication networks with precise calibration, and also neglect cyberattacks which may lead to operators making inappropriate operational choices. To address these issues, in this paper, a novel hybrid deep learning-based DLR forecasting approach called the autoencoder bidirectional long short-term memory (AE-BiLSTM) is efficiently and precisely developed. Several scenarios were developed to test the robustness and accuracy of the proposed methodology using real-world data with and without cyber-attacks. Detection of cyber-attack is done based on the increase in the least square errors of forecasting models. Then, the carefully designed hybrid AE-BiLSTM method reconstructs the falsified measurement data and provides reliable DLR forecasting. Also, a comparative study is carried out. The numerical results demonstrated that the proposed hybrid approach can significantly provide acceptable performance even under cyber-attacks and forecast DLR values with the least possible error, outperforming the existing conventional and deep learning-based techniques.
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    Electric load forecasting under False Data Injection Attacks using deep learning
    (Elsevier Ltd, 2022) Moradzadeh, Arash; Mohammadpourfard, Mostafa; Konstantinou, Charalambos; Genc, Istemihan; Kim, Taesic; Mohammadi-Ivatloo, Behnam
    Precise electric load forecasting at different time horizons is an essential aspect for electricity producers and consumers who participate in energy markets in order to maximize their economic efficiency. Moreover, accurate prediction of the electric load contributes toward robust and resilient power grids due to the error minimization of generators scheduling schemes. The accuracy of the existing electric load forecasting methods relies on data quality due to noisy real-world environments, and data integrity due to malicious cyber-attacks. This paper proposes a cyber-secure deep learning framework that accurately predicts electric load in power grids for a time horizon spanning from an hour to a week. The proposed deep learning framework systematically integrates Autoencoder (AE), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models (AE-CLSTM). The feasibility of the proposed solution is validated by using realistic grid data acquired from the distribution network of Tabriz, Iran. Compared to other load forecasting methods, the proposed method shows the highest accuracy in both a normal case with real-world noise and a stealthy False Data Injection Attack (FDIA). The proposed load forecasting method is practical and suitable for mitigating noise in real-world data and integrity attacks.